Helmholtz Russian-German Workshop on Systems Biology Moscow ...

<strong>Helmholtz</strong> 

<strong>Russian</strong>-<strong>German</strong> <strong>Workshop</strong> on 

Systems Biology 

February, 27-29, 2008, Moscow

Contents: 

WELCOME ADDRESS ........................................................................................................................................ 1 

HELMHOLTZ OFFICE MOSCOW .................................................................................................................... 2 

HELMHOLTZRUSSIA JOINT RESEARCH GROUPS .................................................................................. 3 

THE HELMHOLTZ ALLIANCE ON SYSTEMS BIOLOGY ........................................................................... 4 

THE GERMAN CANCER RESEARCH CENTER HEIDELBERG ................................................................. 6 

PROGRAM ............................................................................................................................................................ 8 

COMMITTEES .................................................................................................................................................. 10 

PARTICIPANTS ............................................................................................................................................... 11 

PARTICIPANT’S CVS AND ABSTRACTS ................................................................................................... 13 

VENUE DETAILS ............................................................................................................................................. 77 

Funding: 

The <strong>Helmholtz</strong> <strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology is generously supported by:

Welcome Address 

Dear Colleagues, 

As an exciting new field in biomedical research, systems biology has recently gained a lot of attention 

in <strong>German</strong>y. In 2007 the <strong>Helmholtz</strong> Association of <strong>German</strong> Research Centres has launched the 

<strong>Helmholtz</strong> Alliance on Systems Biology, a large network comprising <strong>Helmholtz</strong> Centres, universities 

and other external partners. The long‐term goal of the initiative is to shed light on the causes of 

complex disorders and diseases and develop new approaches for treating them. 

Due to its enormous potential in the field of mathematics and informatics, Russia has been chosen as 

one of the key partners for the development of international relations with the <strong>Helmholtz</strong> Alliance on 

Systems Biology. 

We are happy to welcome you to the workshop on Systems Biology, to be held in Moscow on 

February 27‐29 2008 in Moscow and are looking forward to an exciting meeting. It should help to 

open the field for scientific interactions in the field of systems biology between scientists from Russia 

and the <strong>Helmholtz</strong> Association as well as other institutions in <strong>German</strong>y. 

Prof. Otmar D. Wiestler 

Research Field Coordinator Health, <strong>Helmholtz</strong> 

Association 

Chairman and Scientific Director, <strong>German</strong> Cancer 

Research Center (DKFZ) Heidelberg 

Dear Friends and Colleagues, 

Prof. Roland Eils 

Coordinator <strong>Helmholtz</strong> Alliance 

on Systems Biology 

Department Head, <strong>German</strong> Cancer Research 

Center (DKFZ) Heidelberg 

In the name of the president of the <strong>Helmholtz</strong> Association the Moscow Office welcomes you as 

participants of the <strong>Helmholtz</strong> <strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology. I know, some of you 

have travelled far in order to join this workshop and others may even visit Russia the first time in 

their life. May your efforts not be in vain, but rewarded by meeting new partners for cooperation in 

this quickly emerging research field. We are certain that especially in the field of Systems Biology 

mutual interest and complementing scientific ideas lie immediately ahead of us. 

In order to pick up on these opportunities the <strong>Helmholtz</strong> Office Moscow supports the workshop with 

means from the Initiative and Networking Fund of the president of the <strong>Helmholtz</strong> Association and 

offers those of you who develop a mutual interest for co‐operation our full support for the 

completion of joint proposals within the <strong>Helmholtz</strong>‐Russia Joint Research Group program. With the 

formation of the Systems Biology Network among six <strong>Helmholtz</strong> Centres and numerous universities 

in <strong>German</strong>y the <strong>Helmholtz</strong> Association provides a unique spectrum of research and infrastructure in 

this field. We wish our colleagues and partners from Russia and the <strong>Helmholtz</strong> Centres a successful 

conference that will result into new exciting co‐operations in Systems Biology. 

Dr. Bertram Heinze 

Head <strong>Helmholtz</strong> Office Moscow 

<strong>Helmholtz</strong> <strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology 1

<strong>Helmholtz</strong> Office Moscow 

To help initiate and establish new strategic networks of scientific 

excellence between Russia and <strong>German</strong>y, the <strong>Helmholtz</strong> Association runs 

an office in Moscow. The <strong>Helmholtz</strong> Association chose Russia to be one of 

its key strategic partners to jointly face the challenges of the future 

through scientific cooperation. Partners in <strong>German</strong>y looking for specific 

information about Russia and <strong>Russian</strong>s seeking contacts in <strong>German</strong>y have 

an excellent starting point to find the right people for their special 

interests. 

Russia is going through significant socioeconomic changes with 

tremendous potential for creativity and technological development. The 

research area Russia with its well‐trained scientists and its comprehensive 

infrastructure offers numerous opportunities for a successful cooperation 

and to achieve scientific goals together. 

The transfer of new technologies and the exchange of promising young 

research talent hold great potential for the future development of both 

<strong>German</strong>y and Russia. Identifying the people and programs that will carry 

this project forward is the goal of the <strong>Helmholtz</strong> Office Moscow. 

Service provided by the <strong>Helmholtz</strong> Office Moscow (HOM): 

The Moscow Office represents the interests of <strong>Helmholtz</strong> Association as a whole in Russia. It serves 

both <strong>Helmholtz</strong> scientists and <strong>Russian</strong> researchers interested in mutual cooperation. Its main tasks 

are to provide help for scientific partners to establish contacts, to promote joint projects and to 

foster the exchange of scientists. 

For further information please visit: 

http://www.helmholtz.de/en/about_us/organisation/international_offices/moscow_office/ 

and http://www.helmholtz.ru/ . 

Contact: 

Dr. Bertram Heinze 

Head Moscow Office 

<strong>Helmholtz</strong>‐Association 

<strong>German</strong>‐<strong>Russian</strong> House Moscow 

Malaya Pirogovskaya 5 

119435 Moscow 

<strong>Russian</strong> Federation 

Tel.: +7 495 981 17 63 

Fax: +7 495 981 17 65 

bertram.heinze@helmholtz.de 

Postal Address from/via <strong>German</strong>y: 

<strong>Helmholtz</strong>‐Office Moscow | c/o Spring 

MOW/MOW/15130 

Postfach 920109 | D‐51151 Köln 

2 <strong>Helmholtz</strong> <strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology

<strong>Helmholtz</strong>Russia Joint Research Groups 

In September 2006 the <strong>Russian</strong> Foundation for Basic Research (RFBR) and the <strong>Helmholtz</strong> Association 

of <strong>German</strong> Research Centres signed an agreement over the joint funding of <strong>Helmholtz</strong>‐Russia Joint 

Research Groups. In spring 2007, the first call was launched and proved to raise strong interest from 

all <strong>Helmholtz</strong> Research Centres and <strong>Russian</strong> partners. In September 2007, eight groups were selected 

for funding from among 25 applications which the <strong>Helmholtz</strong> Association and the <strong>Russian</strong> Foundation 

for Basic Research submitted to an international review panel. Based on the conclusions of the first 

call the <strong>Helmholtz</strong> Association and the <strong>Russian</strong> Foundation for Basic Research will invest over 3.5 Mio 

EUR into the <strong>German</strong>‐<strong>Russian</strong> co‐operation during the comimg 3 years. 

Based on the successful launch of this funding instrument the presidents of the <strong>Helmholtz</strong> 

Association and the <strong>Russian</strong> Foundation for Basic Research decided to continue this action with a 

second call this year. From February, 15 to May, 15, 2008 the second call for the <strong>Helmholtz</strong>‐Russia 

Joint Research Groups will be open. 

The <strong>Helmholtz</strong>‐Russia Joint Research Groups are designed to intensify scientific cooperation between 

the <strong>Helmholtz</strong> Research Centres and <strong>Russian</strong> scientific institutions and universities in order to set 

new impulses in existing and upcoming research programs of the <strong>Helmholtz</strong> Association. A special 

focus lies in the promotion of excellent young <strong>Russian</strong> scientists, post‐docs and PhD students, and 

their involvement in the multinational research projects and infrastructure directed by the research 

centers of the <strong>Helmholtz</strong> Association. 

The <strong>Helmholtz</strong>‐Russia Joint Research Groups are funded by the <strong>Helmholtz</strong> Association for a duration 

of three years with 130,000 Euros per year. The RFBR co‐funds the <strong>Russian</strong> partner institute and/or 

university with 1,000,000 RUB (approx. 28,000 Euros) per year. 

Deadline: May, 15, 2008 

For more information visit: 

http://www.helmholtz.de/en/research/promoting_research/helmholtz_calls_for_applications/artikel 

/detail/helmholtz_russia_joint_research_groups‐1/ 

<strong>Helmholtz</strong>‐<strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology 3

The <strong>Helmholtz</strong> Alliance on 

Systems Biology 

Introduction 

The <strong>Helmholtz</strong> Alliance on Systems Biology is a centrally funded, joint initiative of several <strong>Helmholtz</strong> 

centers and external partners. The aim of the alliance is to exploit the outstanding expertise of the 

<strong>Helmholtz</strong> Association in basic, high‐throughput and bioinformatics research and to transfer it to 

innovative “Systems Biology” type of approaches. Scientific focuses of the Alliance are various 

complex diseases with the overall goal to widen the understanding of the causes of these diseases 

and the development of new strategies for treating them. 

The Alliance consists of six interconnected networks, each led by a specific <strong>Helmholtz</strong> centre. 

In order to promote the emerging discipline of Systems Biology, the <strong>Helmholtz</strong> Alliance will establish 

an educational program (workshops, Summer Schools, PhD programs) to transfer its knowledge and 

experience. 

Participating Networks and Centers 

Systems Biology of Signaling in Cancer (SBCancer) 

<strong>German</strong> Cancer Research Institute (DKFZ), Heidelberg 

The network on Systems Biology of Signalling in Cancer (SBCancer) concentrates on signalling 

pathways that play a pivotal role in the cellular decisions between proliferation, differentiation and 

death. Alterations in these signalling pathways and connected gene regulatory networks can change 

cellular decisions and thereby trigger the onset of tumour formation. 

The MDC Systems Biology Network – MSBN 

Max‐Delbrück Centre for Molecular Medicine (MDC), Berlin‐Buch 

MSBN focuses on the regulation of disease phenotypes of progressive cardiovascular and 

neurodegenerative disorders. Both are characterized by long asymptomatic phases before they 

become manifest. The regulatory mechanisms that effect first compensation and later pathology are 

being investigated and compared, employing a well designed, comprehensive systems biology 

strategy. 

From contaminant molecules to cellular response: system quantification 

and predictive model development 

<strong>Helmholtz</strong> Centre for Environmental Research (UFZ), Leipzig 

The systems biology network of the Centre for Environmental Research aims at understanding 

cellular responses to chemical stressors with a systems perspective. This involves analysis of intra‐ 

cellular transport of the chemicals, reaction with sub‐cellular target sites and the response of the cell 

at the transcriptional and post‐transcriptional level. Research is initiated focussing on the cellular 

response to polycyclic aromatic hydrocarbons, which are known to exert a wide range of toxic 

effects. 

CoReNe: Control of Regulatory Networks with focus on noncoding RNA 

<strong>Helmholtz</strong> Zentrum München ‐ <strong>German</strong> Research Center for Environmental Health 

The interdisciplinary network entitled CoReNe focuses on the integrative interpretation of regulatory 

networks involved in cell differentiation with focus on the interaction of non‐coding RNAs (ncRNAs). 

Focus is put on the influence of ncRNAs on gene expression and thereby on the regulatory networks 


of the cell. The aim is to extend existing models of regulatory networks by integrating the influence 

of ncRNAs. 

The Human Brain Model: Connecting neuronal structure and function 

across temporal and spatial scales 

Research Centre Jülich (FZJ) 

The network entitled "The Human Brain Model" investigates structure function relationships in the 

human brain as a multi‐scale complex system. In particular, the structural, spatial and temporal 

interactions of different units of the nervous system are analysed on different scales. 

Systems Biology at the HZI 

<strong>Helmholtz</strong> Centre for Infection Research (HZI), Braunschweig 

Various projects at the HZI work on scientific problems using methodologies from systems biology. 

These projects include medical as well as biotechnological topics. 

Protein Networks in Microorganisms 

Forschungszentrum Karlsruhe (FZK) 

Systems biology activities at the FZK focus on protein interaction networks in various organisms in 

order to improve the overall understanding of the biological systems. Both the networks within cells 

as well as the host‐pathogen interactions, e.g. the interaction between herpes viruses and humans or 

bacteria and their phages, are being analysed. 

Next scheduled events: 

• First <strong>Helmholtz</strong> Alliance on Systems Biology Spring School 

April 23‐26, 2008 ‐ Kloster Seeon, Bavaria 

• Training <strong>Workshop</strong> on "Mathematical modeling and high‐throughput screening“ 

May 13‐15, 2008 ‐ Heidelberg 

• First Status Seminar of the <strong>Helmholtz</strong> Alliance on Systems Biology 

June 22‐24, 2008 ‐ Potsdam 

Overview of partners from the <strong>Helmholtz</strong> 

Associa�on and from other ins�tu�ons 

Universität 

Düsseldorf 

Universität 

Aachen 

Forschungszentrum Jülich 

Universität 

Köln 

Deutsches 

Krebsforschungszentrum 

Universität 

Heidelberg, 

EMBL 

Forschungszentrum Karlsruhe 

Universität 

Freiburg 

Bonn 

Berlin 

Universität 

Rostock 

<strong>Helmholtz</strong>-Zentrum für 

Infektionsforschung 

Max-Delbrück-Centrum für 

Molekulare Medizin Berlin-Buch 

Humboldt 

Universität 

<strong>Helmholtz</strong>-Zentrum für 

Umweltforschung - UFZ 

TU + LMU 

München 

TU Dresden 

GSF-Forschungszentrum für 

Umwelt und Gesundheit 


The <strong>German</strong> Cancer Research Center 

Heidelberg 

Our Goals Our Tasks 

In order to develop new strategies in the battle against cancer, we 

first need to understand the complex biological mechanisms 

underlying this disease. What induces cells to grow in an 

uncontrolled manner? What biochemical processes are occurring 

when this happens? How can we influence these processes? 

At the <strong>German</strong> Cancer Research Center (Deutsches Krebs‐ 

forschungszentrum, DKFZ), a globally reknown research institution, 

our mission is to systematically investigate the mechanisms of cancer 

development and to identify cancer risk factors. Based on the results 

of this basic research new approaches are developed for prevention, 

diagnosis, and treatment of cancer. 

Brief History 

1964 The <strong>German</strong> Cancer Research Center was set up. The establishment of a national cancer 

research center was initiated by Heidelberg surgeon Professor Karl Heinrich Bauer. 

1977 The Center joined the Deutsche Forschungsgemeinschaft (DFG). 

2001 The Center joined the newly structured association "Hermann von <strong>Helmholtz</strong> Association of 

National Research Centers". With its 15 member research centers, an annual budget of 

approximately 2.2 billion Euros and 24,000 staff, the <strong>Helmholtz</strong> Association is <strong>German</strong>y's 

largest research organization. 

2004 The <strong>German</strong> Cancer Research Center, Heidelberg University Hospitals and the <strong>German</strong> 

Cancer Aid jointly set up the National Center for Tumor Diseases (NCT) Heidelberg. 

2007 Alliance with Center for Molecular Biology of the University of Heidelberg (ZMBH) 

Management Board 

Prof. Dr. Otmar D. Wiestler Chairman of the Management Board and Scientific Director 

Dr. Josef Puchta Administrative‐commercial Director 

Staff (as of Dec. 31, 2007) 

Total staff 2,128 

Staff scientists without doctoral students 619 Doctoral students 351 

Scientific infrastructure 660 Management support 161 

Technical and central services 104 Apprentices 116 

Diploma students 117 

In 2007, there were 154 visiting scientists from over 40 nations working at the DKFZ. 

Funding 2005 

Program‐based funding (90%/10% Federal vs. State Funding) 110.8 million EUR 

Project funding (External funds: DFG, DKH, EU, NIH, etc) 29 million EUR 

Own revenues (License revenues, patient care, donations and bequests) 18.7 million EUR 

6 <strong>Helmholtz</strong> <strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology 

DKFZ Main Building

The Range of Our Research 

The 47 scientific divisions, 14 junior research groups as well as 9 Clinical Cooperation Units of the 

<strong>German</strong> Cancer Research Center are assigned to seven Research Programs, which are assessed by 

international experts every five years: 

• Cell Biology and Tumor Biology 

• Functional and Structural Genomics 

• Cancer Risk Factors and Prevention 

• Tumor Immunology 

• Innovative Cancer Diagnostics and Therapy 

• Infection and Cancer 

• Translational Cancer Research 

The National Center for Tumor Diseases Heidelberg 

The foundation of the National Center for Tumor Diseases (NCT) modeled after U.S. Comprehensive 

Cancer Centers marked the start of a cooperation between the <strong>German</strong> Cancer Research Center, 

Heidelberg University Hospitals, the Heidelberg Thorax Clinic and the Deutsche Krebshilfe (<strong>German</strong> 

Cancer Aid). This cooperation is unique in <strong>German</strong>y. Through multidisciplinary interconnection of 

research and patient care, the NCT is designed to contribute to more efficient and swifter transfer of 

innovative approaches from basic research to clinical application. 

In summer 2009 the inauguration of the new NCT building in Neuenheimer Feld is expected. 

Cooperations: Together We Are Stronger 

Top‐class research relies on communication and is unimaginable today without international 

exchange. DKFZ attaches great importance to this area. This is documented ‐ alongside a large 

number of foreign staff scientists ‐ by over 650 grant‐holders from over 50 countries who have spent 

research stays at the Center over the past few years. 

At the international level, the Center collaborates with numerous institutes and organizations. The 

collaborations with Israeli partners and with the French Institut National de la Santé et de la 

Recherche Médical (Inserm) deserve special mention. 

Support of Young Scientists 

The <strong>German</strong> Cancer Research Center attaches great importance to the support of young scientists. 

Young scientists' groups have proven as a particularly successful approach. By providing sufficient 

equipment and independence for young scientists, these groups offer an opportunity for young 

researchers to distinguish themselves for their scientific career. 

Technology Transfer A Bridge to Industry 

Since 1997, the Office of Technology Transfer of the DKFZ has been making sure that the Center's 

intellectual property is protected by the patenting of inventions. Thus, industrial applications become 

possible. 

The Cancer Information Service KID 

Readily comprehensible, scientifically well‐founded and up‐to‐date information is provided by the 

cancer information service KID ('Krebsinformationsdienst') free of charge, by telephone and email, to 

cancer sufferers, their families, and interested citizens nationwide. 

KID additionally offers these services within the framework of consulting hours for patients of the 

NCT Heidelberg. 

How to reach KID and its modules: 

Mon through Fri, 8 am to 8 pm, Phone ++49‐(0)6221‐410121 

Email‐Service: krebsinformation@dkfz.de Internet: www.krebsinformation.de 


Program 

Wednesday, Feb 27, 2008 

17.00‐18.00 Registration 

18.00‐18.45 Welcome by Betram Heinze, <strong>Helmholtz</strong> Office Moscow 

Speakers: 

Sebastian Schmidt, <strong>Helmholtz</strong> Association 

Valery Chereshnev, Institute of Immunology and Physiology / Member of <strong>Russian</strong> 

Parlament (Duma), Moscow/Ekaterinburg 

Aleksander Makarov, Engelhardt Institute of Molecular Biology, Moscow 

Sergei Nedospasov, Engelhardt Institute of Molecular Biology and Belozersky Institute of 

Physico‐Chemical Biology, Moscow 

18.45‐19.30 Roland Eils, Heidelberg. Keynote Lecture : From models to experiments and back: 

challenges and promises of systems biology 

19.45 Welcome Dinner 

Thursday, Feb 28, 2008 

08.30‐09.00 Registration 

09.00‐10.40 Session 1 ‐ Chairs: U. Klingmüller and V. G. Tumanyan 

10.40‐10.55 Coffee Break 

Mikhail Gelfand, Moscow. Regulatory systems in bacteria: from comparative genomics 

to reconstruction of evolutionary history 

Vitor dos Santos, Braunschweig. Navigating the microbial metabolic landscape through 

constraint‐based modeling 

Vadim Govorun, Moscow. Systems biology studies of prokaryotes 

Konstantin Severinov, Moscow. The use of kinetic modeling to study the process of 

bacteriophage infection 

10.55‐12.50 Thomas Höfer, Heidelberg. From molecular machines to gene‐regulatory networks in 

mammalian cells 

12.50‐13.50 Lunch 

Alexander Apt, Moscow. Immunity to and pathogenesis of M. tuberculosis and M. avium 

infections: mirror‐type genetics of similar diseases in mice 

Klaus Schughart, Braunschweig. Systems genetics for infection and immunity 

Gennady Bocharov, Moscow. Modeling and identification of distributed parameter 

systems in immunology 

Inna Lavrik, Heidelberg. Life and death decisions on CD95 by systems biology approaches 

13.50‐15.30 Session 2 ‐ Chairs: I. Lavrik and M. Gelfand 

Maria Samsonova, St.Petersburg. Variation and canalization of gene expression in the 

Drosophila blastoderm 

Ursula Klingmüller, Heidelberg. Linking the extent of signaling pathway activation with 

cellular decisions 

Nikolay Kolchanov, Novosibirsk. Computer proteomics – functional sites analysis and 

recognition in 3‐D protein structure 

Jana Wolf, Berlin. Modeling of signaling networks for the analysis of oncogenic mutations 


15.30‐16.40 Poster Session and Coffee Break 

16.40‐18.20 Vsevolod Makeev, Moscow. Deciphering complex regulatory regions in eukaryotic 

genomes 

18.20 – 19.30 Poster Session 

19.45 Speakers Dinner 

Friday, Feb 29, 2008 

Nikolaus Rajewsky, Berlin. Small RNAs and deep sequencing 

Andrey Mironov, Moscow. Alternative splicing and secondary structure of RNA ‐ 

Systematic computational study and experimental verification of predictions 

Fabian Theis, Muenchen. Exploratory data analysis of biological systems 

8.45‐10.00 Session 3 ‐ Chairs: N.A. Kolchanov and R. Eils 

10.00‐10.15 Coffee Break 

Martin von Bergen, Leipzig. From contaminant molecules to cellular responses 

Dmitry Chudakov, Moscow. Fluorescent proteins and instruments on their basis 

Markus Diesmann, Wako City. Large‐scale simulations of plastic neural systems 

10.15‐11.30 Ralf Hofestaedt, Bielefeld. RAMEDIS : monitoring of inborn errors based on clinical 

and molecular data 

Oleg Demin, Moscow. Modeling in systems biology: applications to biomedicine and 

biotechnology 

Vladimir Poroikov, Moscow. Bio‐ and chemoinformatics of multitargeted anticancer 

drugs 

11.30‐12.00 Funding: IB‐BMBF, DAAD, DFG and HGF present their programs 

12.00‐13.45 Panel Discussion: Defining Options for <strong>Russian</strong>‐<strong>German</strong> Cooperations 

13.45‐14.00 Closing Remarks: Roland Eils 

15.00‐18.00 Visit to Lomonsov Moscow State University 

Saturday, March 1, 2008 

9.00‐14.00 Site‐visits and sightseeing in Moscow with <strong>Russian</strong> partners 


Committees 

Advisory Committee 

Valery Alexandrovich Chereshnev 

Institute of Immunology and Physiology, Ural Branch of the <strong>Russian</strong> Academy of Sciences, Member of 

<strong>Russian</strong> Parlament (Duma), Ekaterinburg/Moscow 

Nikolay Aleksandrovich Kolchanov 

Institute of Cytology and Genetics, 

Siberian Branch of the <strong>Russian</strong> Academy of Sciences, Novosibirsk 

Aleksander Aleksandrovich Makarov 

Engelhardt Institute of Molecular Biology 

<strong>Russian</strong> Academy of Sciences, Moscow 

Vladimir Petrovich Skulachev 

Belozersky Institute of Physico‐Chemical Biology, 

Lomonosov Moscow State University, Moscow 

Vladimir Guevich Tumanyan 

Engelhardt Institute of Molecular Biology 

<strong>Russian</strong> Academy of Sciences, Moscow 

Sebastian M. Schmidt 

Forschungszentrum Jülich, Jülich 

Otmar D. Wiestler (Chair) 

<strong>German</strong> Cancer Research Center (DKFZ), Heidelberg 

Scientific Committee 

Roland Eils (Chair) 


Mikhail Gelfand 

Institute for Information Transmission Problems, Moscow 

Ursula Klingmüller 


Inna Lavrik 


Organizational Committee 

Natalja Dobrowolskaja 


Jan Eufinger 


Bertram Heinze 


Ursula Schöttler 



Participants 

Name Institution and City 

CV on 

page 

Andrei Alexeevski Belozersky Institite, Moscow State University, Moscow 13 

Miguel Andrade Max‐Delbrück‐Centrum für Molekulare Medizin (MDC), Berlin 

Alexander Apt Central Research Institute of Tuberculosis, Moscow 

Irena Artamonova Vavilov Institute of General Genetics , Moscow 14 

Verena Becker Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 15 

Gennady Bocharov Institute of Numerical Mathematics, Moscow 16 

Sebastian Bohl Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 17 

Hauke Busch Deutsches Krebsforschungszentrum (DKFZ) , Heidelberg 18 

Valery Chereshnev Institute of Immunology and Physiology, Moscow/Ekaterinburg 

Dmitriy Chudakov Institite of Bioorganic Chemistry , Moscow 19 

Oleg Demin Moscow State University, Moscow 20 

Sofia Depner Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 21 

Markus Diesmann Riken Brain Science Institut (RIKEN BSI), Wako, Japan 22 

Natalja Dobrowolskaja <strong>Helmholtz</strong> Office Moscow, Moscow 

Vítor dos Santos <strong>Helmholtz</strong>‐Zentrum für Infektionsforschung (HZI), Braunschweig 23 

Roland Eils Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 24 

Jan Eufinger Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 25 

Mikhail Gelfand Institute for Information Transmission Problems, Moscow 26 

Nail Gizzatkulov (1) Institute for Systems Biology SPb (2) Moscow State University 27 

Evgeny Gladilin Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 28 

Giovani Gomez Estrada <strong>Helmholtz</strong> Zentrum München, München 29 

Ekaterina Goryacheva Institute for Systems Biology SPb, Moscow 30 

Vadim Govorun Research Institute for Physical–Chemical Medicine, Moscow 

Georgy Gulbekyan Moscow State University, Moscow 31 

Vitaly Gursky St.Petersburg Technical University ‐ Ioffe Physico‐Technical Institute 32 

Bertram Heinze <strong>Helmholtz</strong> Office Moscow, Moscow 

Thomas Höfer Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 33 

Ralf Hofestädt Universität Bielefeld ‐ Bioinformatik, Bielefeld 34 

Anna Ignatovich Institute of Numerical Mathematics, Moscow 35 

John Ionides St Petersburg State Polytech. Uni, St. Petersburg 36 

Pavel Ivanov Moscow State University, Moscow 37 

Lars Kaderali Universität Heidelberg ‐ BIOQUANT , Heidelberg 38 

Alexey Kazakov Institute for Information Transmission Problems , Moscow 39 

Ursula Klingmüller Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 40 

Olga Koborova Moscow State University, Moscow 41 

Nikolai Kolchanov Novosibirsk Institute of Cytology and Genetics, Novosibirsk 

Rainer König Deutsches Krebsforschungszentrum (DKFZ) / Universität Heidelberg, 

Heidelberg 

42 

Yuri Kosinsky (1) Institute for Systems Biology SPb (2) Moscow State University, 43 

Moscow 

Konstantin Kozlov St.Petersburg Technical University, St. Petersburg 44 

Ivan Kulakovsky Engelhardt Institute of Molecular Biology , Moscow 45 

Alexey Lagunin Institute of Biomedical Chemistry RAMS, Moscow 46 

Inna Lavrik Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 47 


Name Institution and City 

CV on 

page 

Aleksander Makarov Engelhardt Institute of Molecular Biology, Moscow 

Vsevolod Makeev GosNIIgenetika, Moscow 48 

Anna Matveeva St.Petersburg Technical University, St. Petersburg 49 

Julia Medvedeva GosNIIgenetika, Moscow 50 

Natalya Medvedeva Institute of Numerical Mathematics, Moscow 51 

Eugeniy Metelkin (1) Institute for Systems Biology SPb (2) Moscow State University, 52 


Andrey Mironov Moscow State University, Moscow 53 

Ekaterina Mogilevskaya (1) Institute for Systems Biology SPb (2) Moscow State University 54 

Mario Mommer Universität Heidelberg ‐ IWR, Heidelberg 55 

Margareta Müller Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 56 

Sergei Nedospasov Engelhardt Institute of Molecular Biology and Belozersky Institute of 

Physico‐Chemical Biology, Moscow 

Angela Oberthür Universität Heidelberg ‐ BIOQUANT, Heidelberg 57 

Kirill Peskov (1) Institute for Systems Biology SPb (2) Moscow State University, 58 


Vladimir Poroikov Institute of Biomedical Chemistry RAMS, Moscow 59 

Ksenia Pougach Institute of Molecular Genetics , Moscow 60 

Nikolaus Rajewsky Max‐Delbrück‐Centrum für Molekulare Medizin (MDC), Berlin 

S. Rakhmanov GosNIIgenetika, Moscow 

Vasily Ramensky Engelhardt Institute of Molecular Biology, Moscow 61 

Babette Regierer Universität Potsdam ‐ FORSYS, Potsdam 62 

Seong‐Hwan Rho Universität Freiburg ‐ Physics Institute, Freiburg 63 

Carlos Salazar Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 64 

Maria Samsonova St. Petersburg State Polytechnical University, St. Petersburg 65 

Johannes Schlöder Universität Heidelberg ‐ IWR, Heidelberg 66 

Sebastian M. Schmidt Forschungszentrum Jülich, Jülich 67 

Ursula Schöttler Deutsches Krebsforschungszentrum (DKFZ), Heidelberg 68 

Klaus Schughart <strong>Helmholtz</strong>‐Zentrum für Infektionsforschung (HZI), Braunschweig 69 

Konstantin Severinov Institute of Gene Biology and Institute of Molecular Genetics, Moscow 

Vladimir Skulachev Belozersky Institute, Moscow State University , Moscow 

Sergey Smirnov Institute for Systems Biology SPb, Moscow 70 

Sergej Spirin Belozersky Institute, Moscow State University , Moscow 71 

Fabian Theis <strong>Helmholtz</strong> Zentrum München, München 72 

Vladimir Tumanyan Engelhardt Institute of Molecular Biology, Moscow 

Alexey Vitreschak Institute for Information Transmission Problems, Moscow 73 

Martin von Bergen <strong>Helmholtz</strong>‐Zentrum für Umweltforschung (UFZ), Leipzig 74 

Jana Wolf Humboldt‐Universität Berlin, Berlin 75 

Alexey Zakharov Institute of Biomedical Chemistry RAMS, Moscow 76 


<strong>Helmholtz</strong>‐<strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology 13 

Dr. Andrei Alexeevski 

Institution Belozersky Inst. Moscow State University 

Contact Address 

Street Address Leninskij prospekt 

Zip /Postal Code 119313 

City Moscow 

Country Russia 

Phone +7 (495) 939 5414 

Fax +7 (495) 939 3181 

E‐Mail aba@belozersky.msu.ru 

Short CV 

1989 ‐ present Senior/Leading Scientist, A.N.Belozersky Institute, Moscow State University, Russia 

2002 ‐ present Co‐creator of the program in Bioinformatics, Lecturer at Faculty of Bioengineering and 

Bioinformatics, Moscow State University 

1981 ‐ 1989 Senior Engineer, Cardiological Scientific Center of USSR 

1983 Ph.D. in Mathematics from Moscow State University 

1973 ‐ 1981 Senior Researcher, INFORMELECTRO (research Institute in Electrical Engineering), 

Moscow, Russia 

1970‐1973 Graduate student ("aspirant"), Mechanico‐Mathematical Department, Moscow State 

University 

1970 M.S., Mathematics, Moscow State University, Moscow, Russia 

Research Interests 

Bioinformatics of 3D structures of biological macromolecules. 

Protein‐DNA interactions. 

Bacterial restriction‐modification systems. 

Theoretical mathematics. 

Five most important publications 

1. T.M.Dmitrieva, A.V.Alexeevski, G.S.Shatskaya, E.A.Tolskaya, A.P. Gmyl, E.V. Khitrina, V.I. Agol. 

Significance of the C‐terminal amino acid residue in mengovirus RNA‐dependent RNA polymerase. Virology 

365 (2007) 79‐91 

2. Sergei Spirin, Mikhail Titov, Anna Karyagina and Andrei Alexeevski. NPIDB, A Database of Nucleic Acids – 

Protein Interactions. Bioinformatics. 2007. 23(23):3247‐3248. 

3. Karyagina A., Ershova A., Titov M., Olovnikov I., Aksianov E., Ryazanova A., Kubareva E., Spirin S., 

Alexeevski A. Analysis of conserved hydrophobic cores in proteins and supramolecular complexes. J. of 

Bioinformatics and Computational Biology. 2006. Vol. 4, No 2, p. 357‐372. 

4. Ledneva RK, Alekseevskii AV, Vasil'ev SA, Spirin SA, Kariagina AS. (2001). Structural aspects of interaction 

of homeodomains with DNA. Molecular Biology (Moscow) 35, 647–659. 

5. Alexeevski, A. V., Natanzon, S. M., Noncommutative two‐dimensional topological field theories and 

Hurwitz numbers for real algebraic curves, Selecta Mathematica, New Series, 2006, 12 (3), 307‐377 

Contribution to <strong>Helmholtz</strong> <strong>Russian</strong><strong>German</strong> workshop on systems biology 

I will prepare 

(X ) a poster 

( ) a talk 

Poster 

number 

Title of poster / talk 

Occurence of recognition sites of restriction‐modification systems in bacteriophage genomes. 

Abstract 

Many bacteria hold restriction‐modification (RM) systems preventing host cells from invasions of 

foreign DNA by cleavage the latter at specific sites. Typically, type II RM‐system includes two proteins: 

the restriction endonuclease cleaves DNA at specific sites, the methyltransferase methylate the same 

sites preventing the cleavage of the host DNA. Bacteriophages posses several strategies to prevent 

DNA cleavage. One of them is the avoidance in genome sites with sequences that are recognized by 

the host RM‐systems. To reveal the spread of that strategy, we have computed the occurrences of 

259 sequences of known type II RM‐sites in 366 bacteriophage genomes. 

All 2545 pairs (phage genome, RM‐system recognition sequence), such that the number of sites with 

that sequence is highly underrepresented according to appropriate Markov model estimation, we 

divided in two groups. First group contains pairs with zero or very few sites per genome. Simple 

mathematical model and a number of published experimental data show that in this case phage 

infection has reasonable chances to survive against the RM‐system. Second group contains pairs with 

highly statistically underrepresented number of sites, but the number of sites is rather large, up to 

several hundred per genome. The mathematical model predicts exponential fall of chances to survive 

depending on the absolute number of sites in a genome. Thus, on one hand, due to 

underrepresentation of sites we believe that bacteriophages of the second group were under the 

pressure of RM‐system. On the other hand, these bacteriophages are not believed to survive against 

RM‐system due to exclusively site avoidance. 

First group allows us to predict tight interaction in nature of certain bacteriophages with certain 

bacterial RM‐systems. We hypothised that phages of the second group either lost the interactions 

with the corresponding RM‐system recently in the evolutionary scale, or should posses addition 

strategies against RM‐systems. 

The work was supported by grants RFBR 07‐04‐91560‐NNIO, 06‐04‐49558‐а, 06‐07‐89143‐а, INTAS 05‐ 

1000008‐8028 

Poster number: 1 

1 

Participant’s CVs and Abstracts

14 <strong>Helmholtz</strong>‐<strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology 

Dr. Irena I. Artamonova 

Institution Vavilov Institute of General Genetics RAS 

Contact Adress 

Street Address Gubkina 3 




Phone +7 916 9155809 

E‐Mail irenart@gmail.com 

Short CV 

1995 MSc, Moscow State University, pure mathematics, magna cum laude 

1998 MSc, Moscow Institute of Physics and Technology, physical and chemical biology and 

biotechnology, magna cum laude 

2002 PhD, molecular biology 

1999‐2001 Laboratory of Distant Education, Center of New Information Technologies, Moscow 

State University, research scientist 

2002‐2004 Laboratory of Structure and Function of Human Genes, Shemyakin‐Ovchinnikov 

Institute of Bioorganic Chemistry RAS, junior research scientist 

2004‐2007 Munich Unformation Center for Protein Science, Institute for Bioinformatics, GSF‐ 

National Research Center for Environment and Heath, Neuherberg, <strong>German</strong>y, postdoc 

fellow 

Since 2007 Group of Bioinformatics, Vavilov Institute of General Genetics RAS, group leader 

Awards 

1997, 1999 International Soros Science Foundation. Special Ph.D Student award 

1998 Annual competition of student projects of the Moscow Institute of Physics and 

Technology, main prize 

1997‐1998 Yu. Ovchinnikov student award 

2006 Best poster prize, Conference ECCB’06, Israel 


Computational biology: evolution of alternative splicing and gene/genome structure of higher eukaryotes; 

prokaryotic immunity; gene regulation; improvement of gene/protein annotation 


I.I. Artamonova, M.S. Gelfand (2007) Comparative genomics and evolution of alternative splicing: The 

pessimists’s science. Review. Chemical Reviews. V. 107, P. 3407‐3430 

Riley, L., Schmidt, T., Artamonova, I., Wagner, C., Volz, A., Heumann, K., Mewes, H.‐W., Frishman, D. 

(2007) PEDANT genome database: ten years online. Nucleic Acids Res. 

V. 35, D354‐7 

Artamonova II, Frishman G, Gelfand MS, Frishman D. (2005) Mining sequence annotation databanks 

for association patterns. Bioinformatics. V. 21 (Suppl_3), P. iii49‐iii57 

A.D. Neverov, I.I. Artamonova, R.N. Nurtdinov, D. Frishman, M. Gelfand, A. Mironov (2005) Alternative 

splicing and protein function. BMC Bioinformatics. V. 6, P. 266 

R.N. Nurtdinov, I.I. Artamonova, A.A. Mironov, M.S. Gelfand (2003) Low conservation of alternative 

splicing patterns in the human and mouse genomes. Human Molecular Genetics, v. 12, P. 1313‐1320. 



(x) a poster 

( ) a talk 


A recently discovered type of the prokaryotic immunity, the CRISPR system, in metagenomes 

Abstract 

We developed a methodology to search for CRISPR cassettes in metagenomes. It is based on a 

combination of three publicly available programs that, if applied separately, produce heavy false 

positive noise. 

The application of this schema to the Sargasso Sea metagenome data showed that the frequency of 

CRISPR cassettes in this metagenome is almost ten‐fold lower than in completely sequenced 

prokaryotic genomes on average. 

The identified CRISPR cassettes were collected in a special database. Families of related cassettes were 

formed by analysis of similarity between repeat units. Additional analysis of flanking regions allows 

one to distinguish between the lateral transfer and the parallel evolution of the cassettes in related 

strains. In many cases the similarity between cassettes is confined to single spacers, and some spacers 

are similar to known phage sequences. These observations support the hypothesis that this system 

defends organisms constituting the metagenome against invasion of foreign DNA. 

Poster number: 2


Verena Becker, PhD 

Institution <strong>German</strong> Cancer Research Center (DKFZ) 


Street Address Im Neuenheimer Feld 280, A150 


City Heidelberg 

Country <strong>German</strong>y 

Phone +49‐6221‐424482 

Fax +49‐6221‐424488 

E‐Mail v.becker@dkfz‐heidelberg.de 

Website http://www.dkfz.de/en/systembiologie/ 

Short CV 

1997 High school diploma 

1997‐2002 Studies in biology at the University of Freiburg 

2001 Diploma exminations in molecular immunology, developmental biology, 

biochemistry, and neuropathology 

2001‐2002 Diploma thesis in the laboratory of PD Dr. U. Klingmüller, Max‐Planck‐Institute of 

Immunobiology, Freiburg 

2003‐2007 Graduate studies at the University of Heidelberg 

Thesis in the laboratory of PD Dr. U. Klingmüller, <strong>German</strong> Cancer Research Center 

(DKFZ), Heidelberg 

since 2007 Postdoctoral research fellow in the laboratory of PD Dr. U. Klingmüller, <strong>German</strong> 

Cancer Research Center (DKFZ), Heidelberg 


Using the erythropoietin receptor (EpoR) as a model system, we are interested in the early control 

mechanisms of cytokine receptor activation. By combining signaling studies and biological assays with 

molecular modeling of receptor transmembrane domain dimers, we could show that modulating the 

EpoR dimer packing density allows for selective amplification of downstream signaling cascades and 

thus biological decisions. In a systems biology approach, we are analyzing the mechanisms by which 

ligand‐induced EpoR internalization controls short and long‐term signal activation. 


R. Ketteler, A.C. Heinrich, J.K. Offe, V. Becker, J. Cohen, D. Neumann, and U. Klingmüller. (2002) A 

functional green fluorescent protein‐tagged erythropoietin receptor despite physical separation of 

JAK2 binding site and tyrosine residues. J Biol Chem. 277:26547‐26552. 

W. Ruan, V. Becker, U. Klingmüller, and D. Langosch. (2004) The interface between self‐assembling 

erythropoietin receptor transmembrane segments corresponds to a membrane‐spanning leucine 

zipper. J Biol Chem. 279:3273‐3279. 

F. Grebien, M.A. Kerenyi, B. Kovacic, T. Kolbe, V. Becker, H. Dolznig, K. Pfeffer, U. Klingmüller, M. 

Müller, H. Beug, E.W. Müllner, and R. Moriggl. Stat5 activation enables erythropoiesis in the absence 

of EpoR and Jak2. Blood, in press. 

V. Becker, D. Sengupta, S. Heinzer, R. Ketteler, G.M. Ullmann, J.C. Smith, M. Weiss, and U. Klingmüller. 

Packing density of EpoR transmembrane domain correlates with amplification of biological responses. 

Submitted. 



(x) a poster 

( ) a talk 


Erythropoietin receptor internalization in control of short and long‐term signaling 

Abstract 

The fine‐tuned balance of self‐renewal and rapid adaptation in the hematopoietic system is regulated 

by cytokines. To ensure the integrity of blood cells within the body, cytokine receptors display several 

mechanisms to efficiently activate as well as terminate signal transduction. The contribution of 

receptor endocytosis and downregulation for signal attenuation of cytokine receptors is still unclear 

since in contrast to receptor tyrosine kinases only a minor fraction of receptor proteins is expressed at 

the plasma membrane whereas the majority is retained in the endoplasmic reticulum. Applying a 

systems biology approach, we investigated the dynamics of receptor turnover and internalization of 

the erythropoietin receptor (EpoR), the key factor for definitive erythropoiesis. Receptor endocytosis 

was measured with radiolabeled compounds in a time‐resolved manner. Data fitting of ordinary 

differential equation‐based mathematical models describing both constitutive and stimulated EpoR 

internalization revealed identifiable parameters. Thus, these models allow for predicting the 

mechanisms by which EpoR internalization determines the kinetics of short as well as long‐term 

receptor signaling. 

Poster number: 3


Prof. Dr. Gennady Bocharov 

Institution 


Institute of Numerical Mathematics 

Street Address Gubkina Street 8 



Country <strong>Russian</strong> Federation 


Fax +7‐495‐9381821 

E‐Mail 

Short CV 

bocharov@inm.ras.ru 

1974 ‐ 1980 Moscow Institute for Physics and Technology 

1980 ‐ present Institute of Numerical Mathematics, <strong>Russian</strong> Academy of Sciences, Leading 

Researcher 

1996 Institute of Experimental Immunology, University of Zurich 

May – Nov 2000 The Wellcome Trust Centre for the Epidemiology of Infectious Disease, Oxford 

University 

Nov 2000 – Aug 

2001 

Department of Infectious Disease Epidemiology, Imperial College London 

2003 – 2004 Leverhulme International Professor Chester University 

2006 ‐ present 

Awards 

Department of Computational Technologies and Modelling, Moscow State 

University 

1997‐1999 Fellow of Alexander von Humboldt Foundation, <strong>German</strong>y 

2007‐2009 Visiting Professor, Mathematics Department, Chester University 


General: 

Application of mathematics to immunology and virology; Mathematical systems theory; 

System identification; Computational modelling 

Specific: 

Quantitative modeling of virus infections and immune responses in humans (HBV, HCV, HIV, influenza) 

and experimental animals (LCMV, SIV, MHV); 

Genetic evolution of HIV within a single host under treatment; 

Dynamics of labeled (CFSE, BrdU, Ki‐67) T‐lymphocytes in vitro and in vivo 


1. Tatyana Luzyanina, Sonya Mrusek, John T. Edwards, Dirk Roose, Stephan Ehl and Gennady Bocharov 

(2007): Computational analysis of CFSE proliferation Journal of Mathematical Biology. 54: 57‐89. 

2. Burkhard Ludewig and Gennady Bocharov (2006): A systems biologist’s view on dendritic cell‐ 

cytotoxic T lymphocyte interaction. In: Handbook of Dendritic Cells. Biology, Diseases and Therapie, 

Eds. M. Lutz, N. Romani and A. Steinkasserer. Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim, Ch. 23: 

455‐479. 

3. Gennady Bocharov (2005): Understanding complex regulatory systems: Integrating molecular 

biology and systems analysis. Transfusion Medicine and Hemotherapy 32 (6): 304‐321 

4. Gennady Bocharov, Burkhard Ludewig, Antonio Bertoletti, Paul Klenerman, Tobias Junt, Philippe 

Krebs, Tatyana Luzyanina, Cristophe Fraser, and Roy M. Anderson (2004): Underwhelming the Immune 

Response: Effect of Slow Virus Growth on CD8 + ‐T‐Lymphocyte Responses J. Virology 78: 2247‐2254 

5. Stephan Ehl, Paul Klenerman, Rolf M. Zinkernagel, Gennadii Bocharov(1998): The impact of Variation 

in the Number of CD8 T‐Cell Precursors on the Outcome of Virus Infection. Cellular Immunology, 189, 

67‐73 



( ) a poster 

(+) a talk 


Modelling and identification of distributed parameter systems in immunology 

Abstract 

The problem of how to develop, in a systematic manner, consistent mathematical models that provide 

a basis for rigorous analysis and quantitative prediction in every day immunology research is explored. 

We present a quantitative approach towards an integrative modelling of distributed parameter 

systems in immunology. Two case studies are presented in detail: 

The type I interferon (IFN) response to coronavirus infection in mice The model of IFN response 

considers the population dynamics of plasmacytoid dendritic cells and macrophages. It is set up to 

quantify some fundamental parameters of IFN production and protection and predict the sensitivity of 

the response dynamics. 

Turnover of labeled (e.g. with fluorescent markers CFSE, BrdU) T lymphocytes in vitro and in vivo. We 

identified a set of mathematical models of differing complexity (formulated with ODEs or hyperbolic 

PDEs) to quantify the division‐, differentiation and death rate parameters of turning over T cells using 

flow cytometry data on the evolution of cell distribution with respect to the fluorescent markers.


Sebastian Bohl 



Street Address Im Neuenheimer Feld 280 





Fax +49‐6221‐424488 

E‐Mail S.Bohl@dkfz.de 

Website www.dkfz.de/de/systembiologie/ 

Short CV 

1997 High school diploma 

1998‐2003 Studies in biology at the university of Freiburg, <strong>German</strong>y 

2000‐2001 ERASMUS year at the university of Lund, Sweden 

2003 Diploma examination in molecular immunology, cell biology, biochemistry, and 

computer sciences 

2003‐2004 Diploma Thesis in the laboratory of PD Dr. U. Klingmüller, <strong>German</strong> Cancer 

Research Center (DKFZ), Heidelberg 

since 2004 Graduate studies at the University of Heidelberg 

Thesis in the laboratory of PD Dr. U. Klingmüller, <strong>German</strong> Cancer Research Center 

(DKFZ), Heidelberg 


Signal transduction in intracellular networks comprises the transmission of information from cell 

surface receptors to the nucleus where the activity of genes is regulated, alterations in these control 

mechanisms can cause cancer and other diseases. To elucidate key regulatory mechanisms it is not 

sufficient to identify the network components but rather essential to understand the dynamic 

behavior. In close collaboration with modeling partners we are following a systems biology approach 

and combine quantitative data with mathematical models to investigate alterations in signaling 

networks that promote the onset of cancer. The data‐based mathematical models enable rapid testing 

of hypotheses, the targeted design of experiments and the prediction of steps most suitable for 

intervention. 


Schilling M, Maiwald T, Bohl S, Kollmann M, Kreutz C, Timmer J, Klingmüller U. Computational 

processing and error reduction strategies for standardized quantitative data in biological networks. 

FEBS J. 2005 Dec;272(24):6400‐11 

Schilling M, Maiwald T, Bohl S, Kollmann M, Kreutz C, Timmer J, Klingmüller U. Quantitative data 

generation for systems biology: the impact of randomisation, calibrators and normalisers. Syst Biol 

(Stevenage). 2005 Dec;152(4):193‐200. 

Klingmüller U, Bauer A, Bohl S, Nickel PJ, Breitkopf K, Dooley S, Zellmer S, Kern C, Merfort I, Sparna T, 

Donauer J, Walz G, Geyer M, Kreutz C, Hermes M, Götschel F, Hecht A, Walter D, Egger L, Neubert K, 

Borner C, Brulport M, Schormann W, Sauer C, Baumann F, Preiss R, MacNelly S, Godoy P, Wiercinska E, 

Ciuclan L, Edelmann J, Zeilinger K, Heinrich M, Zanger UM, Gebhardt R, Maiwald T, Heinrich R, Timmer 

J, von Weizsäcker F, Hengstler JG. Primary mouse hepatocytes for systems biology approaches: a 

standardized in vitro system for modelling of signal transduction pathways. Syst Biol (Stevenage). 2006 

Nov;153(6):433‐47. 

Klingmüller U, Schilling M, Bohl S, Pfeifer AC The need for standardisation in Systems Biolog. ESF 

Forward Look (Systems Biology). 2007 Sep; 23‐27 

Maiwald T, Kreutz C, Pfeifer AC, Bohl S, Klingmüller U, Timmer J. Dynamic pathway modeling: 

feasibility analysis and optimal experimental design. Ann N Y Acad Sci. 2007 Dec;1115:212‐20. 



(x) a poster 

( ) a talk 


Dynamic Modeling of IL‐6 Induced Signaling in Primary Hepatocytes 

Abstract 

Hepatocyte regeneration is a tightly coordinated process, which is regulated by multiple signal 

transduction pathways. This process can be divided into three consecutive phases: priming, cell cycle 

progression and termination. During the priming phase, interleukin (IL)‐6 rapidly activates the gp130‐ 

JAK1‐STAT3 signaling pathway, enabling the previously quiescent hepatocytes to enter cell cycle 

progression. To examine the dynamic behavior of the JAK1‐STAT3 signaling cascade, we are employing 

a systems biology approach. We stimulated primary hepatocytes with different stimuli. The activation 

of the signaling components was quantified and the parameters of an ODE model of the pathway were 

fitted to the generated data. The model will be used to study the effects of the induced negative 

regulator SOCS3 on the dynamic behavior of the signaling cascade. Particular focus will be placed on 

the effect of the SOCS3 half‐life on signal duration and amplitude. 

Poster number: 4


Dr. Hauke Busch 

Institution Theoretical Bioinformatics, <strong>German</strong> Cancer Research 

Center (DKFZ) 


Street Address Bioquant, Im Neuenheimer Feld 267 




Phone +49‐6221‐54‐51309 

Fax +49‐6221‐54‐51488 

E‐Mail h.busch@dkfz.de 

Website http://www.dkfz.de/ibios/ 

Short CV 

1992‐1998 Study in Physics at the Darmstadt University of Technology 

1999‐2004 PhD at the Darmstadt University of Technology 

”Influence of Spatiotemporal Noise on Pattern Formation 

in Excitable Media“. 

1999‐2002 Member of the Graduate College 340 

„Comminication in Biological Systems“ 

2004‐now Postdoc at DKFZ, Group of Prof. R. Eils, 

Division of Theoretical Bioinformatics 

Head of Applied Systems Biology Modeling Group 

2004‐2007 Postdoc Fellow of the Systems Biology BioMS Initiative 


Main research interests: 

‐ the influence of noise in biological systems 

‐ development of methods for the efficient simulation of stochastic chemical systems in space and 

time. 

‐ reverse engineering of dynamic gene regulatory networks from high throughput data 

‐ optimal experiment design strategies for biologcial systems. 


H. Busch, D. Camacho‐Trullio, K. Breuhahn, P. Angel, R.Eils and A. Szabowski, Gene Network Dynamics 

controlling Keratinocyte Migration, submitted. 

H. Busch, W. Sandmann and V. Wolf, A numerical aggregation algorithm for the enzymecatalyzed 

substrate conversion, in Proceedings of the Int. Conference on Computational Methods in Systems 

Biology, Trento, 2006. 

H. Busch and R. Eils, Systems Biology, in Encyclopedia of Molecular Cell Biology and 

Molecular Medicine, 14, 123, (2005). 

H. Busch and M.‐Th. Hütt, Scale‐dependence of spatiotemporal filters inspired by cellular 

automata, Int. J. Bifurcation Chaos, 14, 1957, (2004). 

H. Busch, J. Garcia‐Ojalvo, and F. Kaiser, Influence of spatiotemporal 1/f_‐noise on structure formation 

in excitable media, Proc. SPIE, 5114, 468, (2003). 



( x) a poster 

( ) a talk 


Gene Network Dynamics Controlling Keratinocyte Migration 

Abstract 

So far it is not known how to translate large‐scale omic data into a coherent model of cellular 

regulation allowing to simulate, predict and control cellular behavior. However, assuming that 

information on cell wide behavior is reflected in the gene expression kinetics, we infer a dynamic gene 

regulatory network from time series measurements of DNA micro‐array data for well studied 

Hepatocyte Growth Factor‐induced migration of primary human keratinocytes. Transforming the 

obtained interactions to the level of signaling pathways, we predict in silico and verify in vitro the 

necessary and sufficient time‐ordered events that initiate, maintain and stop migration. We show that 

pulse‐like activation of the protooncogene receptor Met triggers a responsive state, while time 

sequential activation of EGF‐R is required to initiate and maintain migration. Context information for 

enhancing, delaying or stopping migration is provided via the activity of the PKA‐signaling pathway. 

Our results shed a new light on a continued kinetic interplay of multiple signaling pathways mediating 

the transition to a migratory system state over a time course of several hours. 

Poster number: 5


Dmitriy M. Chudakov, PhD 

Institution IBCH, Moscow, RAS 


Street Address Miklukho‐Maklaya, 16/10 




Phone 7‐495‐429‐80‐20 

Fax 7‐495‐330‐70‐56 

E‐Mail ChudakovDM@mail.ru 

Website http://www.ibch.ru/lgr/ 

Short CV 

Fluorescent proteins 

Photoactivatable fluorescent proteins 

Fluorescent sensors 

Awards 

2005 Young Scientists Award of <strong>Russian</strong> Academy of Sciences 

2006 European Academy Award 


Doctor Dmitriy Chudakov is a Research Scientist in the Institute of Bioorganic Chemistry of <strong>Russian</strong> 

Academy of Sciences, Moscow. He received his PhD degree in 2003 at the same institute, working on 

the development and studies of the photoactivatable fluorescent proteins with Doctor Konstantin 

Lukyanov. His current interests involve the studies and development of fluorescent proteins, 

photoactivatable proteins, genetically encoded sensors, genetically encoded photosensitizers. 


1 Chudakov DM, Belousov VV, Zaraisky AG, Novoselov VV, Staroverov DB, Zorov DB, Lukyanov S, 

Lukyanov KA. Kindling fluorescent proteins for precise in vivo photolabeling. Nat Biotechnol. 2003 

Feb;21(2):191‐4. 

2. Chudakov DM, Verkhusha VV, Staroverov DB, Souslova EA, Lukyanov S, Lukyanov KA. 

Photoswitchable cyan fluorescent protein for protein tracking. Nat Biotechnol. 2004 Nov;22(11):1435‐ 

9. 

3. Shcherbo D, Merzlyak EM, Chepurnykh TV, Fradkov AF, Ermakova GV, Solovieva EA, Lukyanov KA, 

Bogdanova EA, Zaraisky AG, Lukyanov S, Chudakov DM. Bright far‐red fluorescent protein for whole‐ 

body imaging. Nat Methods. 2007 Sep;4(9):741‐6. 

4. Merzlyak EM, Goedhart J, Shcherbo D, Bulina ME, Shcheglov AS, Fradkov AF, Gaintzeva A, Lukyanov 

KA, Lukyanov S, Gadella TW, Chudakov DM. Bright monomeric red fluorescent protein with an 

extended fluorescence lifetime. Nat Methods. 2007 Jul;4(7):555‐7. 

5. Lukyanov KA, Chudakov DM, Lukyanov S, Verkhusha VV. Innovation: Photoactivatable fluorescent 

proteins. Nat Rev Mol Cell Biol. 2005 Nov;6(11):885‐91. Review. 



( ) a poster 

(x) a talk 


Fluorescent proteins and instruments on their basis 

Abstract 

A number of wild type fluorescent proteins, homologues of Aeqourea victoria green fluorescent 

protein (GFP), and their innumerable mutant variants, form a whole palette of markers for in vivo 

labeling. Besides, a great number of various instruments were developed on their basis, including 

photoactivatable proteins, genetically encoded sensors, photosensitizer, etc. Here I’ll describe the 

most recent GFP‐based instruments generated in our laboratory.


Dr. Oleg Demin 

Institution (1) Institute for Systems Biology SPb (2) Moscow 

State University 


Street Address Leninskie Gory, 1/73, AN Belozerski IPCB Moscow 

State UniversityPCB 




Phone +7 495 783 8718 

Fax +7 495 783 87 18 

E‐Mail demin@genebee.msu.su 

Website www.insysbio.ru 

Short CV 

2004‐present CSO Institute for Systems Biology SPb 

2002–present Principal Research Scientist, Department of Bioenergetics of A.N.Belozersky 

Institute of Physico‐Chemical Biology, Moscow State University 

1995‐2002 Visiting Research Scientist, Department of Microbial Physiology, Free University 

of Amsterdam, Amsterdam, The Netherlands 

1995‐2002 Research Scientist, Department of Bioenergetics of A.N.Belozersky Institute of 

Physico‐Chemical Biology, Moscow State University 

1992‐1995 Post‐graduate of Biophysical Department, Moscow State University 

1995 Ph.D., Biophysical Department, Faculty of Biology, Moscow State University, 

Moscow. Title: Structural organization of multi‐enzyme metabolic systems and 

regulation of their fluxes and concentrations: theoretical approach 

1992 M.Sc., Faculty of Applied Mathematics and Cybernetics, Moscow State University, 

Moscow. Title: Optimal control and stability of steady states of metabolic 

pathways with different feedbacks 

1986‐1992 Biophysical Department, Faculty of Biology, Moscow State University 

1989‐1992 Faculty of Applied Mathematics and Cybernetics, Moscow State University 

Awards 

Participation in EU programmes: 

2006 ‐ 2008 Systems Biology of RNA metabolism in yeast; Acronym: RiboSys; FP6 Specific 

Targeted Project 

1999 ‐ 2001 European Union INTAS grant 

1998 ‐ 2000 European Union COPERNICUS grant 


Research Interests of Dr Demin are focused in areas of Systems Biology and Bioinformatics and 

scientific programming with especial focus on quantitative description of biological processes and their 

application to biotechnology and biomedicine. 

Areas of expertise: 

Modeling of cellular metabolism 

Modeling of cell signaling 

Modeling of gene regulatory networks 

Pathway reconstruction 

Methods and software for control of industrial biotechnology processes 

Methods and software for drug safety assessment 

Methods and software for kinetic modeling 


Metelkin E., Goryanin I., Demin O. Mathematical modeling of mitochondrial adenine nucleotide 

translocase. Biophys. J (2006) v.90 p.423‐432 

Goryanin II, Lebedeva GV, Mogilevskaya EA, Metelkin EA, Demin OV. Cellular kinetic modeling of the 

microbial metabolism. Methods Biochem Anal (2006) 49, 437‐488 

Demin O.V., Plyusnina T.Y., Lebedeva G.V., Zobova E.A., Metelkin E.A., Kolupaev A.G., Goryanin I.I., 

Tobin F. Kinetic modelling of the E. coli metabolism. IN: Topics in Current Genetics (2005) 31‐67, Eds. 

Alberghina L., Westerhoff H.V. , Springer 

Demin, O.V.,Goryanin, I.I., Kholodenko, B.N., Westerhoff, H.V. Kinetic modeling of energy metabolism 

and superoxide generation in hepatocyte mitochondria. Molecular Biology (Moscow) (2001) 35(6), 1‐ 

11 

Kholodenko, B.N., Demin, O.V., Moehren, G., Hoek, J.B. Quantification of short term signaling by the 

epidermal growth factor receptor. J Biol Chem (1999) 274, 30169‐30181 


I will prepare a talk 


Modeling in Systems Biology: Applications to Biomedicine and Biotechnology 

Abstract 

Two approaches of computational systems biology are presented: pathway reconstruction and kinetic 

modeling. Pathway reconstruction collects all information about players of interest, processes 

interconnecting them and their stoichiometry and can be considered as a powerful tool to search for 

drug targets, discover possible biomarkers and attribute them to particular cell state or phenomenon. 

In framework of kinetic modeling approach we mine, collect and integrate quantitative in vitro and in 

vivo experimental data produced by classical biochemistry, genomics, proteomics and metabolomics 

and use them to build and verify kinetic models [1,2]. These kinetic models when considered as a 

repository of all information about the system of interest can be applied to different problems of drug 

discovery and production such as screening optimization [3], investigation/prediction of drug safety [4] 

and optimization/maximization of yield of drug precursors. Several examples illustrating these 

approaches have been presented. 

1. E. Metelkin, I. Goryanin, O. Demin (2006) Biophys. J, 90: 423‐432; 2. I. Goryanin, G. Lebedeva, E. 

Mogilevskaya, E. Metelkin, O. Demin (2006) Methods Biochem Anal, 49: 437‐488 ; 3. M. Noble, Y. 

Sinha, A. Kolupaev, O. Demin, D. Earnshaw, F. Tobin, J. West, J. Martin, C. Qiu, W. Liu, W. DeWolf Jr., D. 

Tew, I.Goryanin (2006) Biotechnology and Bioengineering, 95: 560‐571. ;4. E. Mogilevskaya, O. Demin, 

I. Goryanin (2006) Journal of Biological Physics, 32: 245‐271.


Dr. Sofia Depner 







Phone 06221/424534 

Fax 06621/424551 

E‐Mail s.depner@dkfz.de 

Short CV 

born: 13.09.1968, Moskau 

1975‐1985 common secondary school Nr. 61, Moskau 

1985‐1990 Technical institute for forestry, Moskau. Degree: diplom engineer (FH) 

1994‐2002 Johannes Gutenberg University Mainz, <strong>German</strong>y. Degree: diplom biologist. 

2002‐2006 PhD Thesis at <strong>German</strong> cancer research center (DKFZ) 

2006‐dato Postdoc position at DKFZ, group Tumor and Microenvironment 


Regulation of signal transduction in tumor and stromal cells; The role of cytokines by the interactions 

between tumor and stromal cells. 



( + ) a poster 


Differential regulation of IL‐6 signaling pathway in HaCaT A5 benigne tumor keratinocytes and 

fibroblasts 

Abstract 

The activated progression promoting tumor microenvironment is initially induced by a network of 

tumor derived growth factors/cytokines that induce cellular responses in tumor and stromal cells. In a 

tumor transplantation model of HaCaT skin squamous cell carcinomas we could demonstrate the 

functional contribution of an IL‐6 regulated growth factor network to tumor progression. The network 

induces tumor cells proliferation and migration as well as persistent angiogenesis, recruitment and 

activation of stromal cells. In response to ligand binding the IL‐6R activates the JAK/STAT signalling 

pathway in stromal fibroblasts and tumor cells but pathway activation results in the induction of 

different target genes and triggers different cellular responses in both cell types. This differential 

target gene response is most likely mediated by a differential kinetics of expression, phosphorylation 

and nuclear localization of STAT proteins (STAT1 and 3) in both cell types. Additionally tumor 

keratinocytes and stromal fibroblasts respond with a different pattern of activation for MAP kinases 

such as Erk1/2. 

Poster number: 6


Dr. Markus Diesmann 

Institution RIKEN Brain Science Institute 


Street Address 2‐1 Hirosawa 

Zip /Postal Code 351‐0198 

City Wako‐shi, Saitama 

Country Japan 


Fax +81‐48‐467‐9644 

E‐Mail diesmann@brain.riken.jp 

Website http://www.cnpsn.brain.riken.jp 

Short CV 

2006 Unit Leader, RIKEN Brain Science Institute 

2004 Juniorprofessor, Freiburg 

2003 Assistant Professor (C1), Freiburg 

1999 Senior staff , MPI for Dynamics and SelfOrganization, Goettingen 

1997 PhD studies University of Freiburg 

1994 PhD studies Weizmann Institute of Science, Rehovot 

1993 Diploma in Physics (Bochum) 


The cortical neuronal network is among the most complex structures found in nature. The functional 

role of its dynamics exhibited on many spatio‐temporal scales is presently not understood. 

Furthermore, in contrast to other systems, the structure of the cortex is in fact not static but 

undergoes a continuous activity dependent reorganization. The Diesmann Research Unit studies the 

mechanisms and functional consequences of spike synchronization and plasticity in biologically 

realistic models of the cortical network. However, this bottom‐up approach alone may not lead to an 

understanding of brain function. For this reason we also incorporate top‐down approaches in our 

research. At the interface of top‐down and bottom up approaches, our strategy is to implement 

established formal theories of system function like temporal‐difference learning in biologically 

constrained network models. These investigations depend on large‐scale simulations requiring non‐ 

standard algorithms and high‐performance parallel computing. Therefore, the unit is also concerned 

with the creation of appropriate simulation technology. 


Morrison A, Aertsen A, Diesmann M. (2007) Spike‐time dependent plasticity in balanced recurrent 

networks. Neural Computation 19: 1437–1467. 

Morrison A, Straube S, Plesser H E, Diesmann M. (2007) Exact subthreshold integration with 

continuous spike times in discrete time neural network simulations. Neural Computation 19: 47—79. 

Morrison A, Mehring C, Geisel T, Aertsen A, Diesmann M . (2005) Advancing the boundaries of high 

connectivity network simulation with distributed computing. Neural Computation 17(8):1776—1801. 

Mehring C, Hehl U, Kubo M, Diesmann M , Aertsen A. (2003) Activity Dynamics and Propagation of 

Synchronous Spiking in Locally Connected Random Networks. Biological Cybernetics 88(5):395—408. 

Diesmann M , Gewaltig M‐O, Aertsen A. (1999) Conditions for Stable Propagation of Synchronous 

Spiking in Cortical Neural Networks. Nature 402:529—533. 

Contribution to <strong>Helmholtz</strong> <strong>Russian</strong>‐<strong>German</strong> workshop on systems biology 


( ) a poster 

(X) a talk 


Large‐scale simulations of plastic neural systems 

Abstract 

Higher brain functions emerge from large and complex cortical networks and their interactions. 

However, the large number of neurons combined with the high connectivity of the biological network 

and the heterogeneity in neuron and synapse types impose severe constraints on the explorable 

system size which have previously been hard to overcome. Furthermore, the continuous 

reorganization processes in the brain by different types of plasticity require simulations on the 

biological time‐scale of minutes but with a temporal resolution of fractions of a millisecond. In this 

contribution we review three recent advances that enable us to investigate large‐scale networks 

exploiting modern hybrid multi‐node/multi‐core computer architectures: (1) efficient communication 

in distributed simulation, (2) use of continuous spike times in a time‐driven simulation scheme, and (3) 

a multi‐threaded message‐passing simulation kernel. The technical advances are illustrated by a study 

of spike timing dependent plasticity (STDP) in a model of a cubic millimeter of cortical tissue containing 

some 1,000,000,000 synapses. www.nest‐initiative.org


Dr‐ Dipl‐Ing Martins dos Santos 



<strong>Helmholtz</strong> Centre for Infection Research 

Street Address Inhoffenstrasse 7 


City Braunschweig 


Phone +4953161814008 

Fax +4953161815002 

E‐Mail vds@helmholtz‐hzi.de 

Website 

Short CV 

www.helmholtz‐hzi.de/en/research_groups/molecular_biotechnology/synthetic_and_systems_biology/ 

1986‐1991 MEng., Food Engineering, College of Biotechnology, Porto Portugal 

1992‐1996 PhD Environmental Bioprocess Engineering. Wagenbing University, The 

Netherlands 

1997‐2001 Research Associate Dept. Molecular Biology, Council for Scientific Research, 

Granada Spain 

2000‐2005 Senior Scientist <strong>German</strong> Centre for Biotechnology Research, Braunschweig, 

<strong>German</strong>y 

2005‐ 

Awards 

Head Systems and Synthetic Biology Group, <strong>Helmholtz</strong> Centre for Infection 

Research, Braunschweig, <strong>German</strong>y 

1992‐1996 Portuguese Council for Scientific and Technological Research (JNICT), Portugal 

1997 European Environmental Research Organization (EERO) long‐term fellowship, The 

Netherlands 

1998‐199 Training and Mobility of Researchers, European Union, Brussels, Belgium 

2004 Runner‐up in “Biofuture”, the most important research prize in Biotechnology 

and Biological Sciences in <strong>German</strong>y 

2005 Runner‐up (finalist) Marie Curie Excellent Grants, EU 


The research goals of my group are to contribute to the elucidation of mechanisms underlying basic 

cellular processes, evolution and ecological interactions of microbes, and to translate this knowledge 

into applications of biomedical, biotechnological and environmental interest. The strategic concept is 

to develop and apply theoretical frameworks supporting (and relying on) experimental research 

towards the understanding, from a Systems Biology perspective of the various processes and 

hierarchies of cellular networks as well as interrelationships among prokaryotes and of prokaryotes 

with their environments. Ultimately, we aim at using these frameworks for directed cellular re‐ 

programming, by means of a forward‐design of experiments inspired in sound engineering concepts 

and the functional genomics of the organisms we sequence, combining mathematical modelling with 

experiments at all stages. Cellular re‐programming, under the broader umbrella of Synthetic Biology, 

plays an increasingly important role in my research. 

Five recent publications 

Oberhardt MA, Puchalka J., Fryer K, Martins dos Santos VAP * , Papin J. 2008vGenome‐scale Metabolic 

Reconstruction of the opportunistic pathogen Pseudomonas aeruginosa PA01. J. Bacteriol. Jan 11; 

[Epub ahead of print] 

Kachane AN, Timmis KN; Martins dos Santos VAP. 2006. Dynamics of reductive genome evolution in 

mitochondria and obligate intracellular microbe. Mol. Biol. Evolution. 24(2):449‐56 

Schneiker S * ., Martins dos Santos VA * Bartels D, Bekel T, Brecht M, Buhrmester J, Chernikova TN, 

Denaro R, Ferrer M, Gertler C, Goesmann A, Golyshina OV, Kaminski F, Khachane AN, Lang S, Linke B, 

McHardy AC, Meyer F, Nechitaylo T, Puhler A, Regenhardt D, Rupp O, Sabirova JS, Selbitschka W, 

Yakimov MM, Timmis KN, Vorholter FJ, Weidner S, Kaiser O, Golyshin PN. (2006) Genome sequence of 

the ubiquitous hydrocarbon‐degrading marine bacterium Alcanivorax borkumensis. Nature 

Biotechnol. 24(8):997‐1004. *joint first‐authorship, corresponding author 

Kachane AN, Timmis KN; Martins dos Santos VAP. 2005. Uracil content of 16S rRNA of thermophilic 

and psychrophilic prokaryotes correlates inversely with their optimal growth temperatures. Nucl. Acid 

Res. 33:4016‐4022 

Martins dos Santos, V.A.P., Heim S, Nelson KE, Timmis KN. 2004 Insights into the genomic basis of 

niche specificity of Pseudomonas putida strain KT2440. Environm. Microbiol. 6:1264‐1286 


I will prepare (x ) a talk 


Navigating the microbial metabolic landscape through constraint‐based modeling 

Abstract 

The relationship between the genotype and the phenotype is complex, highly non‐linear and cannot be predicted from simply 

cataloguing and assigning gene functions to genes found in a genome. Thus, if we are to understand how cellular functions 

operate, the function of every gene must be placed in the context of its role in attaining the set goals of a cellular function. 

This requires the integrated consideration of many interacting components. Clearly, however, the capabilities needed for a 

coherent and internally consistent analysis of the wealth of information available are far beyond the capacity of human mind. 

Mathematical modeling provides us a powerful way of handling such information and allows us to effectively develop 

appropriate frameworks that account for these complexities. We report here on the genome‐wide constraint‐based modelling 

of Pseudomonas putida (a versatile soil bacterium of biotechnological importance) and Pseudomonas aeruginosa (a 

threatening pathogen), an in silico representation that describes their metabolic capacities within the scope of their 

environmental constraints. Using annotated genome 

sequence data, biochemical information and strain‐specific knowledge, we analysed the cellular behaviour of these micro‐ 

organisms under a range of conditions relevant for both human health and environmental applications. This has been done 

using flux balance analysis for the entire metabolic networks of these bacteria. This has been done using flux 

balance analysis for the entire metabolic networks of these bacteria, as well as by determining the extreme pathways for 

relevant subsets of the networks. The networks comprise between 600 and 900 reactions representing between 650 and 750 

genes, which is about 10 to 12% of the genome of these bacteria. Preliminary results show that the number of extreme 

pathways that represent the metabolic potential of P. aeruginosa is 2 to 6 times higher than those for P. putida, although the 

former has only two more reactions than the latter. This may reflect a higher flexibility of the central metabolism of P. 

aeruginosa as compared to P. putida. This is clearly a emergent property of the system that could not be predicted solely on 

basis of the linear comparison of gene lists. The construction of comprehensive metabolic maps 

provides a framework to study the consequences of alterations in the genotype and to gain insight into the phenotype‐ 

genotype relation. Ultimately, this analysis defines the entire metabolic space of the possible flux distributions and metabolic 

interactions within the network. A direct comparison of this „phenotypic space" for both bacteria will possibly help in 

identifying „orphan genes", evolutionary features and genetic plasticity. The use of such models to choose the most 

informative knockouts and ultimately, to rationally design experiments relevant for the 

elucidation of the behavior of these bacteria in polluted environments or within the scope of their relationships with an 

infected host will be discussed.


Prof. Dr. Roland Eils 

Institution <strong>German</strong> Cancer Research Center (DKFZ) and 

University of Heidelberg (BIOQUANT) 


Street Address Im Neuenheimer Feld 580, B080 





Fax +49‐6221‐42‐3610 

E‐Mail r.eils@dkfz‐heidelberg.de 

Website http://www.dkfz.de/tbi/ 

Short CV 

1992 ‐ 1995 Ph.D. study at University of Heidelberg (Ruprecht‐Karl‐Universität Heidelberg); 

1995 ‐ 1996 Postdoctoral student at the IWR. 

1996 – 1996 Guest researcher at the Institut Albert Bonniot, Université Grenoble 

1996 ‐ 1999 Head of the biocomputing group „Structure and function in cellbiology“, IWR, 

University of Heidelberg, <strong>German</strong>y 

January 2000 head of the bioinformatics group „Intelligent bioinformatics systems“ at the 

<strong>German</strong> Cancer Research Center (dkfz) 

Since 2002 head of division „Theoretical Bioinformatics“ at the <strong>German</strong> Cancer Research 

Center (dkfz), Heidelberg, presently with 25 researchers 

November 2002 appointed director of department “Bioinformatics and Functional Genomics” and 

full professor (C4) at University of Heidelberg 

2006 Appointed founding director of BIOQUANT, Heidelberg Universities New Center 

for Systems Biology 

Awards 

June 1999 BioFuture prize from the <strong>German</strong> Ministery for Education and Research (approx. 

1.2 Millionen €) 

2002/2003 Nominated for full professor positions (C4) at University of Giessen, University of 

Bonn and University of Heidelberg 

2005 Microsoft Research award “Computational tools for advancing science” 

2005 Award for new innovative research by <strong>Helmholtz</strong> Society: “Systems Biology of 

Complex Diseases” 


Analysis and mathematical modelling of complex processes in molecular and cell biology 


Bacher CP, Guggiari M, Brors B, Augui S, Clerc P, Avner P, Eils R*, Heard E* (2006) Transient 

colocalization of X‐inactivation centres accompanies the initiation of X inactivation. Nat. Cell Biol. 2006 

Jan 24; [Epub ahead of print] *corresp. author and equal contribution. 

Bulashevska S, Eils R (2005) Inferring regulatory logic from gene expression data. Bioinformatics 21: 

2706‐2713. 

R. König and R. Eils (2004) Gene expression analysis on biochemical networks using the potts spin 

model. Bioinformatics 20: 1500‐1505. 

Bentele M, Lavrik I, Ulrich M, Stosser S, Heermann DW, Kalthoff H, Krammer PH, Eils R. (2004) 

Mathematical modeling reveals threshold mechanism in CD95‐induced apoptosis. J. Cell Biol. 166: 839‐ 

51. 

Gerlich, D., Kalbfuss, B., Beaudouin, J., Daigle, N., Eils, R.*, Ellenberg, J. (2003) Inheritance of 

chromosome topology throughout mitosis. Cell 112: 751‐764. *corresponding author 


I will prepare (x) a talk 


From models to experiments and back: challenges and promises of systems biology


Dr. Jan Eufinger 



<strong>German</strong> Cancer Research Center (DKFZ) 






Fax +49‐6221‐42‐3620 

E‐Mail j.eufinger@dkfz‐heidelberg.de 

Website 

Short CV 

www.dkfz.de/en/sbcancer 

www.helmholtz.de/systemsbiology 

1996 – 2001 Studies in Biology (Diploma), University of Heidelberg 

2001 4 months internship at BASF, Ludwigshafen 

2002‐2006 PhD Studies in Molecular Plant Sciences, 

Heidelberg Institute of Plant Sciences, University of Heidelberg 

2006/2007 Facility Management at BIOQUANT‐Institute, University of Heidelberg 

Since 

June 2007 

Scientific Project Management for the <strong>Helmholtz</strong> Alliance on Systems Biology and 

SBCancer, <strong>German</strong> Cancer Research Institute (DKFZ) Heidelberg 



(X) a poster 

( ) a talk 


The research networks of the <strong>Helmholtz</strong> Alliance on Systems Biology 

Abstract 

The <strong>Helmholtz</strong> Alliance on Systems Biology is a centrally funded, joint initiative of several <strong>Helmholtz</strong> 

centers and external partners. The aim of the Alliance is to exploit the outstanding expertise of the 

<strong>Helmholtz</strong> Association in basic, high‐through put and bioinformatics research and to transfer it to 

innovative "Systems Biology" type of approaches. Scientific focuses of the Alliance are various complex 

diseases with the overall goal to widen the understanding of the causes of these diseases and the 

development of new strategies for treating them. 

The Alliance consists of six interconnected networks, each led by a specific <strong>Helmholtz</strong> centre. The 

DKFZ's network "Systems Biology of Signalling in Cancer (SBCancer)", the largest network of the 

Alliance, concentrates on signalling pathways that play a pivotal role in the cellular decisions between 

proliferation, differentiation and death. 

For quantitative modeling approaches, the production and integration of standardized information is 

inevitable. Therefore the <strong>Helmholtz</strong> Alliance will concertedly expand existing experimental, IT and 

modeling techniques to provide an integrated efficient structure for all members of the Alliance. 

In order to promote the emerging discipline of Systems Biology, the <strong>Helmholtz</strong> Alliance will transfer its 

knowledge and experience into educational programs (workshops, Summer Schools, PhD programs). 

Poster number: 7


Prof. Mikhail Gelfand 

Institution Institute for Information Transmission Problems 


Street Address Bolshoi Karetny per. 19 





Fax +7‐495‐6500579 

E‐Mail gelfand@iitp.ru 

Website http://www.rtcb.iitp.ru/mg_e.htm 

Short CV 

comparative genomics 

metagenomics 

functional annotation of genes and proteins and metabolic reconstruction from 

genomic data 

evolution of metabolic pathways and regulatory systems 

alternative splicing 

statistics of DNA sequences 

Awards 

2007 A.A.Baev Prize in Genomics and Genoinformatics ‐ <strong>Russian</strong> Academy of Sciences 

2006 Best publication in a <strong>Russian</strong> scientific journal award ‐ Nauka Publishers 

2004 Best Scientist of the <strong>Russian</strong> Academy of Sciences (doctors of science, biology) ‐ 

Fund for Support fo the <strong>Russian</strong> Science 

2001‐2005 and Howard Hughes International Research Scholar 

2006‐2010 

2000 The President of <strong>Russian</strong> Federation's Award for Young Doctors of Science 


comparative genomics; 

metagenomics; 

functional annotation of genes and proteins and metabolic reconstruction from genomic data; 

evolution of metabolic pathways and regulatory systems; 

alternative splicing. 


Rodionov DA, Gelfand MS, Todd JD, Curson A.R.J, Johnston A.W.B. Computational reconstruction of 

iron‐ and manganese‐responsive transcriptional networks in alfa‐proteobacteria. PLoS Comput Biol. 

2006, 444: 240. 

Spirin V, Gelfand MS, Mironov AA, Mirny LA. A metabolic network in the evolutionary context: 

Multiscale structure and modularity. Proc Natl Acad Sci U S A., 2006, 103: 8774‐8779. 

Vitreschak AG, Rodionov DA, Mironov AA, Gelfand MS. Riboswitches: the oldest mechanism for the 

regulation of gene expression? Trends Genet. 2004, 20: 44‐50. 

Panina EM, Mironov AA, Gelfand MS. Comparative genomics of bacterial zinc regulons: enhanced ion 

transport, pathogenesis, and rearrangement of ribosomal proteins. Proc Natl Acad Sci U S A. 2003, 

100: 9912‐9917. 

Nurtdinov RN, Artamonova II, Mironov AA, Gelfand MS. Low conservation of alternative splicing 

patterns in the human and mouse genomes. Hum Mol Genet. 2003, 12:1313‐1320. 



( ) a poster 

(X) a talk 


Regulatory systems in bacteria: from comparative genomics to reconstruction of evolutionary history 

Abstract 

Comparative analysis of bacterial genomes allows not only for identification of new regulatory systems 

and functional annotation of hypothetical genes, but also for characterization of changes in regulatory 

patterns. Although it is premature to speak about a theory of regulatory evolution, some patterns start 

to emerge. I will present results of genomic analysis of several systems of varying complexity, 

providing examples of regulon expansion, contraction, changes in regulatory systems, co‐evolution of 

transcription factors and their DNA motifs, etc. In particular, I plan to describe the reconstruction of 

the evolution of iron homeostasis regulation in alpha‐proteobacteria.


Dr Nail Gizzatkulov 

Institution (1) Institute for Systems Biology SPb (2) Moscow State University 


Street Address Leninskie Gory, 1/73, AN Belozerski IPCB Moscow State UniversityPCB 





Fax +7 495 783 87 18 

E‐Mail gizzatkulov@gmail.com 


Short CV (keywords) 

2004‐present Institute for Systems Biology SPb 

2005‐2006 Research Scientist Keldysh Istitute of Apply Mathematics of <strong>Russian</strong> Academy of 

Science, Moscow. 

2005 Ph.D., Keldysh Istitute of Apply Mathematics of <strong>Russian</strong> Academy of Science, 

Moscow. 

2002‐2005 PhD Student of Keldysh Istitute of Apply Mathematics of <strong>Russian</strong> Academy of 

Science 

1996‐2002 Faculty of Applied Mathematics, Ufa Aircraft State Technical University 

Research Interests (3‐5 sentences) 

Research Interests of Dr Gizzatkulov are focused in areas of Systems Biology and Bioinformatics and 

scientific programming with especial focus on scientific programming of software modeling of 

biological processes and their application to biotechnology and biomedicine. Areas of expertise: 






(X) a poster 

(please indicate) 

() a talk 


Software for modeling complex biological systems 

Abstract 

Poster presents software developed for modeling complex biological systems: 

Model Creator – create kinetic model from scratch, annotate model, save model to various format 

such as SBML ( System Biology Markup Language ); 

DBSolve7 – software to create, solve, analyze and visualize kinetic model corresponding to complex 

biological systems; 

One more topic of my poster is software which could be created using engine of DBSolve7 and Model 

Creator. This software has been applied to resolve different problems arising in area of biomedicine 

and biotechnology. 

Poster number: 8


Dr. Evgeny Gladilin 








E‐Mail e.gladilin@dkfz‐heidelberg.de 

Short CV 

Postdoctoral Scientist, <strong>German</strong> Cancer Research Center, Theoretical 

Bioinformatics, Heidelberg 

PhD in Mathematics, Free University Berlin 

MS in Physics, University Hamburg 

BS in Biophysics, <strong>Russian</strong> State Medical University, Moscow 


cell mechanics, mechanotransduction, image analysis, numerical modeling 


1. E. Gladilin, A. Micoulet, B. Hosseini, J. Spatz, K. Rohr, R. Eils. "Finite element analysis of 

uniaxial cell stretching: from image to insight". IOP Phys. Biol., Volume 4, Issue 2, p. 104‐ 

113, 2007 

2. E. Gladilin, S. Goetze, J. Mateos‐Langerak, R. van Driel, K. Rohr, R. Eils. "Geometrical 

probability approach for analysis of 3D chromatin structure in interphase cell nuclei". 

Proc. of IEEE CIBCB Symposium, p. 124‐134, 2007 

3. E. Gladilin, S. Goetze, J. Mateos‐Langerak, R. van Driel, R. Eils, K. Rohr. 

"Topological analysis of 3D cell nuclei using finite element template‐based spherical 

mapping". Proc. of SPIE MI, vol. 6144, p. 1557‐1566, 2006 

4. E. Gladilin, V. Pekar, K. Rohr, H.S. Stiehl. "A comparison between BEM and FEM for elastic 

registration of medical images". Image and Vision Computing, 24(4):375‐379, 2006 

5. E. Gladilin, A. Ivanov, V. Roginsky. "Generic Approach for Biomechanical Simulation 

of Typical Boundary Value Problems in Cranio‐Maxillofacial Surgery Planning". Proc. of 

MICCAI, p. 380‐388, 2004 



(X) a poster 

( ) a talk 


Computational analysis of uniaxial cell stretching using time series of microscopic images and 

predictive numerical model of cell mechanics 

Abstract 

Mechanical forces play an important role in many microbiological phenomena such as embryogenesis, 

regeneration, cell proliferation and differentiation. Micromanipulation of cells in a controlled 

environment is a widely used approach for understanding cellular responses with respect to external 

mechanical forces. While modern micromanipulation and imaging techniques provide useful optical 

information about the change of overall cell contours under the impact of external loads, the intrinsic 

mechanisms of energy and signal propagation throughout the cell structure are usually not accessible 

by direct observation. This work deals 

with the computational modeling and simulation of intracellular strain state of uniaxially stretched 

cells captured in a series of images. A nonlinear elastic finite element method was applied for 

numerical analysis of inhomogeneous stretching of a rat embryonic fibroblast 52 (REF 52) using a 

simplified two‐component model of a eukaryotic cell consisting of a stiffer nucleus surrounded by a 

softer cytoplasm. The difference between simulated and experimentally observed cell contours is used 

as a feedback criterion for iterative estimation of canonical material parameters of the two‐ 

component model such as stiffness and compressibility. Analysis of comparative simulations with 

varying material parameters shows that (i) the ratio between the stiffness of cell nucleus and 

cytoplasm determines intracellular strain distribution and (ii) large deformations result in increased 

stiffness and decreased compressibility of the cell cytoplasm. The proposed model is able to reproduce 

the evolution of the cellular shape over a sequence of observed deformations and provides 

complementary information for a better understanding of mechanical cell response. 

Poster number: 9


Dr. rer. nat. Giovani Gomez Estrada 

Institution <strong>Helmholtz</strong> Zentrum Muenchen 


Street Address Ingolstaedter Landstrasse 1 


City Neuherberg 


Phone 089 3187‐3683 

Fax 089 3187‐3585 

E‐Mail giovani.estrada@helmholtz‐muenchen.de 

Short CV 

Bsc in Comp. Eng., 1996, Monterrey Tech, Mexico 

Msc in Comp. Sci., 1999, Monterrey Tech, Mexico 

Dr. rer. nat., 2007, University of Stuttgart, <strong>German</strong>y 

Research positions: Swiss Federal Institute of Technology (ETH, 2000‐2001), 

Monterrey Tech (2001‐2002), Max Planck Institute (MPI‐MF, 2002‐2006) and 

Royal College of Surgeons in Ireland (RCSI, 2006‐2007). 

Currently a post‐doc in the <strong>Helmholtz</strong> Zentrum Muenchen, Institute 

of Bioinformatics and Systems Biology 


Biomedical data analysis and biostatistics (e.g. nanotoxicity, prognosis of breast and gastric cancer), 

mathematical modelling of multi‐stable dynamic systems with biochemical reaction networks 

(apoptosis, stability) and discrete structures (cellular tensegrity). 


Quantitative analysis of cell adhesion on aligned micro‐ and nanofibers; Journal of Biomedical 

Materials Research Part A, 84A(2):291‐299, Feb. 2008 

Cytotoxicity of Single Wall Carbon Nanotubes on Human Fibroblasts; Toxicology in Vitro, 20(7):1202‐ 

1212, Oct. 2006 

Numerical form‐finding of tensegrity structures; Int. J. of Solids and Structures, 43(22‐23):6855‐6868, 

Nov. 2006 

A microarray‐based gastric carcinoma prewarning system; World Journal of Gastroenterology, 

11(9):1273‐1282, Mar. 2005 

Characterization of BRCAA1 and its novel antigen epitope identification; Cancer Epidemiology, 

Biomarker & Prevention, 13(7):1136‐1145, Jul. 2004


Dr Ekaterina Goryacheva 

Institution Institute for Systems Biology SPb 








Fax +7 495 783 87 18 

E‐Mail kate@biophys.msu.ru 


Short CV 

2004‐present CSO Institute for Systems Biology SPb 

2001–present Institute of Bioorganic Chemistry, <strong>Russian</strong> Academy of Sciences 


Moscow. Title: The influence of modification of Reaction Centres from purple 

bacterium Rhodobacter sphaeroides on the electron transfer in the picosecond 

time scale. 

1996‐1999 Post‐graduate of Biophysical Department, Faculty of Biology, Moscow State 

University 

1991‐1996 Biophysical Department, Faculty of Biology, Moscow State University 

May 1996: M.Sc., Biophysical Department, Faculty of Biology, Moscow State 

University, Moscow. Title: The influence of intramolecular dynamics on kinetics 

of picosecond stages in Reaction Centres from purple bacterium Rb. sphaeroides. 


Research Interests of Dr Goryacheva are focused in areas of Systems Biology and Bioinformatics, 

databases analysis, kinetic modeling and their application to biotechnology and biomedicine. Areas of 

expertise: 





Analysis of proteins and peptides structure 

Analysis of enzyme‐substrate interactions, search of inhibitors 


Demin O.V., Lebedeva G.V., Kolupaev A.G., Zobova E.A., Plyusnina T.Yu., Lavrova A.I., Dubinsky A., 

Goryacheva E.A., Tobin F., Goryanin I.I. Kinetic Modelling as a Modern Technology to Explore and 

Modify Living Cells. IN: G. Ciobanu, G. Rozenberg (Eds.): Modelling in Molecular Biology (2004), 

Natural Computing Series, Springer p.59‐103 

Pletnev VZ, Goryacheva EA, Tsygannik IN, Nesmeianov VA, Pletnev SV, Pangborn W, Daux W. A new 

crystal form of the Fab fragment of a monoclonal antibody to human interleukin‐2: the three‐ 

dimensional structure at 2.7 A resolution Bioorg Khim, <strong>Russian</strong> (2004) v30(5) p.466‐469. 

Krasilnikov PM., Gorokhov VV., Goryacheva EA., Knox PP., Pashchenko VZ., Rubin AB. Investigation of 

the electron transfer reactions and redox characteristics of photoactive bacteriochlorophyll in 

Rhodobacter sphaeroides reaction centers modified by D2O and cryoprotectants. Membr Cell Biol. 

(2000) v.14(3), p.343‐356. 

Paschenko VZ., Gorokhov VV., Grishanova NP., Goryacheva EA, Korvatovsky BN., Knox PP., Zakharova 

NI., Rubin AB. The influence of structural‐dynamic organization of the RC from purple bacterium 

Rhodobacter sphaeroides on picosecond stages of photoinduced reactions. // Biochim. Biophys. Acta 

(1998), v.1364, p.361‐372. 

Paschenko VZ., Knox PP., Gorokhov VV., Korvatovsky BN., Zakharova NI., Goryacheva EA, Rubin AB. 

Temperature dependence of first stages of photosynthesis in native and modified RC from bacterium 

Rhodobacter sphaeroides. Doklady Akademii Nauk (1997) v.357, N6, p.835‐838. 



(X) a poster 

( ) a talk 


Pathway reconstruction and target identification in breast cancer 

Abstract 

Breast cancer is one of the most serious problems of oncology. In the past 20 years, there has been a 

tremendous increase in our knowledge of the molecular mechanisms and pathophysiology of human 

cancer. However insufficient clinical effectiveness and toxicity to the patient of used antitumor 

treatments stimulate further investigations of tumor cells features. Effective analysis and fusion of 

available experimental data is possible using system biology methods, one of them is pathway 

reconstruction. Pathway reconstruction of breast cancer cells accumulates all information about 

metabolic processes, signal transduction, gene regulation playing important role in tumor growth. This 

information is necessary for deeper understanding of molecular mechanisms of tumor cells 

metabolism regulation and searching for drug targets and drug combinations for breast cancer 

treatments. 

Poster number: 10


Georgy G. Gulbekyan 

Institution M.V.Lomonosov Moscow State University 


Street Address Vorobievy Gory 




Phone +7(495)1372384 

E‐Mail gulbekyan@gmail.com 

Short CV 

Education B.S., M.S., in Biophysics from School of Physics, M.V.Lomonosov Moscow State 

University 

Keywords Gene expression, microarrays, gene markers, evolutionary algorithms, data 

mining, acute lymphoblastic leukemia 

Research interests 

New approaches to construction of a compact set of tumor marker genes and its application in 

differential diagnosis of various subtypes of acute lymphoblastic leukemia. Tumor marker genes and 

underlying biology. Data mining. 



(X) a poster 

( ) a talk 


A combined algorithm of tumor marker genes determination and its application to microarray ALL data 

Gulbekyan, G.G., Valyaev, V.Yu, Ivanov P.S. 

Abstract 

We present a novel algorithm for detecting marker genes in multiclass microarray data that combines 

two well‐proved approaches, namely, supervised classification and evolution simulation. We selected 

Support Vector Machine (SVM) to classify microarray samples. First, we use a LKOCV scheme to 

partition initial data into training and test datasets and to fit the parameters of classification algorithm. 

Second, we estimate the classification error for a large number of training/test partitions by 

randomizing initial data. Third, we simulate mutations in a randomly chosen set of potential marker 

genes (predictor) and simultaneous evolution of several such predictors combined in a predictor pool. 

At each evolutionary epoch, we retain a predictor in or exclude it from the predictor pool based on its 

classification power (quality measure). Fourth, we stop the evolutionary process when changes in 

classification quality measure appear to be less than a chosen threshold or after a predefined number 

of iterations. Results of applying this algorithm to a model dataset as well as to pediatric acute 

lymphoblastic leukemia (ALL) microarray data (Ross M. et al. (2003) Blood 102: 2951‐2959) will be 

presented. 

Poster number: 11


Vitaly V. Gursky, PhD student 

Institution Ioffe Physico‐Technical Institute 


Street Address 26, Politekhnicheskaya Street 


City St. Petersburg 



Fax +7 812 2971017 

E‐Mail gursky@math.ioffe.ru 

Short CV 

1991‐1999 Magister in Physics, Physics Faculty of the St. Petersburg State University 

2002‐now Junior Research Fellow at the Ioffe Physico‐Technical Institute of the <strong>Russian</strong> 

Academy of Sciences 


Systems biology, mathematical modeling in biology, mathematical modeling of gene regulation, gene 

networks, mechanisms of development, mechanisms of robustness, development of multi‐cellular 

organisms, segmentation of Drosophila, dynamical system applications in modeling segmentation gene 

expression in Drosophila, reaction‐diffusion systems in nonlinear science 


V. V. Gursky, K. N. Kozlov, A. M. Samsonov, J. Reinitz, Cell divisions as a mechanism for selection in 

stable steady states of multi‐stationary gene circuits, Physica D 218(1): 70‐76, 2006 

M. G. Samsonova, A. M. Samsonov, V. V. Gursky, C. E. Vanario‐Alonso, A survey of gene circuit 

approach applied to modelling of segment determination in fruit fly. In: Multiple aspects of DNA and 

RNA: from Biophysics to Bioinformatics, Session LXXXII (Eds. D. Chatenay et al.), Elsevier, 2005 

V. V. Gursky, J. Jaeger, K. N. Kozlov, J. Reinitz, A. M. Samsonov, Pattern formation and nuclear divisions 

are uncoupled in Drosophila segmentation: Comparison of spatially discrete and continuous models, 

Physica D 197: 286‐302, 2004 

V. V. Gursky, J. Reinitz, A. M. Samsonov, How gap genes make their domains: An analytical study based 

on data driven approximations, Chaos 11(1): 132‐141, 2001 

A. M. Samsonov, V. V. Gursky, Exact solutions to a nonlinear reaction‐diffusion equation and 

hyperelliptic integrals inversion, J. Phys. A: Math. Gen. 32: 6573‐6588, 1999 



(X) a poster 

( ) a talk 


Stable hb expression under variable Bcd morphogen in Drosophila: Asymptotic approach in model of 

gap gene expression 

Abstract 

It is well known that the Bcd morphogen is one of the key regulators of gap gene hb in the early 

Drosophila embryo. In the classical model, the posterior border of the Hb anterior domain gets formed 

as a threshold dependent response to the Bcd gradient at that position in the A‐P axis of the embryo. A 

serious problem in this concept arises from the fact that Bcd concentration exhibits high embryo to 

embryo variability, which essentially exceeds that of the Hb domain border [1]. A candidate for 

mechanisms stabilizing the expression of gap genes under variable Bcd is the cross regulation between 

gap genes. We explore this hypothesis by studying a mathematical model of gap gene expression 

formulated in [2]. Under simplifying assumptions of stationarity and sharp sigmoid regulation function 

in model equations, the expression patterns in the model can be analytically derived as superpositions 

of local interfaces. We obtain analytical formulas for variations of these interfaces as functions of Bcd 

variation. By using this approach, we demonstrate that the mechanism of mutual regulation in the gap 

gene network is able to provide the experimentally observed stability rate of gap gene expression. 

[1] B. Houchmandzadeh, E. Wieschaus, S. Leibler (2002). Establishment of developmental precision and 

proportions in the early Drosophila embryo, Nature 415, 798–802. 

[2] J. Jaeger, S. Surkova, M. Blagov, H. Janssens, D. Kosman, K. N. Kozlov, Manu, E. Myasnikova, C. E. 

Vanario‐Alonso, M. Samsonova, D. H. Sharp, J. Reinitz (2004). Dynamic control of positional 

information in the early Drosophila embryo, Nature 430, 368–371. 

Poster number: 12


Prof. Dr. Thomas Höfer 







Phone +49 6221 54 51 380 

Fax +49 6221 54 51 487 

E‐Mail t.hoefer@dkfz‐heidelberg.de 

Website www.dkfz.de 

Short CV 

2006 – present Group leader, Modeling of Biological Systems, <strong>German</strong> Cancer 

Research Center, Heidelberg 

2002 – 2006 Junior professor in theoretical biophysics, Humboldt Univ. Berlin 

1997 – 2002 Lecturer in biophysics, Humboldt Univ. Berlin 

Extended research visits to Collège de France (2000/01) and New Jersey Medical 

School (2001/02) 

1996 ‐ 1997 Postdoc, Max‐Planck Institute for Physics of Complex Systems, Dresden 

1996 PhD, mathematical biology, University of Oxford 

1993 Diplom‐Biophysiker, Humboldt University Berlin 

Awards 

1994 ‐ 1996 Jowett Senior Scholar, Balliol College Oxford 

1993 ‐ 1996 PhD scholarship, Boehringer Ingelheim Fonds 

1991 ‐ 1993 Fellow, Studienstiftung des deutschen Volkes 


Mathematical modeling of complex cellular processes: 

Gene‐regulatory networks, especially in T lymphocyte differentiation 

Signal transduction pathways (hormone/calcium and cytokine/Stat pathways, T‐cell receptor signaling) 

Molecular machines of DNA repair, replication, and chromatin regulation 


Scheffold, A., Murphy, K.M., and Höfer T.. (2007) Competition for cytokines: Treg cells take all. Nat. 

Immunol. 8, 1285‐1287 

Höfer, T., Mühlinghaus, G., Moser, K., Yoshida, T.E., Mei, H., Hebel, K., Hauser, A., Hoyer, B., Luger E., 

Dörner, T., Manz, R.A. Hiepe, F., and Radbruch A. (2006) Adaptation of humoral memory. Immunol. 

Rev. 211, 295‐30 

Salazar, C. and Höfer, T. (2003) Allosteric regulation of the transcription factor NFAT1 by multiple 

phosphorylation sites: a mathematical analysis. J. Mol. Biol. 327, 31‐45 

Höfer, T., Nathanson, H., Löhning, M., Radbruch, A., and Heinrich, R. (2002) GATA‐3 transcriptional 

imprinting in Th2 lymphocytes: a mathematical model. Proc. Natl. Acad. Sci. USA 99, 9364‐9368 

Höfer, T., Venance, L., and Giaume, C. (2002) Control and plasticity of intercellular calcium waves in 

astrocytes: a modeling approach. J. Neurosci. 22, 4850‐4859 



( ) a poster 

(x ) a talk 


From molecular machines to gene‐regulatory networks in mammalian cells 

Abstract 

The complexity of regulatory networks in mammalian cells – exemplified by the vast number of 

components and their dynamic interactions on a wide range of time and space scales – requires 

mathematical models at different levels of organization. I will discuss experimentally‐based models of 

(i) the protein machinery that recognizes and repairs UV‐damaged DNA, and (ii) a gene‐regulatory 

network governing T lymphocyte differentiation. Iterative theoretical and experimental analyses of 

network dynamics have allowed us to uncover novel regulatory interactions and identify critical 

molecular steps for controlling the biological functions of the respective networks.


Prof. Dr. Ralf Hofestädt 

Institution Bielefeld University 


Street Address PF 100131 


City Bielefeld 


Phone 0521 106 5283 

Fax 

E‐Mail hofestae@uni‐bielefeld.de 

Website www.uni‐bielefeld.de 

Short CV 

1985‐1990 Assistant researcher, Theoretical Computer Science, University Bonn 

1990‐1994 Assistant Professor, Institute Applied Computer Science, University Koblenz‐ 

Landau; 

1995‐1996 Assistant Professor, Institute of Medical Informatics, University Leipzig; 

1996‐2001 Professor, Institute of Applied Computer Science, University Magdeburg; 

Science 2001 Professor, Institute of Bioinformatics and Medical Informatics, University 

Bielefeld; 

2004 University Heidelberg (Rejected 2004), Professor position for Medical Informatics 

and Bioinformatics; 

Since 2004 Member of the Fachkollegium of the <strong>German</strong> Foundation of Science for 

Bioinformatics – since 2004. 

Scince 2007 Speaker of the “FB Computer Science for Life Science” of the <strong>German</strong> Society of 

Computer 


Databases and database integration; Modeling and dimulation of metabolic processes; Drug pointing; 

Knowledge Representation; Medical Diagnosis Systems, Parallel Computing, GRID Computing, 

Detection of Metabolic Diseases. 


Hofestädt R., Krückeberg F. und Lengauer T. (eds): Informatik in den Biowissenschaften. Informatik 

Aktuell, Springer‐Verlag, Heidelberg 1993. 

Hofestädt R., Lengauer T., Löffler M. and Schomburg D. (eds): Bioinformatics. LNCS, 1278, Springer‐ 

Verlag 1997. 

Hofestädt R. and Schnee R.: Studien und Forschungsführer Bioinformatik. Spektrum Akademischer 

Verlag, Heidelberg, 2002. 

Collado‐Vides J. and Hofestädt R. (eds): Gene Regulation and Metabolism ‐ Post‐Genomic 

Computational Approaches. Cambridge, MA: MIT Press, 2002. 

Kolchanov N. and Hofestädt R. (eds.): Bioinformatics of Genome Regulation and Structure. Kluewer 

Academic Publishers 2004. 

Kolchanov N, Milanesi L. and R. Hofestädt (eds): Binformatics of Genome Regulation and Structure II. 

Kluewer Academic Publishers 2005. 



( ) a poster 


( x) a talk 


RAMEDIS: monitoring of inborn errors based on clinical and molecular data 

Abstract 

The RAMEDIS system is a platform independent, web‐based information system for rare diseases on 

the basis of individual case reports. It was developed in close cooperation with clinical partners and 

collects information on rare metabolic diseases with extensive details, e.g. about occurring symptoms, 

laboratory findings, therapy and genetic data. This combination of clinical and genetic facts enables 

the analysis of genotype‐phenotype correlations. RAMEDIS supports an extendable number of 

different genetic diseases and enables co‐operative studies. Furthermore, use of our system should 

lead to advances in epidemiology, combination of molecular and clinical facts, and generation of rules 

for therapeutic intervention and identification of new diseases. 

Availability: http://www.ramedis.de


Anna Ignatovich, student 

Institution VMiK MSU, INM RAS 


Street Address Orechovij bulvar, 59 ‐ 20 

Zip /Postal Code 115 682 



Phone 8‐495‐344‐20‐27, 8‐916‐596‐88‐39 

E‐Mail anja_ignatovich@mail.ru 

Short CV 

2003‐2008 Moscow State University, Faculty of Computational Mathematics and Cybernetics 


mathematical modeling in immunology 

modelling the dynamics of HIV infection (virus quasispieces, recombination and mutation) 

emergence of drug resistance 

epistasis 

genetic algorithms 



(+) a poster 

( ‐) a talk 


Modelling the dynamics of AZT‐resistant mutants in HIV infection 

Abstract 

Treatment of HIV‐1 infection with drugs (AZT) reduces the HIV‐1 load, but after some time the virus 

evolves drug resistant mutations. In oder to investigate the evolutionary order in which the mutants 

arise and to describe their dynamics, we develop a mathematical model for this situation. The model 

considers the generation of new mutants, their fitness values and the nucleotide misincorporation 

rates. The computational results of modelling the AZT‐driven intra‐patient HIV evolution are discussed. 

Poster number: 13


Dr John Ionides 

Institution St Petersburg State Polytech. Uni 


Street Address Polytehnicheskaya 29 


City St Petersburg 


Phone +7 9213152449 

E‐Mail john.ionides@gmail.com 

Short CV 

1993‐1998 Ph.D NMR methods (Laboratory of Molecular biology, Cambridge, UK) 

1998‐2007 work European Bioinformatics Insitiute; structural bioinformatics 

2007‐ work St Petersburg State Polytech. Uni.; drosophila development 


My interestes revolve around the application of computing techniques to the biological sciences. I 

divide my time between gene expression models (modelling gene expression in Drosophila embryo 

development) and developing architectures for handling large biological datasets, in particular the 

Protein Data Bank (PDB) and the Collaborative Computing Project for NMR (CCPN). 


• Henrick K, Feng Z, Bluhm WF, Dimitropoulos D, Doreleijers JF, Dutta S, Flippen‐Anderson JL, 

Ionides J, Kamada C, Krissinel E, Lawson CL, Markley JL, Nakamura H, Newman R, Shimizu Y, 

Swaminathan J, Velankar S, Ory J, Ulrich EL, Vranken W, Westbrook J, Yamashita R, Yang H, 

Young J, Yousufuddin M, Berman HM. Remediation of the protein data bank archive. 

Nucleic Acids Research. 36(Database issue):D426‐33 (2008). 

• Fogh RH, Boucher W, Vranken WF, Pajon A, Stevens TJ, Bhat TN, Westbrook J, Ionides JM, 

Laue ED. A framework for scientific data modeling and automated software development. 

Bioinformatics 21(8): 1678‐84 (2005) 

• Vranken WF, Boucher W, Stevens TJ, Fogh RH, Pajon A, Llinas M, Ulrich EL, Markley JL, 

Ionides J, Laue ED. The CCPN data model for NMR spectroscopy: development of a software 

pipeline. Proteins. 2005 Jun 1;59(4):687‐96. 

• Pajon, A., Ionides, J., Diprose, J., Fillon, J., Fogh, R., Ashton, A.W., Berman, H., Boucher, W., 

Cygler, M., Deleury, E., Esnouf, R., Janin, J., Kim, R., Krimm, I., Lawson, K.L., Oeuillet, E., 

Poupon, A., Raymond, S., Stevens, T., van Tilbeurgh, H., Westbrook, J., Wood, P., Ulrich, E., 

Vranken W., Xueli, L., Laue, E., Stuart, D.I. and Henrick, K. Design of a Data Model for 

Developing Laboratory Information Management and Analysis Systems for Protein 

Production. PROTEINS: Structure, Function, and Bioinformatics 58(2): 278‐84 (2005) 

• Golovin, T. J. Oldfield, J. G. Tate, S. Velankar, G. J. Barton, H. Boutselakis, D. Dimitropoulos, 

J. Fillon, A. Hussain, J. M. C. Ionides, M. John, P. A. Keller, E. Krissinel, P. McNeil, A. Naim, R. 

Newman, A. Pajon, J. Pineda, A. Rachedi, J. Copeland, A. Sitnov, S. Sobhany, A. Suarez‐ 

Uruena, J. Swaminathan, M. Tagari, S. Tromm, W. Vranken and K. Henrick E‐MSD: an 

integrated data resource for bioinformatics Nucleic Acids Research, 32 (Database issue), 

D211‐D216 (2004) 



( x) a poster 

( ) a talk 


Incorporating interactions between transcription factors into the quantitative prediction of expression 

from regulatory sequences in Drosophila segmentation 

Abstract 

Taking as our starting point the model for transcription decribed by Janssens et al. (Nature Genetics, 

38:1159‐65, 2006) we have incorporated terms for cooperativity and coactivation. The result is a 

considerably more general framework that allows us to take into account specific interactions 

between transcription factors, and consequently to probe for such interactions in silico. 

Poster number: 14


Dr. Pavel S. Ivanov 



M.V.Lomonosov Moscow State University 

Street Address Vorobievy Gory 




Phone +7(495)939‐3025 

Fax +7(495)932‐8820 

E‐Mail psmart@rambler.ru 

Website 

Short CV 

www.psys.msu.ru 

Education B.S., M.S., and Ph.D in Biophjysics from School of Physics, M.V.Lomonosov 

Moscow State University 

Research 

Experience 

2003‐ Senior Research Scientist. Dept. Biophysics, School of Physics, 

M.V.Lomonosov Moscow State University, Moscow, Russia 

2002‐ Visiting Professor. Dept. Molelcular Biology, University of Wyoming, 

Laramie, WY, USA 

Funding INTAS 93‐1877, RFBR 95‐05‐14688, DOE OBER DE‐FG02‐01ER63232 

Keywords Gene expression, microarrays, clustering of expression profiles, regulatory 

networks, operon prediction, gene markers, evolutionary algorithms, 

Rhodobacter sphaeroides, Mus musculus, acute lymphoblastic leukemia 

Research interests 

Resampling approaches for validation of microarray data clustering 

Mechanisms of arginine and polyamines related gene expression and metabolism regulation in 

cardiomyocytes under acute myocardial infarction 

New approaches to construction of a compact set of tumor marker genes and its application in 

differential diagnosis of various subtypes of acute lymphoblastic leukemia 

Inference of operon structure in procaryotes with account of transcriptome variability 


Sveshnikova A.N., Ivanov P.S. (2007). Gene expression and microarrays: quantitative analysis issues. 

Rus. Chem. J., 51: 127‐135 (in <strong>Russian</strong>). 

Harpster M.H., Bandyopadhyay S., Thomas D.P., Ivanov P.S., Keele J.A., Pineguina N., Gao B., 

Amarendran V., Gomelsky M., McCormick R.J., Stayton M.M. (2006). Earliest changes in the left 

ventricular transcriptome postmyocardial infarction. Mamm. Genome, 17: 701‐715. 

Pappas C.T., Sram J., Moskvin O.V., Ivanov P.S., Mackenzie R.C., Choudhary M., Land M.L., Larimer 

F.W., Kaplan S., Gomelsky M. (2004). Construction and validation of the Rhodobacter sphaeroides 2.4.1 

DNA microarray: transcriptome flexibility at diverse growth modes. J. Bacteriol. 186: 4748–4758. 

Baryshnikov, B.V., Ivanov, P.S. (2000). Problems in estimating dimensions of strange attractors in the 

analysis of biophysical data. Biophysics, 45, 520‐524 (in <strong>Russian</strong>). 



(X) a poster 

( ) a talk 


A combined algorithm of tumor marker genes determination and its application to microarray ALL data 

Gulbekyan, G.G., Valyaev, V.Yu, Ivanov P.S. 

Abstract 

We present a novel algorithm for detecting marker genes in multiclass microarray data that combines 

two well‐proved approaches, namely, supervised classification and evolution simulation. We selected 

Support Vector Machine (SVM) to classify microarray samples. First, we use a LKOCV scheme to 

partition initial data into training and test datasets and to fit the parameters of classification algorithm. 

Second, we estimate the classification error for a large number of training/test partitions by 

randomizing initial data. Third, we simulate mutations in a randomly chosen set of potential marker 

genes (predictor) and simultaneous evolution of several such predictors combined in a predictor pool. 

At each evolutionary epoch, we retain a predictor in or exclude it from the predictor pool based on its 

classification power (quality measure). Fourth, we stop the evolutionary process when changes in 

classification quality measure appear to be less than a chosen threshold or after a predefined number 

of iterations. Results of applying this algorithm to a model dataset as well as to pediatric acute 

lymphoblastic leukemia (ALL) microarray data (Ross M. et al. (2003) Blood 102: 2951‐2959) will be 

presented. 

Poster number: 15


Dr. Lars Kaderali 

Institution University of Heidelberg, Bioquant BQ26 






Phone +49‐6221‐54 51 357 

Fax +49‐6221‐54 51 486 

E‐Mail lars.kaderali@bioquant.uni‐heidelberg.de 

Website http://hades1.bioquant.uni‐heidelberg.de 

Short CV 

1995‐2001 Studies Business and Computer Science, University of Cologne 

2001 Research Sabbatical, Los Alamos National Laboratories, USA 

2001‐2006 PhD, Computer Science, University of Cologne 

2006‐2007 Postdoc, <strong>German</strong> Cancer Research Center, Heidelberg 

Since 2007 Independent Junior Group Leader, University of Heidelberg 


Mathematical Modeling and analysis of complex processes in molecular and cell biology, with a 

particular focus on virus‐host interactions 

Method development for the analysis of experimental high‐throughput data, e.g. RNAi data, 

Microarray Data, etc. 

Machine learning tools and statistical learning algorithms in Bioinformatics and Systems Biology 


1. Kaderali, Radde (2007). Inferring Gene Regulatory Networks from Gene Expression Data. In: A. 

Kelemen, A. Abraham, Y. Chen (Editors), Computational Intelligence in Bioinformatics. Springer‐ 

Verlag, in press. 

2. Radde, Kaderali (2007). Bayesian Inference of Gene Regulatory Networks using Gene Expression 

Time Series Data. LNBI Lecture Notes in Bioinformatics, 4414, 1‐15. 

3. Schramm, Vandesompele, Schulte, Dreesmann, Kaderali, Brors, Eils, Speleman, Eggert (2007). 

Translating Expression Profiling into a Clinically Feasible Test to Predict Neuroblastoma Outcome. 

Clinical Cancer Research 13(5), 1459‐1465. 

4. Kaderali, Zander, Faigle, Wolf, Schultze, Schrader (2006). CASPAR: A Hierarchical Bayesian 

Approach to predict Survival Times in Cancer from Gene Expression Data. Bioinformatics 22, 1495‐ 

1502. 

5. Kaderali, Schliep (2001). Selecting signature oligonucleotides to identify organisms using DNA 

arrays. Bioinformatics 18, 1340‐1349. 



(X) a poster 

( ) a talk 


Reverse‐Engineering of Gene Regulatory Networks using Bayes’ regularized Differential Equations 

Abstract 

In this work, we present a novel methodological approach to reverse engineer gene regulatory 

networks from gene expression time series data. We combine differential equations with a dynamic 

Bayesian network approach, enabling us not only to infer a network from given data and make 

predictions of future states of the network, but also allowing it to assign confidences to network 

predictions, evaluate different likely network topologies, and make predictions of future states of the 

network together with confidence intervals on the predictions made. Last but not least, this approach 

makes it possible to give feedbak to experimental groups on which additional experiments would yield 

a maximal reduction in uncertainty about an underlying network topology. 

We show an evaluation of our approach on simulated data, as well as results on the gene regulatory 

network underlying the yeast cell cycle, by analyzing publicly available microarray time series data. In 

addition, we point to applications of our approach in the field of medical systems biology. 

Poster number: 16


Dr. Alexey Kazakov 



Institute for Information Transmission Problems RAS 

Street Address Bolshoy Karetny per. 19 





Fax +7 495 650 0579 

E‐Mail kazakov@iitp.ru 

Website 

Short CV 

http://www.rtcb.iitp.ru/ 

Current degree Ph. D. in genetics (2000) 

Education 1996 ‐ 1999: PhD Student, National Research Center of Mental Health RAMS 

1990 ‐ 1996: Student, Department of Biochemistry, <strong>Russian</strong> State Medical 

University 

Professional 

employment 

2004 ‐ present: Senior Scientist Researcher, Institute for Information 

Transmission Problems RAS 

2000 ‐ 2003: Scientist Researcher, Branch of corporation “Integrated Genomics, 

Inc.” 

1999 ‐ 2000: Junior Scientist Researcher National Research Center for Mental 

Health RAMS 


I am interested in transcriptional regulation of bacterial genes and organization of antimicrobial 

peptides biosynthetic systems. I use comparative genomics and bioinformatics to investigate different 

aspects of evolution of coding and regulatory sequences on a genome level. I also participate in 

development of database of bacterial regulatory motifs. 


1. Novikova M, Metlitskaya A, Datsenko K, Kazakov T, Kazakov A, Wanner B, Severinov K. The 

Escherichia coli Yej Transporter Is Required for the Uptake of Translation Inhibitor Microcin C. J 

Bacteriol, 2007 Nov;189(22):8361‐8365. 

2. Severinov K, Semenova E, Kazakov A, Kazakov T, Gelfand MS. Low‐molecular‐weight post‐ 

translationally modified microcins. Mol Microbiol. 2007 Sep;65(6):1380‐94. 

3. Kazakov AE, Cipriano MJ, Novichkov PS, Minovitsky S, Vinogradov DV, Arkin A, Mironov AA, Gelfand 

MS, Dubchak I. RegTransBase – a database of regulatory sequences and interactions in a wide range of 

prokaryotic genomes. Nucleic Acids Res. 2007 Jan;35(Database issue):D407‐12. 

4. Permina EA, Kazakov AE, Kalinina OV, Gelfand MS. Comparative genomics of regulation of heavy 

metal resistance in Eubacteria. BMC Microbiol. 2006 Jun 5;6:49. 

5. Kazakov AE, Shepelev VA, Tumeneva IG, Alexandrov AA, Yurov YB, Alexandrov IA. Interspersed 

repeats are found predominantly in the “old” alpha satellite families. Genomics. 2003 Dec;82(6):619‐ 

27 



(X) a poster 

( ) a talk 


Identification of microcin C‐like antibiotic systems 

Abstract 

Microcins are a class of small peptide antibiotics secreted by enterobacteria. Genes of microcins 

biosynthesis, maturation and secretion are often organized in operons. Taking into account the 

arrangement of genes encoding microcin precursor and microcin maturation enzymes, we were able 

to identify 17 microcin C‐like antibiotic systems in 13 bacterial species belonging to beta‐, gamma‐ and 

epsilon‐Proteobacteria, Firmicutes and Cyanobacteria. Heptapeptides structurally similar to microcin 

C51 precursor were found in all gene clusters identified, except those from Bartonella henselae and 

two Synechococcus strains. In Synechococcus, two (strain CC9605) or three (strain RS9916) direct 

repeats each encoding 56 or 57 aminoacid polypeptides, respectively, were observed. The ORFs are 

sufficiently similar between the two Synechococcus strains and the C‐terminal amino acids of all the 

ORFs are asparagines, that is typical for microcin C51‐like precursors. So, the cyanobacterial microcin 

precursors may constitute a novel subtype of C51‐related microcins, with potentially different 

properties. Our analysis demonstrates that peptides structurally similar to microcin C51 from E. coli 

may be produced by a variety of bacterial groups. 

Poster number: 17


Dr. Ursula Klingmüller 

Institution <strong>German</strong> Cancer Research Center (DKFZ), A150 






Phone ++49‐6221‐42‐4481 

Fax ++49‐6221‐42‐4488 

E‐Mail u.klingmueller@dkfz.de 


Short CV 

1992 PhD University of Heidelberg, <strong>German</strong>y 

1992‐1993 Postdoctoral Fellow, Harvard Medical School, Boston, USA 

1993‐1996 Postdoctoral Fellow, Whitehead Institute for Biomedical Research, Cambridge, 

USA 

1996‐2003 Independent Group, Leader Max‐Planck‐Institute for Immunology Freiburg, 


2003‐2007 Independent Group Leader, <strong>German</strong> Cancer Research Center (DKFZ), Heidelberg, 


Since 2007 Division Head, <strong>German</strong> Cancer Research Center (DKFZ), Heidelberg, <strong>German</strong>y 

Awards 

1997 FEBS Anniversary Prize 

2006 R. Eils, U. Klingmüller, O. Wiestler, E. Wanka,. A.‐P. Zeng, R. Balling. “Systems 

Biology of Complex Diseases” Ideenwettbewerb <strong>Helmholtz</strong> Association 


We are combining generation of spatiotemporal data with mathematical modeling to identify general 

desing principles in signaling pathways determining cellular decisions. After having data‐based models 

for signaling pathways activated in primary erythroid progenitor cells or hepatocytes our aim is to 

quantitatively determine alterations promoting tumor progression and predict targets for intervention. 


U. Klingmüller, U. Lorenz, L. C. Cantley, B. G. Neel, and H. F. Lodish. Specific recruitment of SH‐PTP1 

to the erythropoietin receptor causes inactivation of JAK2 and termination of proliferative signals. Cell 

(1995) 80:729‐738. 

H. Wu, U. Klingmüller, and H. F. Lodish. Interaction of the erythropoietin and stem‐cell‐factor 

receptors. Nature (1995) 377: 242‐246. 

I. Swameye, T. G. Müller, J. Timmer, O. Sandra, and U. Klingmüller. Identification of nucleocytoplasmic 

cycling as a remote sensor in cellular signaling by data‐based dynamic modeling. PNAS (2003) 

100:1028‐33. 

A. C. Heinrich, R. Pelanda, and U. Klingmüller. A mouse model for visualization and targeted mutations 

in the erythroid lineage. Blood. (2004) 104(3):659‐66. 

M. Schilling, T. Maiwald, S. Bohl, M. Kollmann, C. Kreutz, J. Timmer, and U. Klingmüller. Computational 

processing and error reduction strategies for standardized quantitative data in biological networks. 

FEBS Journal (2005) 272:6400‐6411. 


I will prepare ( ) a poster 

(x) a talk 


Linking the extent of signaling pathway activation with cellular decisions 

Abstract 

Cell growth and differentiation processes are tightly controlled by the activation of complex 

intracellular signaling networks. To identify general design principles and to elucidate molecular 

mechanisms underlying cellular decisions, it is important to combine time‐resolved quantitative data 

with mathematical modeling to test hypothesis, predict the behavior of system and design 

experiments most informative for model validation. We established a data‐based mathematical model 

to examine control of transforming growth factor beta mediated signaling in hepatocytes and can 

show that negative feed‐back mechansims are imortant regulators.


Ms. Olga Koborova 

Institution Faculty of Bioengineering and Bioinformatics 


Street Address 2nd Mosfilmovkii sidestr., h.10, fl.16 



Country <strong>Russian</strong> Federation 


E‐Mail Okoborova@gmail.com 

Short CV 

2006‐present Laboratory assistant, Laboratory of Structure‐Function Based Drug Design, 

Institute of Biomedical Chemistry of Rus.Acad.Med.Sci. 

2003‐present Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State 

University, Graduate student 

Awards 

Certificate and 

medal 

IV Moscow International Congress «Biotechnology: State of the Art and 

Prospects of Development» 


The area of interests includes bioinformatics and system biology. 


1) Koborova O.N., Filimonov D.A., Zakharov A.V., Lagunin A.A., Kel A., Kolpakov F., 

Sharipov R., Poroikov V.V. (2007). Anticander drug targets analysis on the basis of bioinformatic 

technologies, IV International conference “Postgenomic technologies for development of antitumor 

agents with novel mechanisms of action”, Moscow, December 3, 2007, p.15. 

2) Koborova O.N., Zakharov A.V., Lagunin A.A., Filimonov D.А., Kel A., Kolpakov F., Sharipov R., 

Poroikov V.V. Computer‐aided prediction of promising anti‐tumor targets taking into account 

information about probable side effects. (2007). Ibid, p.109. 

3) Koborova O.N., Zakharov A.V., Kel A., Kolpakov F., Sharipov R., Poroikov V.V. (2007) Computer‐aided 

prediction of promisiong pharmacological targets: breast cancer E2F/pRb pathway as a case study. IV 

Moscow International Congress «Biotechnology: State of the Art and Prospects of Development», 

March 12‐16, Moscow. Russia, 2007, p.405. 

4) Poroikov V.V., Koborova O.N., Zakharov A.V., Lagunin A.A., Filimonov D.A., Gloriozova T.A., 

Sharipov R., Kolpakov F., Milanesi L., Kel A. (2006). Targeting cell cycle: old and new stories in 

anticancer therapy. Abstr. 4 th Eurasian Meeting on Heterocyclic Chemistry. Thessaloniki, Greece, 

August 27‐31, 2006, p.73‐74. 



(X ) a poster 

( ) a talk 


Computer‐aided prediction of promising drug targets for breast cancer 

Abstract 

One of the most important problems in search for new anticancer therapies is identification of 

proteins that are involved in emergence and progression of malignant diseases, and to find the most 

prospective targets among them. 

We propose an algorithm of anticancer drug target identification. The algorithm models cell cycle 

regulation as logic networks using the beginning states of nodes (proteins and/or genes) in a primary 

moment and counting a number of node states in different time moments – trajectories. 

The method was applied to different types of breast cancer. We used molecular network consisting of 

8829 nodes and 13613 edges from TRANSPATH database (http://www.biobase.de), and expression 

data, consisting of up and down regulated genes list from Cyclonet database 

(http://cyclonet.biouml.org). Promising targets were identified and it’s inhibition changes trajectory 

into apoptosis of breast cancer cells. Preliminary results demonstrate the applicability of the method 

to find new targets. 

The work was supported by FP6 grant LSHB‐CT‐2007‐037590 (Net2Drug). 

Poster number: 18


Dr. Rainer König 

Institution Pharmacy and Molecular Biotechnology – 

University of Heidelberg 







Fax +49 6221 423620 

E‐Mail r.koenig@dkfz.de 

Website www.dkfz.de/tbi/people/koenig 


2004 ‐ present Group leader of the Network Modelling group in the Department of 

Bioinformatics and Functional Genomics (Director: Prof. Dr. Roland Eils), IPMB, 

Univ. Heidelberg 

2001 – 2004 Postdoctoral Researcher in the Division of Theoretical Bioinformatics (head: 

Prof. Dr. Roland Eils), <strong>German</strong> Cancer Research Center, Heidelberg, <strong>German</strong>y. 

2000 – 2001 Postdoctoral Researcher in the Division of Theoretical 

Bioinformatics (head: Prof. Dr. Martin Vingron), <strong>German</strong> Cancer Research 

Center, Heidelberg, <strong>German</strong>y. 

1996 ‐ 1999 Phd thesis at the EMBL Heidelberg, Phd in 1999 

1989 ‐ 1996 Studies in Physics and Mathematics, Freiburg, Sussex University (England) and 

Heidelberg, Physics Diploma in 1996, Mathematics and Physics state exam in 

1997 

Awards 

2004 Teaching degree “Baden‐Württemberg‐Zertifikat für Hochschullehre” 

2003 Supervisor for exchange students of the “Deutscher Akademischer 

Austauschdienst” (<strong>German</strong> Academic Exchange Service) 

Referee for the journals Nucleic Acids Research, BioMedCentral (BMC 

Bioinformatics, BMC Genomics) and Journal of Biomedical Informatics 

Member of the selection committee for the MSc studies program of Molecular 

Biotechnology at the Life Science faculty, University of Heidelberg 


I'm interested in systematic pattern recognition on networks using machine learning techniques for 

defining drug treatments. 


1. Segun Fatumo, Kitiporn Plaimas, Jan‐Philipp Mallm, Gunnar Schramm, Ezekiel Adebiyi, Marcus 

Oswald, Roland Eils and Rainer König (2008). Estimating novel potential drug targets of Plasmodium 

falciparum by analysing the metabolic network of knock‐out strains in silico, Infection, Genetics and 

Evolution (in press). 

2. Gunnar Schramm, Marc Zapatka, Roland Eils and Rainer König (2007). Using gene expression data 

and network topology to detect substantial pathways, clusters and switches during oxygen deprivation 

of Escherichia coli, BMC Bioinformatics, 8:149 

3. Arunachalam Vinayagam, Coral del Val, Falk Schubert, Roland Eils, Karl‐Heinz Glatting, Sandor Suhai 

and Rainer König (2006). GOPET: A tool for automated predictions of Gene Ontology terms, BMC 

Bioinformatics, 7:161 

4. Rainer König, Gunnar Schramm, Marcus Oswald, Hanna Seitz, Sebastian Sager, Marc Zapatka, 

Gerhard Reinelt & Roland Eils (2006). Discovering functional gene expression patterns in the metabolic 

network of Escherichia coli with wavelets transforms, BMC Bioinformatics, 7:119 

5. Rainer König & Roland Eils (2004). Gene expression analysis on biochemical networks using the Potts 

spin model, Bioinformatics, 20, 1500‐1505. 



(x ) a poster 


( ) a talk 


n.n. 

Abstract 

n.n. 

Poster number: 19


Dr Yuri A Kosinsky 








Fax +7 495 783 87 18 

E‐Mail ykos@nmr.ru 


Short CV 

2004‐present Research Scientist, Institute for Systems Biology SPb 

2000–present Research Scientist, Department of structural biology of Shemyakin and 

Ovchinnikov Institute of Bioorganic Chemestry, <strong>Russian</strong> Academia of Science 


Moscow. Title: Molecular modeling of structural and functional aspects of P‐ 

type ATPases interactions with ATP 

1996‐1999 Post‐graduate of Department of structural biology of Shemyakin and 

Ovchinnikov Institute of Bioorganic Chemestry, <strong>Russian</strong> Academia of Science 

1996 M.Sc., Medico‐Biological Faculty of <strong>Russian</strong> state medical university, Moscow. 

Title: Analysis of cofactors microenvironment in proteins 

1990‐1996 Medico‐Biological Faculty of <strong>Russian</strong> state medical university, Moscow 

Awards 



Research Interests of Dr Kosinsky are focused in areas of Systems Biology, Bioinformatics and 

molecular modeling. Areas of expertise: 





Molecular modeling in drug‐design 

Molecular modeling software development 


Pyrkov, T.V., Kosinsky, Yu.A, Arseniev, A.S., Priestle, J.P., Jacoby, E., Efremov, R.G. (2007). Docking of 

ATP to Ca‐ATPase: Considering Protein Domain Motions. J. Chem. Inf. Model. 47, 1171–1181. 

Pyrkov T.V., Kosinsky Yu.A., Arseniev A.S., Priestle J.P., Jacoby E., Efremov R.G. Complementarity of 

hydrophobic properties in ATP‐protein binding: a new criterion to rank docking solutions. (2007) 

Proteins: Structure, Function, and Bioinformatics 66(2), 388–398. 

Efremov, R. G., Kosinsky Yu.A., Nolde D.E., Tsivkovskii, R., Arseniev A.S., Lutsenko, S. Molecular 

Modeling of the Nucleotide‐Binding Domain of the Wilson’s Disease Protein: Location of the ATP‐ 

binding Site, Domain Dynamics, and Potential Effects of the Major Disease Mutations. (2004) Biochem. 

J. 382, Part I, 293‐305. 

Morgan C.T., Tsivkovskii R., Kosinsky Yu.A., Efremov R.G., Lutsenko S. The Distinct Functional 

Properties of the Nucleotide‐binding Domain of ATP7B, the Human Copper‐transporting ATPase. 

Analysis of the Wilson disease mutations E1064A, H1069Q, R1151H, and C1104F. (2004) J. Biol. Chem. 

279(35), 36363‐36371. 

Kosinsky Yu.A., Volynsky P.E., Lagant P., Vergoten G., Suzuki E., Arseniev A.S., Efremov R.G. 

Development of the Force Field Parameters for Phosphoimidazole and Phosphohistidine. (2004) J. 

Comput. Chem., 25(11), 1313‐1321. 



(X) a poster 

( ) a talk 


Dynamics and regulation of signaling and methaboloc networks in mammalian cells: prostanoids 

biosynthesis and signaling system kinetic modeling. 

Abstract 

Kinetic model of prostaglandines byosynthesis, transport and signaling was build and specially 

adopted for platelets and endothelium cells (ECs). Prothrombotic thromboxane (TXA2) and 

antithrombotic prostacyclin (PGI2) are main prostanoids are produced by platelets and by EC’s, 

respectively. So, disbalance in TXA2 and PGI2 synthesis in blood vessels may increase risk of infarcts or 

strokes. We have analysed influence of cyclooxygenase‐2 (COX2)‐specific inhibitors (coxibes) on 

prostanoids synthesis using our kinetic model describing platelets and EC’s interacting by signaling 

pathways. Our results show that in case of COX2 containing EC’s (inflammatory ECs) applying of 

coxibes may leads to increasing of thrombotic risk, and this effect is more pronounced for inhibitors 

with higher COX2‐selectivety. 

Poster number: 20


Konstantin Kozlov 



SPb SPU 

Street Address Polytechnicheskaya ul., 29 


City St.Petersburg 


Phone +7 812 596 28 31 

Fax +7 812 596 28 31 

E‐Mail kozlov@spbcas.ru 

Website 

Short CV 

http://urchin.spbcas.ru/ 

Date of Birth 02/22/77 

Education Master's Degree in Mathematics, June 2000, Department of applied 

Mathematics, St.Petersburg State Polytechnical University 

Current Position 

Awards 

Research Fellow in Department of Computational Biology, SPb SPU 

Best Speaker Award, „Polytechnic Symposium “Young Scientists for Industry of 

2007 

Noth‐West region““ 

2007 Award of „Participant of Youth Scientific‐Innovative Competition“ 


image analysis, mathematical modelling, software development, systems biology 


Kozlov K., Pisarev A., Matveeva A., Kaandorp J. and Samsonova M. (2007): Image Processing Package 

ProStack for Quantification of Biological Images. In: Proc. of the 4th Intl. Symposium on Networks in 

Bioinformatics (ISNB). Amsterdam, The Netherlands, 204. 

J. Jaeger, S. Surkova, M. Blagov, H. Janssens, D. Kosman, K.N. Kozlov, Manu, E.M. Myasnikova, C.E. 

Vanario‐Alonso, M.G. Samsonova, D.H. Sharp, J. Reinitz. (2004): Dynamic control of positional 

information in the early Drosophila embryo, Nature. Vol. 430. P. 368‐371. 

J. Jaeger, M. Blagov, D. Kosman, K.N. Kozlov, Manu, E.M. Myasnikova, S. Surkova, C.E. Vanario‐Alonso, 

M.G. Samsonova, D.H. Sharp, J. Reinitz. (2004): Dynamical analysis of regulatory interactions in the gap 

gene system of Drosophila melanogaster, Genetics. Vol. 167. P. 1721‐1737. 

Kozlov K.N., Samsonov A.M. (2003): New Data Processing Technique Based on the Optimal Control 

Theory. Techn. Physics, 48, 11: 6‐14. 

K.N. Kozlov, E.M. Myasnikova, M.G. Samsonova, J. Reinitz, D. Kosman. Method for spatial registration 

of the expression patterns of Drosophila segmentation genes using wavelets // Comp. Technol. 2000. 

Vol. 5. P. 112‐119. 



(x) a poster 

( ) a talk 

Title of poster / talk 1 

Comparison of performance of Differential Evolution and Simulated Annealing 

Development of robust and reliable algorithms to reduce the complexity of finding the parameters of 

mathematical models by fitting to experimental data has become a foreground job as modern 

molecular biology accumulates massive amounts of quantitative data. In this work we compare the 

performance of two optimization techniques, namely Differential Evolution (DE) and Simulated 

Annealing (SA). 

Simulated Annealing is a popular method of the functional minimization that was successfully applied 

to important biological problems. Differential Evolution is a rather new and promising optimization 

technique that was invented at the end of the previous century by Storn and Price (Storn, Price, 1995). 

Both methods are stochastic and are able to find the global extremum of the functional under appropriate 

conditions. 

We characterized the dependence of the final functional value and of the iteration number on 

algorithmic parameters by extensive series of numerical runs on a test problem The results for both 

methods were then independently fitted to a power law. While the fitting curves appeared to be 

similar in shape, the performance of Differential Evolution was superior in all our experiments. 

Title of poster / talk 2 

ProStack, the image analysis software to process and quantify patterns of gene expression 

Information on expression in time and space is crucial to infer gene function. Modern sophisticated 

microscope techniques allow to monitor gene expression in real time, with a single cell resolution, and 

can easily produce thousands of images in a single day. However a bottleneck exists at the step of 

image analysis. Due to large number and frequent need for extraction of fine details any visual analysis 

of images is impractical. Image analysis packages available today are either expensive commercial or 

do not support an automatic analysis of images and require strong programming skills to adapt 

programs to a new project. 

We present a new software package ProStack developed to process and quantify patterns of gene 

expression. ProStack is capable to process 2D and 3D digital images of gene expression patterns 

acquired with confocal microscope. It implements more than 50 operations. Each image processing 

procedure is implemented as a separate module. Several modules can be joined in a complex image 

processing scenario, and the intermediate results can be visualized during the workflow enactment. 

The processing operations afford tuning to ensure customization and flexibility without the loss of 

efficiency. The designed workflow can be saved as a complex program module and re‐used in other 

workflows. 

The Prostack package was successfully applied to automatically process a wide range of experimental 

images. 

Poster number: 21 & 22


Ivan Vladimirovich Kulakovskiy 

Institution Engelhardt Institute of Molecular Biology 


Street Address Vavilov str., 32 





Fax +7 499 135 14 05 

E‐Mail ikulakovsky@inbox.ru 

Website www.eimb.relarn.ru/ 

Short CV 

Born 12 July 1985 in Velikiye Luki, Pskov Region 

Mr.Sci in Computer 

Science 

July 2006, Moscow State University of Forest 

Positions, 

Central Scientific Research Institute for Machine Building (Korolev), 

2005‐2006 

Mission Control Center, Telemetry department, Engineer 

2006 Engelhardt Institute of Molecular Biology <strong>Russian</strong> Academy of Sciences 

(Moscow), Graduate student 

2007 State Research Institute of Genetics and Selection of Industrial 

Microorganisms, Scientific Center GosNIIGenetika, Junior researcher 

Language skills <strong>Russian</strong> (native), English (reading) 

Keywords Computational biology, bioinformatics, transcription, system biology, computer 

science, databases 


Transcription factor binding sites: structure, models, effective prediction algorithms, experimental 

data processing. Binding site arrangement and clustering in regulatory modules. Storage and 

processing large data volumes. 



(X) a poster 

( ) a talk 


Integrated tool for analysis of DNA‐protein binding data 

Abstract 

Results of ChIP‐chip tiled arrays and even footprints can bring about rather extended DNA 

segments, which make a challenge for protein binding motif identification with traditional techniques. 

At the same time, now the data of protein binding to DNA is available from many different sources of 

experimental information. Simultaneous analysis of data obtained from such sources as SELEX, ChIP‐ 

chip, footprints etc can result in a much clearer signal for DNA‐protein binding than when using any of 

the data sources alone. 

For instance, the oligos yielded by SELEX strictly correspond to the binding protein, but they 

are usually short and the binding motif in practice is often distorted. At the same time ChIP‐chip arrays 

give functional binding motifs, often in vivo, but the resulting sequences are long and can contain 

binding signals for proteins, different from the test protein. 

Our objective was to make an integrated tool for incorporating different types of 

experimental data into the single protein binding model. For a binding model we have selected the 

Positional Weight Matrix (PWM), which is traditional motif model for transcription factor binding sites 

(TFBS) at DNA. We paid a particular attention to identify the length of a binding signal, the problem, 

which is not solved in many signal identification tools. 

The core of the algorithm is SeSiMCMC Gibbs sampler, which is used to construct the 

anchored optimal multiple local alignment (MLA) of the raw sequence data. “The anchored” here 

means that the any sequence included into MLA should overlap with the anchor sequence initially 

seeded into the data. This layout allowed us to incorporate simultaneously the data of SELEX, which are 

used as anchors in ChIP‐chip sequences. The resulting MLA thus corresponds to the binding signal for 

the correct protein. 

We have tested our system for several TFBS of Human and Drosophila fly. In result now we 

can map specific site occurrences at genome sequences within mapped ChIP‐chip resulting regions as 

well as we can detect genome wide putative TFBS rich regions, which were not covered by ChIP‐chip 

results. This opens a view to compare ChIP‐chip results obtained in different experimental environment 

and study tissue‐specific gene expression. 

Poster number: 23


Alexey Lagunin, Ph.D. 

Institution Institute of Biomedical Chemistry RAMS 


Street Address Pogodinskaya Str., 10 




Phone +7 495 247‐3029 

Fax +7 495 245‐0857 

E‐Mail alexey.lagunin@ibmc.msk.ru 

Website www.ibmc.msk.ru 

Short CV 

2006‐ Present Leading Scientist of Institute of Biomedical Chemistry of Rus. Acad. Med. Sci. 

2004‐2006 Senior Scientist of Institute of Biomedical Chemistry of Rus. Acad. Med. Sci. 

2002‐2004 Research Scientist of Institute of Biomedical Chemistry of Rus. Acad. Med. Sci. 

2001‐2002 Junior Scientist of Institute of Biomedical Chemistry of Rus. Acad. Med. Sci. 

2001 Ph.D. degree in Biochemistry 

1998‐2001 PhD Student of Institute of Biomedical Chemistry of Rus. Acad. Med. Sci. 

1992‐1998 

Awards 

Student of <strong>Russian</strong> State Medical University 

2007 the Award for young scientists for the best research at V.N. Orekhovich 

Institute of Biomedical Chemistry, <strong>Russian</strong> Academy of Medicinal Science 

2001 the Award for young scientists for the best research at V.N. Orekhovich 

Institute of Biomedical Chemistry, <strong>Russian</strong> Academy of Medicinal Science 

2004 the Stipend for young scientists of Regional Social Fund of Native Medicine 

Assistance 


Bioinformatics, system biology, chemoinformatics, structure‐activity relationships analysis, medicinal 

chemistry. 


Poroikov V., Filimonov D., Lagunin A., Gloriozova T., Zakharov A. PASS: identification of probable 

targets and mechanisms of toxicity. SAR QSAR Environ Res. 2007, 18(1‐2):101‐110. 

Kolpakov F., Poroikov V., Sharipov R., Kondrakhin Yu., Zakharov A., Lagunin A., Milanesi L., Kel A. 

CYCLONET—an integrated database on cell cycle regulation and carcinogenesis. Nucleic Acids 

Research, 2007, v. 35, D550–D556 

Lagunin A.A., Dearden J.C., Filimonov D.A., Poroikov V.V. Computer‐aided rodent carcinogenicity 

prediction. Mutation Research, 2005, v.586, p.138–146. 

Poroikov V., Lagunin A., Filimonov D. Pharmaexpert: Diseases, Targets and Ligands – Three in One. 

Proceedings of the 15 th European Symposium on Structure‐Activity Relationships (QSAR) and 

Molecular Modeling, Ed. by Esin Aki (SENER), Ismail Yalcin, Istanbul, September 05‐10, 2004, p.514‐ 

515. 

Lagunin A.A., Gomazkov O.A., Filimonov D.A., Gureeva T.A., Dilakyan E.A., Kugaevskaya E.V., Elisseeva 

Yu.E., Solovyeva N.I., Poroikov V.V. Computer‐Aided Selection of Potential Antihypertensive 

Compounds with Dual Mechanism of Action. J. Med. Chem., 2003, v.46, p.3326‐3332. 



(X) a poster 

( ) a talk 


PharmaExpert: Selection of potential antineoplastic agents 

Abstract 

Computer program PASS predicts ~3300 kinds of biological activity including 378 pharmacotherapeutic 

effects, 2756 mechanisms of action, 50 toxic/side effects and 121 metabolic terms on the basis of 

structural formula of chemical compound with average accuracy ~93% 

(http://www.ibmc.msk.ru/PASS). PharmaExpert interprets PASS predictions taking into consideration 

known mechanism‐effect(s) and effect‐mechanism(s) relationships, and provides a flexible mechanism 

for selection of compounds with desirable kinds of biological activity in libraries of chemical 

compounds. Knowledgebase of the current version of Since PASS predictions contain a plethora of 

information about probable biological actions of chemical compounds, using PharmaExpert it is 

possible to select compounds with the required multiple mechanisms of action. The study is supported 

by FP6‐grant LSHB‐CT‐2007‐037590 (Net2Drug). 

Poster number: 24


Dr. Inna Lavrik 



Street Address INF280 

Zip /Postal Code D69120 



Phone 49‐6221‐423765 

Fax 49‐6221‐411715 

E‐Mail i.lavrik@dkfz.de 

Website http://www.dkfz.de/en/immungenetik/Inna_grou 

p/Inna_englisch.html 

Short CV 

Ph.D. student Department of Chemistry of Natural Compounds, Moscow Lomonosov State 

University, Russia 

Research Assitant Research Assitant, Depar Department of Chemistry of Natural Compounds, 

Moscow State University 

Docent Department of chemistry of natural compounds, Moscow Lomonosov State 

University, Russia 

Since 2000 Scientist, Division of Immunogenetics, Head Prof. Dr P. H. Krammer, DKFZ, 

Heidelberg, 

2004 Project leader, Division of Immunogenetics, Tumorimmunology Programme, 

DKFZ, Heidelberg 

2007 Project leader, SBCancer, Bioquant, DKFZ, Heidelberg 

Awards 

1997 <strong>Russian</strong> state prize for young scientists 


Systems biology of apoptosis 

Death receptors 

Cancer cells 

Caspases 


Bentele M*, Lavrik I *, Ulrich M, Stosser S, Heermann DW, Kalthoff H, Krammer PH, Eils R. 

Mathematical modeling reveals threshold mechanism in CD95‐induced apoptosis. J Cell Biol 

2004;166:839‐851. 

Golks A, Brenner D, Krammer PH, Lavrik IN. The c‐ FLIP‐NH2 terminus (p22‐FLIP) induces NF‐kappaB 

activation. J Exp Med 2006; 203:1295‐1305. 

Krammer PH, Arnold R, Lavrik IN. Life and Death of T cells. Nat Rev Immunol 2007; 7:532‐542. 

Lavrik IN, Golks A, Riess D, Bentele M, Eils R, Krammer PH. (2007). Analysis of CD95 threshold 

signaling: Triggering of CD95(FAS/APO‐1) at low concentrations primarily results in survival signaling. J 

Biol Chem. 18: 13664‐13671. 

Lavrik IN, Golks A, Krammer PH. Caspases: Pharmacological manipulation of cell death. J Clin 

Investigations. 2005; 115(10): 2665‐2672. 



( ) a poster 

(x) a talk 


Life/Death decision on CD95 by systems biology approaches 

Abstract 

CD95 (APO‐1/Fas) is well known as a mediator of apoptosis, however evidence off non‐apoptotic 

functions is accumulating. We investigated NF‐κB activation and apoptosis upon CD95 stimulation and 

established an integrated kinetic mathematical model of CD95‐mediated life and death signalling. 

Systematic model reduction resulted in a surprisingly simple model well approximating experimentally 

observed dynamics. The model postulates that the missing link in CD95‐mediated NF‐κB activation is 

the binding of p43‐FLIP to the IKK complex. This prediction was validated experimentally. Furthermore, 

we demonstrated that the CD95 signalling pathways already diverge at the Death‐Inducing Signalling 

Complex (DISC). Model and experimental analysis of protein assembly in the DISC showed that a subtle 

balance of c‐FLIPL and procaspase‐8 at this multi‐protein complex determines life/death decisions in a 

non‐linear way. This is the first mathematical model explaining the complex dynamics of CD95‐ 

mediated apoptosis and survival signalling.


Vsevolod Jurievich Makeev, PhD 



GosNIIgenetika 

Street Address 1st Dorozhny proezd, 1 





Fax +7 495 315 0501 

E‐Mail Makeev@genetika.ru 

Website 

Short CV 

http://bioinform.genetika.ru 

Born 19 August 1967 in Fryazino, Moscow Region, Russia 

M.Sci in Physics January 1990, Moscow State University 

PhD in Physics and 

Mathematics 

March 1996, Moscow State University 

Positions, 

1990‐1993 

Lebedev Physical Institute, <strong>Russian</strong> Academy of Sceinces. Research Assistant 

1993‐2000 Engelhardt Institute of Molecular Biology, <strong>Russian</strong> Academy of Sciences, Fellow 

Researcher 

2001 State Research Institute of Genetics and Selection of Industrial 

Microorganisms, Scientific Center GosNIIGenetika, Head of the Lab 

Reviewer Nucleic Acid Research, Bioinformatics, BMC Genomics, BMC Bioinformatics, 

PLOS Biology, J. of Biology, Molecular Systems Biology, J. of Bioinformatics and 

Computational Biology, J. of Theoretical Biology, Biokhimia (russ), 

Biotekhnologiya (russ), Biofizika (russ), Molekulyanaja Biologiya (russ) 

Grant Reviewer INTAS, ERA‐NET, FP6, FP7 

Language skills <strong>Russian</strong> (native), English (fluent), <strong>German</strong> (reading), French (reading) 

Keywords Bioinformatics, system biology, genomics, statistical methods, computer 

science, biophysics, transcription, protein‐DNA interaction, protein‐protein 

interaction 


Grammatics of location of transcription factor binding sites in regulatory regions: binding signal 

identification, site clustering, overlapping, periodical patterns. Genome evolution, role of nucleotide 

substitution, repeat expansion and dupications. DNA‐protein and protein‐protein interaction, role of 

ions and water. 


1. Boeva V, Regnier M, Papatsenko D, Makeev V. (2006) Short fuzzy tandem repeats in genomic 

sequences, identification, and possible role in regulation of gene expression. 

Bioinformatics. Mar 15;22(6):676‐84. 

2. Favorov AV, Gelfand MS, Gerasimova AV, Ravcheev DA, Mironov AA, Makeev VJ. (2005) A Gibbs 

sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of 

the signal length. Bioinformatics. May 15;21(10):2240‐5. 

3. Kotelnikova EA, Makeev VJ, Gelfand MS. Evolution of transcription factor DNA binding sites. Gene. 

347, 255‐263, (2005). 

4. Lifanov, A.P, Makeev, V.Ju, Nazina, A., Papatsenko, D.A (2003) "Homotypic regulatory clusters in 

Drosophila". Genome Research, vol 13(4), pp. 579‐88. 

5. Ramensky V.E, V.Ju Makeev, M.A. Roytberg, and V.G. Tumanyan (2000) A Bayesian approach to DNA 

segmentation. J Comput Biol. 7(1‐2):215‐31. 



( ) a poster 

(x) a talk 


Deciphering complex regulatory regions in eukaryotic genomes 

Abstract 

Transcriptional initiation in eukaryotes is controlled by sophisticated complexes of transcription factor 

proteins bound at DNA. To study regulation of gene expression in silico one needs to combine 

experimental data from different sources (footprint, SELEX, ChIP‐chip, etc) on the basis of model 

signals recognized by different transcription factors. We present the system, which gives an 

opportunity to construct and verify the motif models using several combined experimental data 

sources, including footprinting, SELEX and ChIP‐chip experiments, map the result to the genome and 

study the architecture of regulatory modules. 

The integrated system includes the database of genomes and programs for multiple alignment, signal 

recognizing (SeSiMCMC Gibbs sampler) and estimation of statistical significance of a motif model 

(AhoPro word analyser). Some results on genome wide binding signal recognition is presented, 

including signals found in CpG islands, and compared to the experimental data on transcription 

initiation.


Anna Matveeva 

Institution SPbSPU 


Street Address 3 Shkolnaya Street, Apartment 28 


City Saint‐Petersburg 



Fax +7 812 5962831 

E‐Mail anya@odd.bio.sunysb.edu 

Short CV 

2001 – 2007 Student in Biophysics Department, The Faculty of Physics and Mechanics, 

St.Petersburg State Polytechnical University (SPbSPU) 

2007 M.Sc. in Physics, awarded with a diploma from SPbSPU 

2004 – 2005 SPbSPU The Faculty of Physics and Mechanics, The research laboratory of 

Biophysics Department – Research assistant 

2005 – present SPbSPU The Department of Computational Biology, Center for Advanced 

Studies of SPbSPU– Researcher 

Awards 

2007 Medal “For devotion to science” 

2006 The Best Poster Award at the NanoBio’06 Conference 

2005 Medal “For devotion to science” 

2004 The Best Report Award at the Polytechnic Symposium “Young Scientists – 

Industry of the Northwest Region” 


Image processing, biological analysis of quantitative gene expression data. Early development of 

Drosophila melanogaster, segmentation gene network. Transcriptional regulation of the segmentation 

genes, mechanisms of gene silencing. 


A. Matveeva, K. Kozlov and M. Samsonova (2006). Methodology for Building of Complex Workflows 

with PROSTAK package and iSIMBioS. Proceedings of the 5th International Conference on 

Bioinformatics of Genome Regulation and Structure (BGRS'2006), July 16‐22, 2006, Novosibirsk, Russia. 

V. 2, 204‐208. 

A. Matveeva, K. Kozlov, M.Samsonova (2006). Extraction of quantitative gene expression data from the 

images of gene expression patterns with ProStack and iSIMBioS. Proc. of the 4rd TICSP <strong>Workshop</strong> on 

Computational Systems Biology (WCSB 2006), Tampere, Finland, 12‐13 June, 2006, 65‐68. 

A.D. Matveeva, T.P. Sankova (2004). The frequency of encounter of the different alleles VDR gene in 

group of osteoporotic patients and their family members. The All‐<strong>Russian</strong> Interuniversity Scientific and 

Technical Conference, Saint‐Petersburg, Russia, December 4, 2004,149. 



(X) a poster 

( ) a talk 


Modeling the expression of the Drosophila even‐skipped (eve) gene driven by its proximal 1.7 kb 

upstream region. 

Abstract 

A central problem in modern molecular genetics is that of understanding how DNA regulatory 

sequences control gene expression. Metazoan regulatory regions are extremely complex and 

qualitatively different from those of prokaryotes. For example, the regulatory regions of genes 

controlling development in Drosophila are large and consist of groups of binding sites, called as cis‐ 

regulatory modules (CRMs), each controlling some aspects of gene expression. 

The goal of our work is to understand the role of proximal 1.7 kb upstream regulatory sequence in the 

regulation of the Drosophila even‐skipped (eve) gene in terms of binding sites. To do this we 1) 

quantitatively monitor gene expression at high resolution in space in time, 2) characterize each 

transcription binding site and 3) construct a new quantitative and predictive model of transcriptional 

readout. We predicted the organization and function of the proximal 1.7 kb eve upstream regulatory 

sequence. The best model with lowest rms (8.66) was built with in silico addition the Sloppy‐paired 

binding site and assigning to Hunchback a role of repressor. 

Poster number: 25


Julia Medvedeva 



GosNIIgenetika 

Street Address 1, 1st Dorozhnyj pr. 




Phone (7495)3150156 

Fax (7495)3150105 

E‐Mail 

Short CV 

ju.medvedeva@gmail.com 

September 1995 ‐ 

May 2003 

Moscow State University, student 

August 2003 ‐ 

October 2005 

Enhelgardt Institute of Molecular Biology, technician 

November 2005 – 

till now 

GosNIIgenetika, PhD student 

April 2007 – till now GosNIIgenetika, researcher 


I’m deeply interested in bioinformatics and genome sequence analysis. Now I’m studying CpG islands 

in genomes of different vertabrates, their sequence structure and properties. In spite of the fact that 

CpG islands in different gene region demonstrate diferent level of methylation, they could play similar 

regulatory function, probably as regions with clusters of protein binding sites (for ex. Sp1, CTCF) or 

structural regions appropriate for origins of DNA replication. 


Medvedeva Yu., Rychkov A., Oparina N. Imprinted genes in human and mouse genomes: detailed 

analysis of CpG islands // Moscow Conference on Computational Molecular Biology (MCCMB'05), july 

2005, Moscow, Russia 

Medvedeva Yu., Fridman M., Oparina N., Makeev V. CpG islands distribution in the human genome // 

Genomics, Proteomics and Bioinformatics, april 2006, Almaty, Khazahstan 

Medvedeva Yu., Abnizova I., Naumenko F., Oparina N., Makeev V Identification of CpG island 

boundaries // Moscow Conference on Computational Molecular Biology (MCCMB’07), july 2007, 




(x) a poster 

( ) a talk 


Reduced level of synonimous substitution in CpG containing codons suggests functional role of 

intragenic and 3’ CpG islands in human genes 

Abstract 

CpG islands (CGIs) are usually defined as DNA segments that are longer than 200 bp, have over 50% of 

G+C content, and have CpG frequency of at least 0.6 of that statistically expected [1]. Most of the 

studies focused on CpG islands considered CGIs associated with 5' gene regions. Generally, such 

islands are more than 1 kb long, can cover the promoter, TSS, the first coding exon and have stronger 

parameters of G+C content and Obs/Exp [2]. The methylation status of such CGIs is believed to 

influence the transcription level of a corresponding gene. 

Contrary to the widespread opinion only 50% of CGIs are located near TSS. About 20% 

gene‐associated CGIs are disposed in internal and 3’ terminal gene regions. Internal exons display less 

overlapping with CGIs than exons in 5’ regions and to some extend exons in 3’ regions of the genes. 

CGIs associated with 3’ region of the gene are more often overlapped with coding exons than with 3' 

UTR [1]. 

Thus, the question arises if CGIs overlapping with protein‐coding regions are in fact the 

result of selection at the protein level. In this work we compared selection at genome and protein 

levels by studying substitution rate between human and mouse orthologs in CpG sites belonging to 5’‐ 

assosiated, intragenic and 3’‐assosiated CGIs. To this end we compared the substitution rate in exons 

overlapping and not overlapping with CGIs separately for non‐CpG containing codons (the background) 

and CpG containing codons. 

Using dn/ds test we came to the following conclusions: 

1. CpG island decrease the substitution rate in СpG pairs at synonymous sites approximately 

two‐fold. 

2. Effect of CGI does not depend on the exon location within the gene: 5’‐assosiated, 

intragenic and 3’‐assosiated CGIs protect CpG sites from methylation and probably play the same 

regulatory role in gene functioning. 

References: 

[1] Gardiner‐Garden, M. & Frommer, M. 1987. CpG islands in vertebrate genomes. J. Mol.Biol. 196, 

261–282. 

[2] Ponger L., Duret L., Mouchiroud D. 2001. Determinants of CpG Islands: Expression in Early Embryo 

and Isochore Structure. Genome Research. 11, 1854–1860. 

[3] Ina, Y. 1995. New methods for estimating the numbers of synonymous and nonsynonymous 

substitutions. J. Mol. Evol. 40, 190–226. 

(This work is a joint study with M.Fridman, N.Oparina, D.Malko, E.Ermankova, I.Kulakovsky, V.Makeev) 

Poster number: 26


Natalia Medvedeva 

Graduate Student 

Institution MSU CMC 


Street Address Marshala Nedelina 40‐13 





E‐Mail solnush@gmail.com 

Short CV 

2003 ‐ present Moscow State University, Faculty of Computational Mathematics and 

Cybernetics 


Application of mathematics to biology 

Model Identification 

Computational modelling 


Bocharov GA, Medvedeva NA. (2008) Model identification in immunology: computational approaches 

to sensitivity and information‐theoretic complexity analyses. In: “Numerical methods, parallel 

computing and information technologies” Eds. Vl.V. Voevodin and E.E.Tyrtyushnikov. MSU: Moscow (in 

<strong>Russian</strong>) 



(x) a poster 

( ) a talk 


Sensitivity analysis of the mathematical model of type I interferon response to MHV infection in mice 

Abstract 

Sensitivity analysis is an important element in the development of a mathematical model. There are 

different approaches for solving this problem. One of them, the Latin hypercube sampling was used for 

performing sensitivity analysis of the mathematical model of type I interferon response to MHV 

infection in mice. It is a type of stratified Monte Carlo sampling. This technique with using partial 

correlation coefficients allows to rank the model parameters according their influence on output 

variables of the model. 

Poster number: 27


Eugeniy Metelkin 








Fax +7 495 783 87 18 

E‐Mail metelkin@insysbio.ru 


Short CV 

1998‐2004 Faculty of Physics, Biophysical Department, Moscow State University 

2004‐2007 PhD Courses, Faculty of Physics, Biophysical Department, Moscow State 

University 

2004‐present Institute for Systems Biology SPb 


Research Interests of mine are focused in areas of Systems Biology and Bioinformatics and scientific 

programming with especial focus on quantitative description of biological processes and their 

application to biotechnology and biomedicine. Areas of expertise: 







Metelkin E., Goryanin I., Demin O. Mathematical modeling of mitochondrial adenine nucleotide 

translocase. Biophys. J (2006) v.90 p.423‐432 

Goryanin II, Lebedeva GV, Mogilevskaya EA, Metelkin EA, Demin OV. Cellular kinetic modeling of the 

microbial metabolism. Methods Biochem Anal (2006) 49, 437‐488 

Demin O.V., Plyusnina T.Y., Lebedeva G.V., Zobova E.A., Metelkin E.A., Kolupaev A.G., Goryanin I.I., 

Tobin F. Kinetic modelling of the E. coli metabolism. IN: Topics in Current Genetics (2005) 31‐67, Eds. 

Alberghina L., Westerhoff H.V. , Springer 



(X) a poster 

( ) a talk 


Encyclopaedia of stem cells: reconstruction of transformation pathways, signaling networks and 

prediction of biomarkers 

Abstract 

This information system can be used for the following purposes: 

1) as the tool for research. The potential users are pharmaceutical companies that work at new drugs 

of cancer, osteoporosis, blood diseases; biological, medical and pharmaceutical research institutes; 

clinics; stem cells banks. 

2) as the guide for the stuff of stem cells companies. The potential users are clinics; pharmaceutical 

companies; research institutes; centers of science information and libraries. 

3) as the visualization tool in e‐commerce. The potential users are the companies that specialized in 

selling of stem cells signaling proteins, antibodies of these proteins and different reagents allowing to 

detect different states of stem cells. 

4) as the tool for students training of biological, medical and pharmacological institutes. The potential 

users are biological, medical and pharmacological institutes and libraries. 

The main characteristics of informational system “Stem cells” are: 

(a) Originality of the data structure that allows being used it for researches; visualizations of 

intercellular mechanisms and as the guide for the stuff of stem cells companies. 

(b) Technology of data collection and analysis of biological information. Our technique includes the 

methods of intracellular signaling pathways reconstruction on the base of published experimental data 

and the software that handles data automatically for visualization and modeling of signaling 

transduction. 

(c) Fullness of the data concerning stem cells functioning. This information includes the biological and 

medical aspects of stem cells functioning. 

Poster number: 28


Prof. Andrey Mironov 

Institution Moscow State Univ. Department of Bioengineering 

and Bioinformatics 


Street Address Lab. Bldg B, Vorobiovy Gory 1‐73, 





Fax +7 (495) 939 41 95 

E‐Mail mironov@bioinf.fbb.msu.ru 

Short CV 

2003‐present Professor, Dept. of Bioengineering and Bioinformatics, Moscow State 

University, Moscow, Russia 

2001‐2003 Director of technology Moscow office Integrated Genomics Inc., Moscow, 

Russia 

1986‐2001 Head of department of Mathematical Modeling, NIIGenetica, Moscow, Russia 

1978‐1985 Senior Researcher in dept. of Mathematical Modeling, NIIGenetica, Moscow, 

USSR 

1972‐1978 Junior Researcher in lab.of Optimization in Mechanics in Institute of Mechanics 

Academy of Science USSR 

Awards 

2007 Baev award, <strong>Russian</strong> acedemy of sciense 


Bioinformatics, Comparative genomics, RNA structure, Gene regulation 


Merkeev IV, Novichkov PS, Mironov AA. PHOG: a database of supergenomes built from proteome 

complements. BMC Evol Biol. 2006 Jun 22;6:52. 

Danilova LV, Pervouchine DD, Favorov AV, Mironov AA. RNAKinetics: a web server that models 

secondary structure kinetics of an elongating RNA. J Bioinform Comput Biol. 2006 Apr;4(2):589‐96. 

Tompa M, Li N, Bailey TL, Church GM, De Moor B, Eskin E, Favorov AV, Frith MC, Fu Y, Kent WJ, Makeev 

VJ, Mironov AA, Noble WS, Pavesi G, Pesole G, Regnier M, Simonis N, Sinha S, Thijs G, van Helden J, 

Vandenbogaert M, Weng Z, Workman C, Ye C, Zhu Z. Assessing computational tools for the discovery 

of transcription factor binding sites. Nat Biotechnol. 2005 Jan;23(1):137‐44. 

Neverov AD, Artamonova II, Nurtdinov RN, Frishman D, Gelfand MS, Mironov AA. Alternative splicing 

and protein function. BMC Bioinformatics. 2005 Nov 7;6:266. 

Novichkov PS, Omelchenko MV, Gelfand MS, Mironov AA, Wolf YI, Koonin EV. Genome‐wide molecular 

clock and horizontal gene transfer in bacterial evolution. J Bacteriol. 2004 Oct;186(19):6575‐85. 



( ) a poster 

(X) a talk 


Alternative splicing and secondary structure of RNA ‐ Systematic computational study and 

experimental verification of predictions 

Abstract 

Pre‐mRNA structure has been reported to impact many cellular processes, including splicing in genes 

associated with human disease. In this work we examine introns of twelve Drosophila species for the 

presence of conserved complementary motifs capable of forming stable RNA structures. Over 200 

introns with such motifs were identified, suggesting a role for secondary structures in regulation of 

splicing. Indeed, mutations that decrease base pairing affected splicing efficiency or induced 

alternative splicing in 4 of 7 cases tested, while compensatory mutations that restored base pairing 

suppressed these effects. Our results represent a proof of principle that naturally occurring secondary 

structures can be relevant to splicing of many more genes than it is believed currently. Moreover, 

without the evolutionary conservation constraint, the predicted number of introns capable of forming 

stable RNA structures increases up to 7000, raising the intriguing hypothesis that these structures 

could actually participate in splicing of every second gene in fruit flies.


Dr Ekaterina Mogilevskaya 










Fax +7 495 783 87 18 

E‐Mail zobova@genebee.msu.su 



2005‐present Researcher, Institute for Systems Biology SPb 


Moscow. Title: Theoretical investigation of the Krebs cycle functioning and 

regulation in E.coli and mitochondria. 

2002‐2005 Post‐graduate of Biophysical Department, Moscow State University 

1996‐2002 Medico‐Biological Faculty, <strong>Russian</strong> State Medical University 

Awards 



Research Interests of Dr Mogilevskaya are focused in areas of Systems Biology and Bioinformatics with 

especial focus on quantitative description of biological processes and their application to 

biotechnology and biomedicine. Areas of expertise: 





• Mogilevskaya E. A., Lebedeva G. V., Goryanin I. I., Demin O. V. Kinetic model of Escherichia coli 

isocitrate dehydrogenase functioning and regulation. // Biophysics, 2007, 52(1), 47‐56. 

• Goryanin II, Lebedeva GV, Mogilevskaya EA, Metelkin EA, Demin OV. Cellular kinetic modeling of 

the microbial metabolism. Methods Biochem Anal (2006) 49, 437‐488 

• Mogilevskaya E.A., Demin O.V., Goryanin I.I. Kinetic Model of Mitochondrial Krebs Cycle: 

Unraveling the Mechanism of Salicylate Hepatotoxic Effects // Journal of Biological Physics. – 

2006. ‐ V. 32. ‐ P. 245‐271. 

• Demin O.V., Plyusnina T.Y., Lebedeva G.V., Mogilevskaya (Zobova) E.A., Metelkin E.A., Kolupaev 

A.G., Goryanin I.I., Tobin F. Kinetic modelling of the E. coli metabolism. IN: Topics in Current 

Genetics (2005) 31‐67, Eds. Alberghina L., Westerhoff H.V. , Springer. 

• Demin O.V., Lebedeva G.V., Kolupaev A.G., Mogilevskaya (Zobova) E.A., Plyusnina T.Yu., Lavrova 

A.I., Dubinsky A., Goryacheva E.A., Tobin F., Goryanin I.I. Kinetic Modelling as a Modern 

Technology to Explore and Modify Living Cells // Modelling in Molecular Biology. Natural 

Computing Series. – Springer, 2004. ‐ P. 59‐103. 


I will prepare (X) a poster 


Application of kinetic modeling to understand hepatotoxic effects of salicylate. 

Abstract 

Liver injury is the serious side effect of the salicylate – an active derivative of aspirin [1]. What are the 

mechanisms of salicylate induced hepatotoxicity and how it can be prevented? To answer these 

questions the detailed kinetic model of mitochondrial Krebs cycle is constructed. It is known that 

salicylate inhibits mitochondrial energy metabolism. It was shown [2] that in stress conditions of high 

energy demand mitochondria can switch from tricarboxylic acids to alternative substrates oxidation in 

the Krebs cycle. The model describes this stress situation when glutamate, malate and alpha‐ 

ketoglutarate are the substrates of the Krebs cycle. It was shown on the model that the inhibition of 

succinate dehydrogenase and alpha‐ketoglutarate dehydrogenase by salicylate contributes 

substantially to the cumulative inhibition of the Krebs cycle by salicylate. Whereas the uncoupling of 

oxidative phosphorylation and coenzyme A consumption in salicylate transformation processes has 

little effect on the rate of substrate oxidation in the Krebs cycle. It was found that the salicylate‐ 

inhibited Krebs cycle flux can be increased by flux redirection in the Krebs cycle through increase of 

cytosol glutamate and malate concentrations and depletion in cytosol alpha‐ketoglutarate and 

mitochondrial glycine concentrations [3]. 

1. Bjorkman D. Nonsteroidal anti‐inflammatory drug‐associated toxicity of the liver, lower gastrointestinal tract, and 

esophagus. 1998. American Journal of Medicine, 105(5A), 17S‐21S. 

2. Kondrashova M.N. Structuro‐kinetic organization of the tricarboxylic acid cycle in the active functioning of 

mitochondria. 1989. Biophysics, 34, 450‐458. 

3. Mogilevskaya E.A., Demin O.V., Goryanin I.I. Kinetic Model of Mitochondrial Krebs Cycle: Unraveling the 

Mechanism of Salicylate Hepatotoxic Effects // Journal of Biological Physics. – 2006. ‐ V. 32. ‐ P. 245‐271. 

Poster number: 29


Dr.rer.nat. Mario S. Mommer 

Institution BIOMS / IWR / Heidelberg University 






Phone +49 – 6221 ‐ 54‐8890 

E‐Mail mario.mommer@iwr.uni‐heidelberg.de 

Short CV 

Undergraduate Mathematics. Merida – Tuebingen – Aachen, until 12.1999 

PhD 1.2000‐8.2005, Numerical Analysis 

Postdoc 9.2005‐4.2006, Univ. of Utrecht 

Postdoc 5.2006‐present, BIOMS Postdoc, Univ. of Heidelberg 


Spatiotemporal organization of cells and cellular processes, in particular Ca2+ signalling in neutrophils, 

diffusive transport within cells, and organization of the cytoskeleton and ER. Mathematical methods 

for capturing the „exotic“ behaviour typical of complex cellular processes. 


M. S. Mommer, A Smoothness Preserving Fictitious Domain Method for Elliptic Boundary Value 

Problems. IMA J. of Num. Ana., July 2006. 

K. Eppler, H. Harbrecht and M. S. Mommer, A new fictitious domain method in shape optimization, 

Comp. Opt. Appl., October 2007. 

M. S. Mommer, D. Lebiedz, Modelling Subdiffusion Using Reaction Diffusion Systems, IWR Preprint 

7168, February 2007. 



( X ) a poster 

( ) a talk 


A model for submembrane Calcium waves in migrating neutrophils 

Abstract 

We have put together, from data found in the biological and biophysical literature, a mathematical 

model that may explain the localized, submembrane Ca2+ waves that have recently been observed in 

migrating neutrophils by Kindzelski and Petty (Journal of Immunology, 2003). In the model, the waves 

arise from an interaction between the gating dynamics of voltage gated T‐Type Ca2+ channels, who 

have a short active phase and a long refractory one, and the local depolarization caused by Ca2+ influx. 

We find that this model features traveling pulses among its solutions, and furthermore, that it 

reproduces the reflection behaviour observed when two pulses collide. All relevant parameters are 

within the ranges found in the literature. 

Poster number: 30


PD Dr. Margareta Mueller 








Fax +49‐6221‐424551 

E‐Mail Ma.mueller@dkfz.de 

Website 

Short CV 

1982‐1984 University Saarland, <strong>German</strong>y, Biol. Sciences 

1984‐1987 Albert Ludwigs University Freiburg, <strong>German</strong>y, Masters Degree, Biol. Sciences 

1988‐ 1992 PhD University of California at Irvine, USA 

1992‐1995 Postdoctoral fellow, german Cancer Research Center (DKFZ), Heidelberg, 


1995‐2004 Research group leader in the division of Carcinogennesis and Differentiation, 

DKFZ, Heidelbreg <strong>German</strong>y 

1999 Visiting scientist Istituto di Mutagenesi e Differenziamento, Pisa, Italy 

2001 Appointment as C3‐Professor, University of applied sciences Hamburg, 


2004/2005 Habilitation/Venia legendi in tumor biology, Heidelberg, <strong>German</strong>y 

since 2004 Leader of the independent research group. Tumor and Microenvironment, 

DKFZ, Heidelberg, <strong>German</strong>y (12 coworkers) 

Awards 

1988‐1990 PhD scholarship german Academic Exchange Service 

1990‐1992 PhD scholarship, State of California 

1992‐1993 Research Scholarship European Union 

2001 travel award of the EACR 

2003 NIH travel award (Gordon Conference) 


Growth factor networks in tumor stroma interaction 

Il‐6 signal transduction in tumor and stromal cells 

Functional connection between Inflammation, angiogenesis and tumor progression 


Kiessling F., Greschus S., Lichy M.P., Bock M., Fink C., Vosseler S., Moll J., Mueller M.M., Fusenig N.E., 

Traupe H., Semmler W. Volumetric Computed Tomography (VCT): A Novel Technology for Non‐ 

Invasive High Resolution Monitoring of Tumor Angiogenesis. Nature Med 2004; 10:1133‐1138 

Mueller M.M. Fusenig N.E. Friends or foes: Bipolar effects of the tumor stroma in cancer growth and 

regression. Nature Rev Cancer 2004; 4(11):839‐49. 

Obermueller E., Vosseler S., Fusenig N.E., Mueller M.M. Co‐operative auto‐ and paracrine functions of 

G‐CSF and GM‐CSF in promoting tumor progression of skin carcinoma cells. Cancer Res 64(21):7801‐12, 

(2004). 

Mueller M.M. Inflammation in epithelial skin tumors: Old stories and new ideas. Eur J Cancer 

2006;42(6):735‐44 

Gutschalk C.M. , Herold‐Mende C., Fusenig N.E., Mueller M.M. G‐CSF and GM‐CSF promote malignant 

growth in sqaumous cell carcinomas of the head and neck. Cancer Res 2006; 66: 8026‐36


Dr. Angela Oberthür 

Institution BIOQUANT –University of Heidelberg 







Fax +49‐6221‐54‐51438 

E‐Mail angela.oberthuer@bioquant.uni‐heidelberg.de 

Website http://www.bioquant.uni‐heidelberg.de/ 

Short CV 

2002 PhD at Darmstadt University 

2002‐2003 Princeton University Press, Princeton NJ, USA 

2003‐2004 <strong>German</strong> Cancer Research Center, Heidelberg 

Scientific Assistant to the Chairman of the Management Board 

2004‐2007 <strong>German</strong> Cancer Research Center , Heidelberg 

Head of Division Strategy Planning and Program Coordination 

Since 04/2007 BIOQUANT, Heidelberg University 

Managing Director 


BIOQUANT the recently established Center for "Quantitative analysis of molecular and cellular bio 

systems" at the University of Heidelberg is the first research center in <strong>German</strong>y solely dedicated to 

Systems Biology. 

It is the central building of the interdisciplinary research network BIOQUANT that integrates system 

biology research dispersed over the various university and non‐university research centers at 

Heidelberg campus. It unites experimental scientists and theoreticians under one roof, and its 

objective is to represent a platform for the constant refinement of models and the swift validation of 

scientific hypotheses via experimental data. 

Currently, projects of four prestigious research consortia are accommodated at BIOQUANT, namely 

FORSYS‐VIROQUANT funded by BMBF, the public‐ private‐partnership program BioMS, the excellence 

cluster CellNetworks funded by the DFG as well as parts of the <strong>Helmholtz</strong> program Systems Biology of 

Cancer (SBCancer). 

BIOQUANT's research program consists of four distinct but closely interconnected project areas 

dedicated to the investigation of protein machines‐ biogenesis, interaction and regulation; dynamics of 

cell architecture; information processing in complex multi‐cellular networks and alteration of networks 

by infectious pathogens. 

A central technology platform at BIOQUANT is providing advanced computational methods and tools 

for data analysis, visualization and modeling as well as cutting edge technologies and equipment for 

quantitative analysis.


Kirill Peskov 










Fax +7 495 783 87 18 

E‐Mail kirillpeskov@gmail.com 


Short CV 

2004‐present Research Scientist, Institute for Systems Biology SPb 

2004–2007 Post‐graduated Student, Institute of Theoretical and Experimental Biophysics 

<strong>Russian</strong> Academy of Science, Pushchino, Moscow Region, Russia. 

1999‐2004 Student, Biophysical Department, Faculty of Biology, Moscow State University 

Awards 



Research Interests of Mr. Peskov are focused in areas of Systems Biology and Bioinformatics and 

scientific programming with especial focus on quantitative description of biological processes and their 

application to biotechnology and biomedicine. Areas of expertise: 









I will prepare (X ) a poster 


Kinetic modeling of Escherichia coli central carbon metabolism. 

Abstract 

The understanding of a complex network of genetic and metabolic regulations is a great challenge of 

system study of Escherichia coli metabolism. Indeed, it is rather difficult to estimate the contribution of 

different types of regulations to intracellular dynamics on the one hand and, on the other, to 

understand the role of all possible interactions between metabolic and genetic networks. One of the 

ways to cope with the problems is to use the kinetic modeling approach to integrate different types of 

experimental data and understand more about regulatory behavior of the intracellular system [1‐3]. 

Additionally, kinetic modeling allows us to simulate interactions between metabolic and genetic 

networks and predict complex dynamic behavior, which is typical for central carbon Escherichia coli 

metabolism. The main idea of our approach is a detailed mechanistic description of all elementary 

biochemical interactions, e. g. rate equations of single enzymes, elements of metabolic and genetic 

regulations [4‐6]. This significantly increases the predictive power of in silico modeling and extends the 

possible range of applications [4]. 

The aims of this investigation could be briefly formulated in the following way: 

Pathway reconstruction of central carbon metabolism and its metabolic and genetic regulation 

networks 

Development of kinetic model 

Detail mathematical description of each elementary step of the model (rates of reaction of the 

individual enzymes, elements of metabolic and genetic regulation). 

Maximal level of model verification with different types of experimental data (both in vitro and in 

vivo). 

Model analysis and predictions. 

During these work we have obtained several interesting prediction about functioning of central carbon 

Escherichia coli metabolism concerning metabolic and genetic regulation of this pathway. 

References: 

1. M. Santillan and M. C. Mackey, "Influence of catabolite repression and inducer exclusion on the 

bistable behavior of the lac operon.," Biophys J. 86, 1282‐1292 (2004). 

2. C. Chassagnole, N. Noisommit‐Rizzi, J. W. Schmid, K. Mauch and M. Reuss, "Dynamic modeling of 

the central carbon metabolism of Escherichia coli," Biotechnol Bioenerg. 79, 53‐73 (2002). 

3. M. M. Altintas, C. K. Eddy, M. Zhang, J. D. McMillan and D. S. Kompala, "Kinetic modeling to 

optimize pentose fermentation in Zymomonas mobilis.," Biotechnol Bioenerg 94, 273‐295 (2006). 

4. I. I. Goryanin, G. V. Lebedeva, E. A. Mogilevskaya, E. A. Metelkin and O. V. Demin, "Cellular kinetic 

modeling of the microbial metabolism. ," Methods Biochem Anal 49, 437‐488 (2006). 

5. E. Mogilevskaya, I. I. Goryanin and O. V. Demin, "Kinetic model of mitochondrial Krebs cycle: 

unraveling the mechanism of salicylate hepatotoxic effects," J Biol Phys. 32, 245–271 (2006). 

6. E. Metelkin, I. Goryanin and O. Demin, "Mathematical Modeling of Mitochondrial Adenine 

Nucleotide Translocase," Biophys J. 90, 423‐432 (2006). 

Poster number: 32


Prof. Dr. Vladimir Poroikov 








Fax +7 495 245‐0857 

E‐Mail vladimir.poroikov@ibmc.msk.ru 

Website http://ibmc.msk.ru/en/departments/drug_design/ 

Short CV 

2000 Professor (Biochemistry), High Attestation Comission of Russia 

1995 D.Sci. Degree (Pharmacology), NRC BAC 

1981 Ph.D. Degree (Biophysics), MSU 

1998 ‐ present Vice‐Director (Research), Institute of Biomdical Chemistry of Rus. Acad. Med. 

Sci. (IBMC), Moscow, Russia 

1995 ‐ present Head of Laboratory for Structure‐Function Based Drug Design, IBMC 

1996 ‐ present Professor, Medical & Biological Faculty of <strong>Russian</strong> State Medical University, 


1974 ‐ 1995 National Recearch Center for Biologically Active Compounds (NRC BAC), 

Staraya Kupavna, Moscow Region, Russia (the last position – Head of 

Department for Drug Design and Marketing) 

1968 ‐ 1974 Student, Physical Faculty, The M.V. Lomonosov Moscow State University 

(MSU), Russia 

Awards 

2005 Diploma of Cambridge Biographic Institute (Leading Scientist in Bioinformatics 

and Computer‐Aided Drug Discovery) 

2004 Diploma of <strong>Russian</strong> Academy of Medical Sciences 

2001‐2003 Special Stipend for Outstanding <strong>Russian</strong> Scientists awarded by the <strong>Russian</strong> 

Academy of Sciences 

2001 Gold Medal “For Scientific Partnerships” 

1997 Medal "850 Years of Moscow" 

1968 Gold Medal on School Graduation 


Bioinformatics, chemoinformatics, molecular modelling, (Q)SAR/(Q)SPR, computer‐aided drug 

discovery, systems biology. 


Poroikov V., Filimonov D., Lagunin A., Gloriozova T., Zakharov A. (2007). PASS: Identification of 

probable targets and mechanisms of toxicity. SAR & QSAR in Environmental Research., 18 (1‐2), 101‐ 

110. 

Fomenko A.E., Filimonov D.A., Sobolev B.N., Poroikov V.V. (2006). New approach to predict enzyme 

function without the alignment. OMICS: A Journal of Integrative Biology, 10 (1), 56‐65. 

Filimonov D.A., Poroikov V.V. (2005). Why relevant chemical information cannot be exchanged without 

disclosing structures. J. Comput.‐Aided Mol. Design, 19 (9‐10), 705‐713. 

Geronikaki A., Dearden J., Filimonov D., Galaeva I., Garibova T., Gloriozova T., Krajneva V., Lagunin A., 

Macaev F., Molodavkin G., Poroikov V., Pogrebnoi S., Shepeli F., Voronina T., Tsitlakidou M., Vlad L. 

(2004). Design of new cognition enhancers: from computer prediction to synthesis and biological 

evaluation. J. Med. Chem., 47 (11), 2870‐2876. 

Poroikov V.V., Filimonov D.A., Borodina Yu. V., Lagunin A.A., Kos A. (2000). Robustness of biological 

activity spectra predicting by computer program PASS for non‐congeneric sets of chemical compounds. 

J. Chem. Inform. Comput. Sci., 40 (6), 1349‐1355. 



( ) a poster 

(X) a talk 


Bio‐ and chemoinformatics of multitargeted anticancer drugs 

Abstract 

Due to the progress in postgenomic studies it became obvious that many diseases have a complex 

etiology, and the multitargeted drug concept appears, according which such remedies have some 

advantages comparing to the monotargeted medicines. Discovery of new multitargeted drugs can be 

made by prediction of biological activity spectra with computer system PASS, which predicts more 

than 3,000 biological activity types and molecular mechanisms of action with average accuracy about 

95%. Potential of bioinformatics and computer‐aided drug discovery methods in definition of 

prospective sets of particular molecular targets and identification of lead substances for future 

anticancer multitargeted drugs in the databases of available chemical compound samples will be 

discussed.This work is supported by FP6 (LSHB‐CT‐2007‐037590 ‐ Net2Drug, ISTC/BTEP (3197/111), 

RFBR (05‐07‐90123, 06‐03‐08077, 06‐03‐39015).


Ksenia Pougach 

Undergraduate student 

Institution IMG RAS 


Street Address 2 Kurchatov Sq. 


City Moskow 


Phone 8 909 622 78 14 

E‐Mail xenik‐alt@mail.ru 

Short CV 

Personal Born December 17 th , 1985, Russia, Single 

Citizenship Russia 

Current position Student at Moscow State University 

Address Russia, 119991, Moscow, Leninskie gori, MSU, 1 build.12 

Phone (7)(495)196‐02‐15 

E‐mail xenik‐alt@mail.ru 

Education M.S., Molecular Biology, 2008, Moscow State University, Russia 


Transcription regulation in prokariotes 

Microarrays 

RNAP 

Bacteriophages 

RNAi 



a poster 


Comparative analysis of gene expression strategy in T‐even phages 

Abstract 

Our long‐term goal is to understand the function and regulation of cellular DNA‐dependent RNA 

polymerase (RNAP) in molecular detail. Bacterial viruses‐‐phages‐‐evolved elaborate mechanisms to 

regulate host transcription to make it serve the needs of the virus. The variety of phages and the 

number of regulatory mechanisms that they evolved exceeds the variety of bacterial regulatory 

mechanisms by orders of magnitude. Phage regulatory systems are usually compact, robust, and 

efficient (i.e., phage‐encoded proteins are small, they interact with host RNAP tightly, and their 

regulatory effects are strong). The goal of this research is to uncover phage‐induced modifications of 

bacterial host RNAP, to study them in vitro, and to determine the role of these modifications in viral 

development. In vitro, RNAP sites that are targeted by phage regulators will be identified and the 

mechanisms of action of phage regulators will be determined using discriminatory biochemical assays. 

In vivo, genetic and genomic approaches will be used to understand the biological consequences of 

RNAP modifications by phage‐encoded factors. Comparative analysis of transcription regulation in 

several relatives of T4, a classical phage known to rely on extensive modification of host RNAP by 

phage‐encoded proteins for expression of phage genes, will be performed. The goal is to determine 

how T4 relatives that lack transcription factors that are essential for T4 development express their 

genes in a coordinate manner. 

Poster number: 33


Dr. Vasily Ramensky 

Institution Engelhardt Institute of Molecular Biology of 

<strong>Russian</strong> Academy of Sciences 


Street Address Vavilova, 32 





Fax +7‐499‐135‐1405 

E‐Mail ramensky@imb.ac.ru 

Website http://www.imb.ac.ru/~ramensky/ 

Short CV 

Bioinformatics; systems biology; molecular evolution; drug design; protein 

structure and function 

Awards 

2002‐2003 V.A.Engelhardt award for young scientists; EIMB RAS 

2006‐2007 "Human molecular Polymorphism" grant program from <strong>Russian</strong> Academy of 

Sciences; principal investigator 


Prediction of functional effect of amino acid variation in proteins: PolyPhen (Polymorphism 

Phenotyping) 

Computational drug design 

Protein structure and evolution 

Compositional analysis of DNA sequences: BASIO (Bayesian Approach to Sequence segmentatIOn) 


1. S.R.Sunyaev, V.E.Ramensky, P.Bork. (2000) Towards a structural basis of human non‐synonymous 

single nucleotide polymorphisms. Trends Genet, Vol.16, No.5, pp. 198‐200. 

2. V.E.Ramensky, V.Ju.Makeev, M.A.Roytberg, V.G.Tumanyan (2001) Segmentation of long genomic 

sequences into domains with homogeneous composition with BASIO software. Bioinformatics, Vol.17, 

No.11, pp.1065‐1066. 

3 V.Ramensky, P.Bork, S.Sunyaev (2002) Human non‐synonymous SNPs: server and survey. Nucleic 

Acids Res., Vol. 30, pp.3894‐3900. 

4.V. Ramensky, A. Sobol, N. Zaitseva, A.Rubinov, V.Zosimov (2007) A novel approach to local similarity 

of protein binding sites substantially improves computational drug design results. Proteins. Vol 69, 

pp.349‐357 

5. E. Reuveni, V.Ramensky, C.Gross (2007) Mouse SNP Miner: an annotated database of mouse 

functional single nucleotide polymorphisms. BMC Genomics. Vol. 8, pp.24. 



(x) a poster 

( ) a talk 


Computational drug design based on a novel approach to local similarity of protein binding sites 

Abstract 

We present a novel notion of binding site local similarity based on the analysis of complete protein 

environments of ligand fragments. Comparison of a query protein binding site (target) against the 3D 

structure of another protein (analog) in complex with a ligand enables ligand fragments from the 

analog complex to be transferred to positions in the target site, so that the complete protein 

environments of the fragment and its image are similar. The revealed environments are similarity 

regions and the fragments transferred to the target site are considered as binding patterns. The set of 

such binding patterns derived from a database of analog complexes forms a cloud‐like structure 

(fragment cloud), which is a powerful tool for computational drug design. It has been shown on 

independent test sets that application of fragment clouds to self‐docking and screening dramatically 

improves the results and enables reliable reproduction of experimental ligand optimization results. 

Poster number: 34


Dr. Babette Regierer 

Institution University of Potsdam 


Street Address Karl‐Liebknecht‐Str. 24‐25, House 20 


City Potsdam‐Golm 



Fax +49 331 977 70 2811 

E‐Mail regierer@uni‐potsdam.de 

Website www.forsys.de 

Short CV 

1994‐1997 PhD at Max Planck Institute for Molecular Plant Physiology (Potsdam) 

1997‐2000 Postdoc in EU‐Project ““Phosphate and Crop Productivity” (Potsdam) 

2000‐2001 Coordinator of the “Interdisciplinary Network for Human Genome Research” 

(Berlin) 

2002‐2003 Scientific Manager at Max Planck Institute for molecular Genetics (Berlin) in 

Dep. Vertebrate Genomics of Prof. Hans Lehrach 

2003‐2007 Scientific Coordinator of the Interdisciplinary Center “Advanced Protein 

Technologies” at University of Potsdam 

2008‐ Scientific Manager of the Program “FORSYS – Research Units for Systems 

Biology” in <strong>German</strong>y 


Systems Biology in different model systems (human, animals, plants and microorganisms) and 

technology development. 


Geigenberger P, Regierer B, Nunes‐Nesi A, Leisse A, Urbanczyk‐Wochniak E, Springer F, van Dongen JT, 

Kossmann J, Fernie AR (2005) Inhibition of de novo pyrimidine synthesis in growing potato tubers leads 

to a compensatory stimulation of the pyrimidine salvage pathway and a subsequent increase in 

biosynthetic performance. Plant Cell. 17(7):2077‐88. Epub 2005 Jun 10. 

Zimmermann P, Kossmann J, Frossard E, Amrhein N, Bucher M (2004) Differential expression of three 

purple acid phosphatases from potato. Plant Biol (Stuttg) 6(5), 519‐28 

Geigenberger P, Regierer B, Lytovchenko A, Leisse A, Schauer N, Springer F, Kossmann J, Fernie AR 

(2004) Heterologous expression of a ketohexokinase in potato plants leads to inhibited rates of 

photosynthesis, severe growth retardation and abnormal leaf development. Planta 218(4), 569‐78 

Regierer B, Fernie AR, Springer F, Perez‐Melis A, Leisse A, Koehl K, Willmitzer L, Geigenberger P, 

Kossmann J (2002) Starch content and yield increase as a result of altering adenylate pools in 

transgenic plants. Nat Biotechnol Dec, 20(12):1256‐60 

van Voorthuysen, T, Regierer, B, Springer, F, Dijkema, C, Vreugdenhil, D, Kossmann, J, (2000) 

Introduction of polyphosphate as a novel phosphate pool in the chloroplast of transgenic potato plants 

modifies carbohydrate partitioning. J. Biotech., 77, 65‐80 



(x) a poster 

( ) a talk 


“FORSYS – Research Units for Systems Biology” 

A new research program for Systems Biology in <strong>German</strong>y 

Abstract 

The <strong>German</strong> Federal Ministry of Education and Research (BMBF) has launched a new initiative for 

Systems Biology "FORSYS ‐ Research Units on Systems Biology" to complement the existing research 

programs in <strong>German</strong>y. The FORSYS centers enable the networking of relevant scientific areas under 

one roof and in close connection to the regional cooperating institutions which together form the 

intellectual and technological basis. The centers at the locations of Freiburg, Heidelberg, Magdeburg 

and Potsdam started the work in 2007. One focal point of the FORSYS programme is the promotion of 

young talented researchers by establishment of junior research groups as an excellent basis for future 

careers in Systems Biology. For early stage researchers the FORSYS units offer dedicated master 

courses and structured doctoral programs in Systems Biology. 

Poster number: 35


Seong‐Hwan Rho, Ph.D 

Institution Freiburg Center for Data Analysis and Modeling 

(FDM) 


Street Address Hermann‐Herder‐Str. 3A 


City Freiburg 



Fax +49 761 203 8539 

E‐Mail shrho@fdm.uni‐freiburg.de 

Short CV 

2007‐present Postdoc, Systems biology, FDM, <strong>German</strong>y 

2005‐2007 Postdoc, Systems biology, Sytems Biology Institute, Korea 

1998‐2005 Ph.D., Structural biology, Gwangju Institute of Science and Technology, Korea 

1996‐1998 M.S., Electrophysiology. Gwangju Institute of Science and Technology, Korea 

1991‐1996 B.S., Electrical engineering, Seoul National University, Korea 


Systems biology of eukaryotic systems, quantitative modeling of cellular signaling and experimental 

design 

Bioinformatics and statistical analysis, network inference of transcriptomics and proteomics data 

Protein structure modeling and design; ion channels, proteases 


Rho, S.H., Park, H.H., Kang, G.B., Im, Y.J., Kang, M.S., Lim, B.K., Seong, I.S., Seol, J., Chung, C.H., Wang, 

J., et al. (2007). Crystal structure of Bacillus subtilis CodW, a noncanonical HslV‐like peptidase with an 

impaired catalytic apparatus. Proteins. (in press) 

Hong, S.E., Rho, S.H., Yeom, Y.I., and Kim do, H. (2006). HCNet: a database of heart and calcium 

functional network. Bioinformatics 22, 2053‐2054. (co‐first author) 

Lee, E.H., Rho, S.H., Kwon, S.J., Eom, S.H., Allen, P.D., and Kim do, H. (2004). N‐terminal region of 

FKBP12 is essential for binding to the skeletal ryanodine receptor. J Biol Chem 279, 26481‐26488. 

Rho, S., Lee, H.M., Lee, K., and Park, C. (2000). Effects of mutation at a conserved N‐glycosylation site 

in the bovine retinal cyclic nucleotide‐gated ion channel. FEBS Lett 478, 246‐252. 

Rho, S.H., and Park, C.S. (1998). Extracellular proton alters the divalent cation binding affinity in a cyclic 

nucleotide‐gated channel pore. FEBS Lett 440, 199‐202. 



(X) a poster 

( ) a talk 


Tracing down the EpoR mediated pathway in the hematopoietic system 

Abstract 

The serine/threonine protein kinase Akt/PKB downstream of erythropoietin receptor (EpoR) plays an 

important role in red blood cell development by controlling proliferation and differentiation of 

erythroid progenitor cells. Previous studies have identified multiple pathways involved in EpoR‐Akt 

signaling, being the PI3‐kinase as a major and direct activator of Akt along with possible or indirect 

contributions from other proteins, such as Gab2, K‐ras, and IRS‐2, etc. The detailed contribution by 

each signaling molecule, however, is yet to be established. 

The goal of our study is to understand the detailed Akt activation mechanism in the hematopoietic 

system upon EpoR stimulation by utilizing a data‐based quantitative modeling approach. To this end, 

the quantitative information of signaling proteins in the EpoR signal transduction was obtained 

experimentally from two distinct cellular systems; BaF3 cell line, a murine pro B cell line lacking EpoR 

and dependent on Interleukin‐3 (IL‐3) for growth, and CFU‐E cells, murine fetal liver erythroid 

progenitors with highly expressed EpoRs that undergo normal terminal proliferation and 

differentiation. 

Attenuation of the activated Akt was observed only in BaF3 cells expressing ectopic EpoRs, but not in 

CFU‐E cells. BaF3 cells have higher amount of PTEN and SHIP1 proteins that are known as negative 

feedback regulators of PI3‐kinase pathway. Our model including PTEN or SHIP1 could successfully 

explain the quantitative difference in Akt activation between BaF3 and CFU‐E cells and, thus, supports 

the existence of negative feedback regulation of PI3K activity in the hematopoietic system, too. 

The traditional model assuming that PI3K accounts for most of the activation of Akt was failed to fit 

the experimental data obtained from BaF‐3 cells with a series of mutant EpoRs. A new model including 

a feedforward loop to amplify the signal through PI3K is proposed and it is now under testing by 

experiments. 

http://web.mit.edu/ccr/faculty/pages/lodish.htm 

Poster number: 36


Dr. Carlos Salazar 



Street Address Im Neuenheimer Feld 280 (B086) 





Fax +49 6221 54 51487 

E‐Mail c.salazar@dkfz‐heidelberg.de 

Short CV 

2007‐ Postdoc at the <strong>German</strong> Cancer Research Center, Heidelberg 

2005 ‐ 2007 Postdoc at the group of Theoretical Biophysics, Humboldt University and 

biochemical research at the Leibniz Institute of Molecular Pharmacology, Berlin 

2002 ‐ 2005 PhD studies in Theoretical Biophysics at Humboldt University Berlin 

2000 ‐ 2002 Studies of Biophysics at Humboldt University Berlin, <strong>German</strong>y 

1997 ‐ 2000 Teaching assistant at the Department of Physical Chemistry, University of 

Havana 

1992 ‐ 1997 Studies of Chemistry at University of Havana, Cuba 

Awards 

2000 ‐ 2002 Scholarship from the <strong>German</strong> Academic Exchange Service (DAAD) 

1999 Annual award given by the Rector of the University of Havana 

1997 Best graduating student of University of Havana in the area of academic results 


My research focuses on the regulation of signal transduction networks controlling transcriptional 

activity. Current projects include: 1) ligand binding and activation of T cell receptor, 2) activation of 

the PI3K/Akt signaling pathway, 3) initiation of DNA replication in yeast, 4) regulation of protein 

activity by multisite phosphorylation, 5) hierarchical organization of metabolic networks. 


Matthaus F.*, Salazar C.* and Ebenhöh O.* (2008) Biosynthetic potentials of metabolites and their 

hierarchical organization. PLoS Comput. Biol. (to appear) 

Salazar C.*, Politi A.Z.* and Höfer T. (2008) Decoding of calcium oscillations by phosphorylation cycles: 

analytic results. Biophys J., 94 (doi:10.1529/biophysj.107.113084) 

Salazar C. and Höfer T. (2007) Versatile regulation of multisite protein phosphorylation by the order of 

phosphate processing and protein‐protein interactions. FEBS J. 274, 1046‐1061 

Salazar C. and Höfer T. (2005) Activation of the transcription factor NFAT1: Concerted or modular 

regulation? FEBS Lett. 579, 621‐626 

Salazar C. and Höfer T. (2003). Allosteric regulation of the transcription factor NFAT1 by multiple 

phosphorylation sites: A mathematical analysis. J. Mol. Biol. 327, 31‐45 

*equal contribution 



(X) a poster 

( ) a talk 


Regulation of the initiation of DNA replication by multisite protein phosphorylation 

Abstract 

Protein phosphorylation at several sites is a common regulatory mechanism in cell signaling and cell 

cycle regulation that may impose a certain activation threshold or allow the synchronization of 

molecular events. Using experimental data and mathematical modeling, we studied the role of 

multisite phosphorylation in regulating the initiation of DNA replication. DNA replication is initiated 

simultaneously at several origins, whose firing is controlled by S‐CDK through the multiple 

phosphorylation of the substrates Sld2 and Sld3. Our kinetic analysis shows that multiple 

phosphorylation serves as a timing device, ensuring concerted firing of replication origins. Random 

phosphorylation is found to be a more robust timer than sequential phosphorylation and, indeed, a 

random mechanism has been implicated experimentally in Sld2 phosphorylation. Taken together, our 

results show that the order in which several phosphorylation sites are modified by the enzymes is 

crucial for an optimal regulation of the cell cycle. 

Poster number: 37


Dr. Maria Samsonova 

Institution St.Petersburg State Polytechnical Uniersity 


Street Address Polytekhnichaskaya 


City St.Petersburg 



Fax +7‐812‐596‐2831 

E‐Mail samson@spbcas.ru 

Website http://urchin.spbcas.ru/site/index.htm 

Short CV 

1972 Graduated Dept. of Genetics, Leningrad State University 

1979 PhD in Biology, Leningrad State University 

Awards 

1999 SGI Outstanding Speaker Award at the ISMB’99 conference 


systems biology and developmental biology 


Surkova, S., Kosman, D., Kozlov, K. N., Manu Manu, Myasnikova, E., Samsonova, A., Spirov, A., 

Vanario‐Alonso, C. E., Samsonova, M., Reinitz, J.(2008) Characterization of the Drosophila segment 

determination morphome. Dev. Biol., 313, 844‐862. 

Ekaterina Myasnikova, Maria Samsonova, David Kosman and John Reinitz (2005). Removal of 

background signal from in situ data on the expression of segmentation genes in Drosophila. 

Development, Genes and Evolution, 215(6):320‐326. 

Johannes Jaeger, Svetlana Surkova, Maxim Blagov, Hilde Janssens, David Kosman, Konstantin N. 

Kozlov, Manu, Ekaterina Myasnikova, Carlos E. Vanario‐Alonso, Maria Samsonova, David H. Sharp, and 

John Reinitz (2004). Dynamic control of positional information in the early Drosophila embryo. Nature, 

430:368‐371. 

Johannes Jaeger, Maxim Blagov, David Kosman, Konstantin N. Kozlov, Manu, Ekaterina Myasnikova, 

Svetlana Surkova, Carlos E. Vanario‐Alonso, Maria Samsonova, David H. Sharp, and John Reinitz (2004). 

Dynamical analysis of regulatory interactions in the gap gene system of Drosophila melanogaster. 

Genetics, 167:1721‐1737. 

E. Myasnikova, A. Samsonova, M. Samsonova and J. Reinitz. Support vector regression applied to the 

determination of the developmental age of a Drosophila embryo from its segmentation gene 

expression patterns (2002). Bioinformatics, 18, S87‐S95. 



(x) a talk 


Variation and canalization of gene expression in the Drosophila blastoderm 

Abstract: 

Here we investigate the mechanisms of canalization and embryonic regulation in the morphogenetic 

field that controls the segment determination in {\em Drosophila}. The data used for this 

characterization are quantitative with cellular resolution in space and about 6.5 minutes resolution in 

time. At cycle 13 and the early time classes of cycle 14A the patterns of zygotic segmentation genes 

show considerable variation in amplitude, the way, time and sequence of domain formation, as well as 

significant positional variability. Nevertheless, this variation is dynamically reduced, or canalized by the 

onset of gastrulation. We use the dynamical theory framework to understand this behavior.


Dr. Johannes P. Schlöder 

Institution Interdisciplinary Center for Scientific Computing 

(IWR) ‐ University of Heidelberg 







Fax +49 6221 54 5444 

E‐Mail schloeder@iwr.uni‐heidelberg.de 

Website 

http://www.iwr.uni‐heidelberg.de/~Johannes.Schloeder/ 

Short CV 

since 2007 Member of “Heidelberg Graduate School of Mathematical and Computational 

Methods for the Sciences” 

since 2007 Academic Director at IWR 

since 2001 Participation in International Ph.D. Training Group 

“Complex Processes: Modeling, Simulation and Optimization”, 

Heidelberg‐Warsaw 

1993‐1995 Member of Research Center 

“Reactive Flow, Diffusion and Transport” (SFB359) 


since 1992 Senior researcher at the 

Interdisciplinary Center for Scientific Computing (IWR) 


1989‐1992 Teaching assistant at the Institute for Mathematics, 

University of Augsburg 

1987‐1989 Research assistant at the Institute for Applied Mathematics, 

Department of Applied Analysis, University of Bonn 

1987 Ph.D. in Applied Mathematics, University of Bonn 

1980‐1987 Scientific collaborator at the 

Institute for Applied Mathematics and Research Center 

“Applied Optimization and Approximation” (SFB 72) 

University of Bonn 


Modeling, simulation and optimization of dynamic processes; numerical methods for large‐scale 

constrained optimisation, optimal control, real‐time optimisation and nonlinear model predictive 

control; numerical methods for parameter and state estimation, optimum nonlinear experimental 

design and optimization under uncertainty; applications in aerospace, mechanics, biomechanics and 

robotics; chemical engineering, reaction kinetics, biophysics, environmental physics, epidemiology and 

systems biology. 


1. Bock HG, Kostina E, Schlöder JP (2007): 

Numerical Methods for Parameter Estimation in Nonlinear Differential Algebraic Equations 

GAMM Mitt. 30, 2: 376‐408. 

2. Diehl M, Bock HG, Schlöder JP (2005): 

A real‐time iteration scheme for nonlinear optimization in optimal feedback control. 

SIAM J. on Control and Optimization 43:1714‐1736. 

3. Bock HG, Körkel S, Kostina EA, Schlöder JP (2004): 

Numerical Methods for Optimal Control Problems in Design of Robust Optimal 

Experiments for Nonlinear Dynamic Processes. 

Optimization Methods and Software 19:327‐338. 

4. Leineweber DB, Bauer I, Bock HG, Schlöder JP (2003): 

An Efficient Multiple Shooting Based SQP Strategy for Large‐Scale Dynamic Process 

Optimization. Part I: Theoretical Aspects, Part II: Software Aspects and Applications. 

Comput. Chem. Engng. 27:137‐174. 

5. Dieses A, Schlöder JP, Bock HG, Richter O (2002): 

Optimal Experimental Design for Parameter Estimation in Column Outflow Experiments. 

Water Resources Research 38:1186ff.


Dr. Sebastian M. Schmidt 

Institution Forschungszentrum Jülich GmbH 


Street Address Wilhelm‐Johnen‐Straße 


City Jülich 


Phone +49‐2461 61‐3901 

Fax +49‐2461 61‐2640 

E‐Mail s.schmidt@fz‐juelich.de 

Website www.fz‐juelich.de 


Since 2007 Member of the Board of Directors representing the areas of „Key Technologies 

and Structure of Matter“ at Research Centre Jülich 

2006 ‐2007 Managing director and head of the area of “research” in the <strong>Helmholtz</strong> 

Association 

2002‐2005 Scientific officer at the head office of the <strong>Helmholtz</strong> Association 

2001 Habilitation at the universities of Tübingen and Rostock, <strong>German</strong>y 

2000‐2002 Leader of an Emmy Noether Research Group at the University of Tübingen, 


1999‐2000 Alexander von Humboldt fellow at Argonne National Laboratory, USA 

1997‐1998 University assistant at University of Rostock, <strong>German</strong>y 

1995‐1996 Minerva Fellow at the University of Tel Aviv, Israel 

1995 PhD with the title of “Dr. rer. nat.” in theoretical physics at the University of 

Rostock, <strong>German</strong>y 

Awards 

1999 Fellowship by the Alexander von Humboldt Foundation 

1995 Minerva Fellowship 


Research Management 

Key Technologies 

Structure of Matter 


Alkofer R, Hecht MB, Roberts CD, Schmidt SM, Vinnik DV. Pair creation and an x‐ray free electron laser. 

PHYSICAL REVIEW LETTERS 87:193902 (2001) 

Blaschke D, Grigorian H, Poghosyan G, Roberts CD, Schmidt SM. A dynamical, confining model and hot 

quark stars. PHYSICS LETTERS B 450:207‐214 (1999) 

Blaschke DB, Prozorkevich AV, Roberts CD, Schmidt SM, Smolyanski SA. Pair production and optical 

lasers. PHYSICAL REVIEW LETTERS 96:140402 (2006) 

Blaschke D, Roberts CD, Schmidt S: Thermodynamic properties of a simple, confining model. PHYSICS 

LETTERS B, 425:232‐238 (1998) 

Roberts CD, Schmidt SM: Dyson‐Schwinger equations: Density, temperature and continuum strong 

QCD. PROGRESS IN PARTICLE AND NUCLEAR PHYSICS, 45:S1‐S103 (2000)


Ursula Schöttler 

Institution Deutsches Krebsforschungszentrum 







Fax +49 6221 42‐2840 

E‐Mail u.schoettler@dkfz‐heidelberg.de 


Short CV 

Biology teacher by training. 

1970‐1978 Adult education in language training, Cairo, Egypt 

Project management, Cairo, Egypt 

1978‐1987 Teaching, project management, fundraising event management 

Accra, Ghana 

1988‐1992 Assistant to CEO, ORPEGEN Pharma GmbH Heidelberg, 

Office and project management 

since 1988 Language trainer, adult education 

1992‐2007 Admin. Assistant to Chairman of DKFZ 

Management of international conferences 

since 2007 Coordinator, International Services DKFZ 

Special Interests 

Intercultural and interdisciplinary communication and management 

International conference and project management


Prof. Dr. Klaus Schughart 

Institution <strong>Helmholtz</strong> Centre for Infection Research 


Street Address Inhoffenstraße 7 


City Braunschweig 


Phone 0049 (0) 531 6181 1100 

Fax 0049 (0) 531 6181 1199 

E‐Mail Klaus.Schughart@helmholtz‐hzi.de 

Website www.helmholtz‐hzi.de 

Short CV 

since 1.8.06 Head of Dept. Experimental Mouse Genetics (<strong>Helmholtz</strong> Center for Infection 

Research, Braunschweig) and Professorship at the University of Veterinary 

Medicine, Hannover (TiHo) 

2002 ‐ 2006 Head of Scientific Controlling and Deputy Scientific Director at the <strong>Helmholtz</strong> 

Center for Infection Research, Braunschweig 

1997 ‐ 2001 Head of Dept of Molecular and Cellular Biology, Transgene S.A., Strasbourg 

1995 ‐ 1996 Project Leader at Institute of Mammalian Genetics, GSF Munich 

1990 ‐ 1995 Group Leader at Max‐Planck‐Institute of Immune Biology, Freiburg 

1987 ‐ 1989 Postdoc in Dept. of Biology, Yale University, New Haven (Dr. F.H. Ruddle) 

1986 PhD at the Institute of Genetics, University of Cologne 

1983 Diploma in Biology at University of Cologne 

Awards 

1986 Fellowship from the Boehringer Ingelheim Fonds 

1987‐1988 Postdoctoral Research fellowship of the <strong>German</strong> Research Foundation 


We are using mouse genetic reference populations (recombinant inbred lines and various laboratory 

strains) to identify gene regulatory networks in immune cells. Whole genome expression profiling from 

different T‐cell populations is performed and bioinformatics tools are used to identify expression 

Quantitative Traits (eQTLs). In addition, our laboratory is studying the host susceptibility to infections 

with influenza A virus in the mouse model system, using the same genetic reference populations and 

whole genome expression profiling. 

We are looking for collaborations to further explore our large data sets from T cells and infection 

studies to develop in silico models of gene interaction circuits. 


(1) Schughart K., and Churchill G. (2007). 6th Annual Meeting of the Complex Trait Consortium. 

Mammalian Genome: 18:683‐5. 

(2) Streicher J., Donat M.A., Strauss B., Sporle R., Schughart K., Mueller G.B. (2000). Computer‐based 

three‐dimensional visualization of developmental gene expression. Nature Genetics 25:147‐52. 

(3) Hrabé de Angelis M., … Schughart K., Wolf E., and Balling R. (2000) Genome wide large scale 

production of mutant mice by ENU mutagenesis. Nature Genetics 25, 444‐447. 

(4) Schughart, R. Bischoff, U.B. Rasmussen, D. Ali Hadji, F. Perraud, N. Accart, O. Boussif, N. Silvestre, Y. 

Cordier, A. Pavirani, M. Courtney, H.V.J. Kolbe (1999) Solvoplexes, a new type of non‐viral vectors for 

intrapulmonary gene delivery. Human Gene Therapy 10, 2891‐2905. 

(5) R. Spoerle and K. Schughart (1998). Paradox segmentation along inter‐ and intrasomitic borderlines 

is followed by dysmorphology of the axial skeleton in the open brain (opb) mouse mutant. 

Developmental Genetics 22, 359‐373. 



( ) a poster 


(X) a talk 


Systems Genetics for Infection and Immunity 

Abstract 

Regulatory T cells (T‐reg) play an important role for the proper coordination of the different 

components of the immune response. The absence of T‐reg cells results in severe allergies and auto‐ 

immune diseases. So far, only a few genes and pathways are known that are involved in T‐reg 

differentiation and maintenance. We have collected T‐reg cells from 40 different recombinant inbred 

mouse lines and determined their expression patterns in whole genome arrays. Each of the BXD 

mouse strains has a slightly different genetic background and, therefore, the transcriptional expression 

profiles of many genes vary from strain to strain. Since the genotypes of all strains are known, it is 

possible to identify upstream regulatory loci for all transcripts that differ in expression levels and 

which have a genetic component. In this way, single regulatory interactions can be described as 

expression Quantitative Traits (eQTLs). Furthermore, differences in expression profiles of whole sets of 

genes that are controlled by the same genomic locus can be identified as groups of co‐regulated genes. 

In the next step, we want to use these data, and additional data from infection studies, to build 

computer models of gene regulatory pathways in infection and immunity.


Sergey Smirnov 

Institution Institute for Systems Biology SPb 








Fax +7 495 783 87 18 

E‐Mail smirnov‐ssv@yandex.ru 


Short CV 

2006‐present Research scientist of Institute for Systems Biology SPb 

2004 ‐ 2006 Researcher in the laboratory of Modeling of Metabolism and Bioinformatics in 

the Institute of Theoretical and Experimental Biophysics (Poushchino). 

2002 ‐ 2004 Research officer in EMPproject company. 

1997 ‐ 2002 System administrator in Lvovsky Clinical and Diagnostic Center (Podolsk 

region). 

1984 ‐1997 Researcher in the Poushchino Branch of Institute of bioorganic chemistry. 


My research interests are focused in areas of Systems Biology and Bioinformatics. General field of my 

work is mathematic modeling of metabolic processes in big integral systems of organism (e.g. 

circulation system). Also, I have an interest in developing of special databases for scientific and applied 

purposes. 


1. Smirnov S. V., Malygin A. G., [Kinetics of DNA‐dependent RNA synthesis: coupled synthesis of di‐ and 

trinucleotides in the presence of a minimum complement of substrate]. Mol Biol (Mosk), 1984, 

18(2):436‐46. 

2. Smirnov S. V., Malygin A. G., [Kinetics of DNA‐dependent RNA synthesis: couple synthesis of di‐, tri‐ 

and tetranucleotides in the presence of a limited set of substrates]. Mol Biol (Mosk), 1985, 19(6):1661‐ 

8. 

3. Smirnov S. V., Sukharev S. A., Dmitrieva E. S., Morgunova L. V., Sadovnikov V. B., Inhibition of 

neutrophil‐mediated lysis of syngeneic erythrocytes by components of blood serum and peritoneal 

fluid. Biomed Sci., 1990, 1(5):481‐6. 

4. Sukharev S. A., Smirnov S. V., Pleshakova O. V., Sadovnikov V. B., [Cytotoxic activity of murine 

peritoneal cavity cells during inflammation in vivo]. Dokl Akad Nauk., 1995, 343(3):403‐5. 

5. Sukharev S. A., Smirnov S. V., Sadovnikov V. B., [Neutrophil heat shock at an inflammation focus]. 

Dokl Akad Nauk., 1994, 335(4):515‐8. 



(X) a poster 

( ) a talk 


Development of databases for Systems Biology 

Abstract 

Two databases concerning enzyme kinetics were developed. 

The first one “EnzBase” is the database for storage of data about enzyme kinetic properties and 

mechanisms. There are two specific features of this database suitable just for recording of kinetic data: 

The list of database fields is open. It allows user to write down in database any kinetic parameters 

found in literature, including unique, rare and newly defined. 

Structure of database allows to record and store digitizing kinetic curves from referenced articles. 

Second database was developed for the purpose to estimate to what an extent the knowledge about 

kinetic properties of enzymes was comprehensive to date. The analysis was performed using the 

information available in Internet databases. It has been shown that relatively complete information in 

regard to a particular enzyme or a set of the enzymes (for instance, for a specific metabolic pathway) 

can be retrieved for very limited amount of organisms. As to the rest organisms, the information is 

available for highly restricted amount of the enzymes. 

Poster number: 38


Dr. Sergej Spirin 

Institution Belozersky Institute of MSU 


Street Address 1 Leninskije Gory, building 40 




Phone +7(495)939‐5414 

Fax +7(495)939‐3181 

E‐Mail sas@belozersky.msu.ru 

Website http://monkey.belozersky.msu.ru/~sas/ 

Short CV 

Education: Moscow State University, Faculty of Mechanics and Mathematics 

Position: Senior Researcher in Belozersky Institute of Physical and Chemical 

Biology, Moscow State University 

Scientific interests: bioinformatics 


Structural bioinformatics, DNA‐protein interaction, algorithms in bioinformatics. 


Ledneva R.K., Alekseevskii A.V., Vasil'ev S.A., Spirin S.A., Kariagina A.S. [Structural aspects of 

homeodomain interactions with DNA] (in <strong>Russian</strong>, 2001) Mol.Biol. (Moscow) 35 (5):764–777 

Alexeevski A., Spirin S., Alexeevski D., Klychnikov O., Ershova A., Titov M., Karyagina A. CluD, a Program 

for Determination of Hydrophobic Clusters in 3D Structures of Protein and Protein‐Nucleic Acids 

Complexes. Biophysics (Moscow), vol. 48, Suppl. 1 (2004), p. 146 

Karyagina A., Ershova A., Spirin S., Alexeevski A. The role of water in homeodomain‐DNA interaction. 

In: Bioinformatics of Genome Regulation and Structure II. (Eds. N.Kolchanov & R. Hofestaedt). NY: 

Springer Sc.+Bus. Media, Inc. 2005, P. 247–257. 

Karyagina A., Ershova A., Titov M., Olovnikov I., Aksianov E., Ryazanova A., Kubareva E., Spirin S., 

Alexeevski A. Analysis of conserved hydrophobic cores in proteins and supramolecular complexes. J. 

Bioinf. and Comp. Biology 2006, 4 (2):357–372 

Sergei Spirin, Mikhail Titov, Anna Karyagina, Andrei Alexeevski. NPIDB, a Database of Nucleic Acids– 

Protein Interactions. Bioinformatics 2007, 23 (23):3247–3248 



(x) a poster 

( ) a talk 


NPIDB, a database of structures of nucleic acid – protein complexes 

Abstract 

The resource NPIDB (Nucleic acids – Protein Interaction DataBase) includes a collection of files in PDB 

format containing structural information on DNA‐protein and RNA‐protein complexes, and a number 

of online tools for analysis of the complexes. Those tools are: an original program CluD for analysis of 

hydrophobic clusters on interfaces, programs for detecting potential hydrogen bonds and potential 

water bridges between protein and nucleic acid, visualization of structures with Jmol and Chime. Pfam 

and SCOP domains presented in protein chains of structures are detected. The information on the 

domain types and their representatives in NPIDB is organized as a set of dynamical web pages. 

Update of the content is done weekly by a special program module. NPIDB is available via Internet: 

The work is supported by RFBR and INTAS. 

Poster number: 39


Dr. Fabian Theis 

Institution <strong>Helmholtz</strong> Zentrum München 


Street Address Ingolstädter Landstraße 1 


City Neuherberg 



Fax +49 89 3187 3585 

E‐Mail fabian.theis@helmholtz‐muenchen.de 

Website http://cmb.helmholtz‐muenchen.de 

Short CV 

‐ Undergraduate and graduate studies of Mathematics and Physics at the Universities of Hagen (1995‐ 

1996), Brandeis (Boston) and Regensburg (‐2000) 

‐ Ph.D. in Biophysics, University of Regensburg (2002) 

‐ Ph.D. in Computer Science, University of Granada (2003) 

‐ Visiting researcher at BSI, RIKEN, Tokyo, FSU, Tallahassee, HUT, Helsinki and TUAT, Tokyo 

‐ Post‐Doc, Institute of Biophysics, University of Regensburg (‐2006) 

‐ Junior Research Group leader, MPI for Dynamics and Self‐Organization (2006‐) 

‐ Group leader “Computational Modeling in Biology”, <strong>Helmholtz</strong>‐Zentrum München (2007‐) 

Awards 

2003 EON Kulturpreis Ostbayern 

2006 Heinz Maier‐Leibnitz Award by the DFG 


We are interested in applying methods from biostatistics and statistical machine learning to the 

analysis of biological problems, ranging from regulatory networks to neural recordings. More precisely, 

we are interested in statistical signal processing using information‐theoretic methods such as blind 

source separation for analyzing large‐scale biomedical data sets. Methods include multivariate 

information‐theoretic data analysis, clustering, independent component analysis and various 

computer simulations. A key application area is Systems Biology, where we are building quantitative 

models in order to explain regulatory gene networks. 


F.J. Theis. Towards a general independent subspace analysis. In Proc. NIPS 2006 

F.J. Theis and G.A. García. On the use of sparse signal decomposition in the analysis of multi‐channel 

surface electromyograms. Signal Processing, 86(3):603‐623, 2006 

F.J. Theis and P. Gruber. On model identifiability in analytic postnonlinear ICA. Neurocomputing, 

64:223‐234, 2005 

P. Georgiev, F.J. Theis, and A. Cichocki. Sparse component analysis and blind source separation of 

underdetermined mixtures. IEEE Transactions on Neural Networks, 16(4):992‐996, 2005 

F.J. Theis. A new concept for separability problems in blind source separation. Neural Computation, 

16:1827‐1850, 2004 



( ) a poster 

(x) a talk 


Exploratory data analysis of biological systems 

Abstract 

Systems biology seeks to integrate different levels of information to understand how biological 

systems function. It begins with the study of genes and proteins using high‐throughput techniques 

such as microarray measurements or mass spectrometry data. Although the experimental methods for 

obtaining such recordings are advanced thus generating large and multivariate data sets, the 

underlying employed statistical tools have not reached this level of sophistication. 

In this talk, we propose to use higher‐order statistics and spatiotemporal clustering methods to extract 

additional information from these large data sets. Extended multi‐dimensional inverse models are 

employed to detect latent variables within the observations, which may then be analyzed using graph‐ 

theoretic techniques. The resulting methods have applications in a wide field ranging from genomics to 

biomedical data analysis in general, telecommunications and financial markets, and their implications 

for genomics, proteomics and metabolomics are yet to be fully understood.


Dr. Alexey Vitreschak 

Institution IITP RAS 


Street Address Bolshoj Karetny pereulok 19 Mosco127994, Russia 




Phone 89859688050 

E‐Mail l_veter@mail.ru 

Short CV 

Comparative genomics 

Gene regulation 

Bacteria 

Regulatory RNAs 

Evolution of regulatory systems 

Riboswitches 

Analysis of gene function 

T‐boxes 

DNA repeats 


Prediction of gene regulation in bacteria. Evolutionary and functional analysis of gene regulatory 

systems (e.g. various riboswitches, T‐boxes and others). Analysis of repeat sequences in genomes. 


.G. Vitreschak, A.A Mironov, V.A. Lyubetsky, M.S. Gelfand. Comparaive genomic analysis of T‐box 

regulatory systems in bacteria. RNA, 2008, (accepted, in print). 

Vitreschak AG, Rodionov DA, Mironov AA, Gelfand MS Riboswitches: the oldest mechanism for the 

regulation of gene expression? Trends in Genetics, 2004 Jan; 20(1):44‐50. 

Vitreschak AG, Rodionov DA, Mironov AA, Gelfand MS. Regulation of the vitamin B(12) metabolism 

and transport in bacteria by a conserved RNA structural element. RNA. 2003 Sep; 9(9):1084‐1097. 

Vitreschak AG, Rodionov DA, Mironov AA, Gelfand MS. Regulation of riboflavin biosynthesis and 

transport genes in bacteria by transcriptional and translational attenuation. Nucleic Acids Research. 

2002. V. 30(14) P. 3141‐51. 

Rodionov DA, Vitreschak AG, Mironov AA, Gelfand MS. Comparative Genomics of Thiamin Biosynthesis 

in Procaryotes. New Genes and Regulatory Mechanisms. J. Biol. Chem. 2002 Dec 13;277(50):48949‐59 



(X ) a poster 

( ) a talk 


Comparative Genomic Analysis of T‐Box Regulatory Systems in Bacteria 

Abstract 

The bacteria use a wide range of regulatory mechanisms to control gene expression. While the most common 

regulatory mechanism seems to be regulation of transcription by DNA‐binding proteins, there are other important 

mechanisms, in particular, regulation of transcription (by premature termination) and translation (by interference 

with initiation) via formation of alternative RNA structures in 3’‐untranslated gene regions. The most frequent 

mechanism of RNA‐dependent regulation of amino acid operons in the Firmicutes seems to be the T‐box regulatory 

system. The T‐box is an RNA structure that is capable of binding uncharged tRNA via an interaction between the 

highly conserved 5'‐UGGN‐3' sequence of the T‐box and the complementary 5'‐NCCA‐3' end of the tRNA. The 

specificity of this binding is defined by base‐pairing of the tRNA anticodon and the so‐called specifier (anti‐anti)‐ 

codon in the T‐box structure. The bound uncharged tRNA stabilizes the antiterminator hairpin, which in turn prevents 

formation of the terminator and allow the gene to be expressed. 

Using a set of known T‐box sites, we constructed the common pattern and used it to scan available bacterial 

genomes. New T‐boxes were found in various Gram‐positive bacteria (mainly Firmicutes, but also Actinobacteria), 

some Proteobacteria (delta‐proteobacteria), and some other bacterial groups (Deinococcales/Thermales, Chloroflexi, 

Dictyoglomi). The majority of T‐box‐regulated genes encode aminoacyl‐tRNA synthetases. Two other groups of T‐box‐ 

regulated genes are amino acid biosynthetic genes and transporters, as well as genes with unknown function. 

Analysis of candidate T‐box sites resulted in new functional annotations for a large number of putative amino acid 

transporters as well as some genes with unknown function. We predict the specificity of amino acid transporters 

analyzing the specifier codons in T‐boxes that regulate genes encoding these transporters and use other methods of 

comparative genomics to obtain additional, independent evidence. 

We then studied the evolution of the T‐boxes. Analysis of the constructed phylogenetic trees and changes in the 

specifier codons demonstrated, that in addition to the normal evolution consistent with the evolution of regulated 

genes, T‐boxes may be duplicated, transferred to other genes, and change specificity. We observed several cases of 

recent expansion of T‐box regulons likely caused by the loss of a previously existing regulatory system, in particular, 

the arginine regulon in Clostridium difficile (loss of the transcriptional repressor AhrC) and methionine regulon in the 

Lactobacillaceae (loss of the S‐box riboswitches). 

In some cases, T‐boxes were arranged in tandem. Predominantly this happened upstream of biosynthetic and 

transport genes. The two major types of such tandems are double T‐boxes, that is, repeats of complete T‐box 

structures, and unusual “partially‐double” T‐boxes, formed by a single specifier hairpin followed by two repeated 

terminator/antiterminator structures closely located to each other. 

Finally, we described a new structural class of T‐boxes regulating initiation of translation in the Actinobacteria. They 

are much shorter and the specifier codon is located in the loop of the hairpin, and not the bulge like in the classic T‐ 

box structure. The T‐box‐containing hairpin may be alternative to a hairpin that masks the Shine‐Dalgarno box and 

thus interferes with the initiation of translation. Such T‐boxes were found upstream of the ileS genes in some 

Actinobacteria (all Actinomycetales and Bifidobacterium longum), whereas the translation‐regulating T‐boxes of the 

classical type were observed only in Thermobifida fusca and the Streptomyces spp. In other actinobacterial species, a 

new variant of the specifier hairpin was found. 

Poster number: 40


Martin von Bergen, PhD 

Institution <strong>Helmholtz</strong>‐Centre for Environmental Researcht 


Street Address Permoser Str‐ 15 


City Leipzig 


Phone +49‐341‐235 1211 

Fax +49‐341‐235 451211 

E‐Mail Martin.vonbergen@ufz.de 

Short CV 

1989‐1995 Studies of Biology, University of Hamburg (D), Dipl. Biol. 

1995‐1998 PhD, University of Hamburg, Faculty of Biology 

Max‐Planck Unit for Structural Molecular Biology, Hamburg 

PhD in Natural Sciences, thesis: "The conformation of the microtubules 

associated protein tau in Alzheimer’s disease" 

1998‐2001 Postdoc, Max‐Planck Unit for Structural Molecular Biology, Hamburg 

2001‐2002 Group Leader Proteomics, Mice and More GmbH and Co KG 

2002‐2006 Senior Scientist, Max‐Planck Unit for Structural Molecular Biology, Hamburg (D) 

Since May 2006 Head of Department of Proteomics, 

<strong>Helmholtz</strong>‐Centre for Environmental Research Leipzig‐Halle (D) 


Beside the general interest in the improvement of proteomic technology we are especially interested 

in the combination of proteome, cytome and genome data for obtaining a systemic view of the cellular 

response to exposition to environmental stressors. A focus lies on the effects of volatile organic 

compounds on cells of the respiratory tract and the immune system. 


• Gündel, U., Benndorf, D., von Bergen, M., Altenburger, R. und Küster, E. Lipovitellins as 

potential biomarkers in developing zebrafish embryos: a proteomics approach. Proteomics 

(2007), in press 

• Bock, K., Benndorf, D., Mueller, A., Herbarth, O.and von Bergen, M.: Allergens from 

Aspergillus versicolor and their usage in diagnosis and therapy of indoor relevant mould 

spore allergies. Patent application to the <strong>German</strong> Patent Agency (2007) 

• Santos PM, Roma V, Benndorf D, von Bergen M, Harms H, Sá‐Correia I.: Mechanistic Insights 

Into the Global Response to Phenol in the Phenol‐biodegrading Strain Pseudomonas sp. M1 

Revealed by Quantitative Proteomics. OMICS 11(3), 233‐51 (2007) 

• von Bergen M, Barghorn S, Muller SA, Pickhardt M, Biernat J, Mandelkow EM, Davies P, 

Aebi U, Mandelkow E.: The core of tau‐paired helical filaments studied by scanning trans‐ 

mission electron microscopy and limited proteolysis. Biochemistry 23;45(20), 6446‐57 

(2006) 

• von Bergen M, Friedhoff P, Biernat J, Heberle J, Mandelkow EM, Mandelkow E.: Assembly of 

tau protein into Alzheimer paired helical filaments depends on a local sequence motif 

((306)VQIVYK(311)) forming beta structure. Proc Natl Acad Sci U S A 9;97(10), 5129‐34 

(2000) 



( ) a poster 

(x) a talk 


From contaminant molecule to the cellular response 

Abstract 

To measure the effects of environmental pollutants on cells up to now only a limited number of 

endpoints were determined. To overcome these limitations we started a project in which the 

fundamental aspects of entrance, distribution and molecular effects will be analysed and combined in 

order to obtain a more complete picture. As a model compound both for environmental pollutants and 

pharmaceutical compounds we chose benzo(a)pyrene. The fluorescence of this compound allows the 

measurement of entrance and distribution within the cells. The experimental data will be used for 

modelling the different stages of the distribution. 

It is further known that benzo(a)pyrene binds to the arylhydrocarbon receptor (AhR) and that the 

binding event induces the locomotion of the receptor from the cytosol into the nucleus, where it acts 

as an transcription factor for proteins involved in the detoxification pathway. 

By genomic and proteomic techniques we will determine the later effects of benzo(a)pyrene and 

combine to obtain a model, that explains the different pathways of the cellular response. These data 

will enable the identification of classifiers that allow a prediction of cellular systems on the basis of 

simplified measurements and this might be applied in the future also to the response towards 

pharmaceutical compounds.


Dr. Jana Wolf 

Institution Humboldt‐University Berlin 


Street Address Invalidenstr. 42 


City Berlin 



Fax +49 30 2093 8813 

E‐Mail j.wolf@biologie.hu‐berlin.de 

Website http://www.biologie.hu‐berlin.de/theorybp/ 

Short CV 

1989‐1994 Study of Biophysics, Humboldt‐University Berlin 

1994‐1995 Biochemical Research at Hoechst AG Frankfurt/M. 

1995‐2001 Scientist in the theoretical biophysics group of R. Heinrich, HU Berlin, 

2000 PhD in Theoretical Biophysics 

1997, 1998 Guest scientist at the Free University Amsterdam, Netherlands 

Nov. 1998 Guest scientist at the National Institute of Bioscience and Human‐Technology, 

Tsukuba, Japan 

2001‐2003 Postdoc at Charité and the Institute for Theoretical Biology, HU Berlin 

2003‐2005 Postdoc at GlaxoSmithKline, Scientific Computing and Mathematical Modelling 

group, Medicines Research Centre, Stevenage, UK 

since 2006 Postdoc in the International Research Training Group 'Genomics and Systems 

Biology of Molecular Networks' Berlin‐Boston‐Kyoto 

from March 2008 Mathematical Modelling Group at the Max‐Delbrueck‐Centre, Berlin 

Awards 

2003 Leopoldina Postdoc Scholarship 


Mammalian signal transduction pathways (NF‐κB, Wnt, EGFR) 

Metabolic networks (energy metabolism) 

Cellular oscillations (circadian rhythm, metabolic oscillations) and synchronisation effects 

Robustness of cellular rhythms 


J. Wolf, S. Dronov, F. Tobin & I. Goryanin (2007), The impact of the regulatory design on the response 

of EGFR‐mediated signal transduction towards oncogenic mutations. FEBS Journal 274 (21), 5505‐ 

5517. 

J. Wolf, S. Becker‐Weimann & R. Heinrich (2005), Analysing the robustness of cellular rhythms, IEE 

Syst. Biol. 2(1), 35‐41. 

S. Becker‐Weimann, J. Wolf, H. Herzel & A. Kramer (2004), Modeling feedback loops of the Mammalian 

circadian oscillator. Biophysical Journal 87, 3023‐3034. 

J. Wolf, J. Passarge, O.J.G. Somsen, J.L. Snoep, R. Heinrich & H.V. Westerhoff (2000), Transduction of 

intracellular and intercellular dynamics in yeast glycolytic oscillations. Biophysical Journal 78, 1145‐ 

1153. 

J. Wolf & R. Heinrich (2000), The effect of cellular interaction on glycolytic oscillations in yeast. A 

theoretical investigation. Biochemical Journal 345, 321‐334. 



( ) a poster 

(x) a talk 


Modelling of signalling networks for the analysis of oncogenic mutations 

Abstract 

Signal transduction pathways play a crucial role in the regulation of various cellular functions, such as 

proliferation, differentiation, survival or the formation of cell‐cell contacts. Using mathematical 

modelling we here analyse two examples, that is EGF‐mediated and Wnt signalling, in order to 

understand the effect of specific oncogenic perturbations. 

The EGFR pathway is deregulated in about 30% of all human cancers, such as malignancies of the 

colon, lung, pancreas, ovary and kidney as well as some leukemias. However, different mutations in 

the EGFR‐mediated pathway are involved in different tumours. We address the question whether this 

may result from the regulation structure which is known to be cell‐type specific. In our attempt we will 

use a detailed computational model of the epidermal growth factor signalling network to investigate 

the effect of receptor over‐expression and Ras mutations as most prominent examples under two 

different feedback regulations. 

As a second example we use a mathematical model of the Wnt signalling pathway to analyse the effect 

of ß‐catenin mutations in hepatocellular carcinoma cells. The mutations, present in 13‐43% of the 

tumours, result in mutated β‐catenin protein that can‘t be phosphorylated and degraded by the 

axin/APC/GSK3ß‐dependent destruction cycle.


Alexey Zakharov 








Fax +7 495 245‐0857 

E‐Mail alexey.zakharov@ibmc.msk.ru 

Website www.ibmc.msk.ru 

Short CV 

2005‐2008 PhD Student of Institute of Biomedical Chemistry of Rus. Acad. Med. Sci. 

1999‐2005 Student of <strong>Russian</strong> State Medical University 


My research interests are quantitative structure‐activity relationships analysis using bio‐ and 

chemoinformatics, in application to search for new anticancer agents. 


CYCLONET—an integrated database on cell cycle regulation and carcinogenesis. F. Kolpakov, V. 

Poroikov, R. Sharipov, Y. Kondrakhin, A. Zakharov, A. Lagunin, L. Milanesi and A. Kel. Nucleic Acids 

Research, 2007, 35, Database issue, D550–D556. 

A new approach to QSAR modelling of acute toxicity. A.A. Lagunin, A.V. Zakharov, D.A. Filimonov and 

V.V. Poroikov. SAR and QSAR in Environmental Research, 2007, 18(3–4), 285–298. 

PASS: indentification of probable targets and mechanisms of toxicity. V.V. Poroikov, D.A. Filimonov, 

A.A. Lagunin, T.A. Gloriozova and A.V. Zakharov. SAR and QSAR in Environmental Research, 2007, 

18(1–2), 101‐110. 

Computer prediction of human carcinogenicity for chemical compounds according to iarc classification. 

A.V. Zakharov, A.A. Lagunin, D.A. Filimonov, V.V. Poroikov, QSAR and Molecular Modelling in Rational 

Design of Bioactive Molecules (Euro QSAR 2004), Turkey, 211‐212. 

Quantitative structure‐activity relationships of cyclin‐dependent kinase 1 inhibitors. A.V. Zakharov, 

A.A. Lagunin, D.A. Filimonov and V.V. Poroikov. Biochemistry (Moscow) Supplemental Series B: 

Biomedical Chemistry. 2007, 1(1), 17–28. 



(X) a poster 

( ) a talk 


QSAR modelling of antineoplastic activities using NIH Roadmap Data. 

Abstract 

A new and extensive database of chemical structures and their biological activities is being developed 

by the National Center for Biotechnology Information at NIH. The database, called PubChem, contains 

both structural information from the scientific literature as well as screening and probe data from the 

Molecular Libraries Screening Center Network. We selected compounds with atineoplastic activities 

from PubChem for QSAR modelling of antineoplastic activities by GUSAR program. We obtained QSAR 

models for each antineoplastic activities and validated them both by leave‐one‐out cross validation 

procedure and vs. external test set. The study is supported by FP6‐grant LSHB‐CT‐2007‐037590 

(Net2Drug). 

Poster number: 41

Venue Details 

Holiday Inn 

Moscow‐Sokolniki 

Rusakovskaya Ulitsa 24 

Moscow, 107014 

<strong>Russian</strong> Federation 

phone: +7‐495‐7867373 

fax: +7‐495‐7867374 

http://www.hi‐sokolniki.ru/ 

Transportation to and from Hotel 

From Sheremetyevo (SVO) Airport 

Distance: 33 KM South East to Hotel 

By car or taxi: 

From the airport drive up to the Leningradskoye 

shosse and turn to the Moscow Centre. Drive up 

to the MKAD (Moscow Ring Road). Take the exit 

to the MKAD East and drive up to the 

Shchelkovskoye shosse. Turn to the Moscow 

Centre and drive via Bol. Cherkizovskaya Street 

up to Rusakovskaya Street. 

By public transport: 

By Bus #Ш851c from Sheremetyevo‐I (07:05 ‐ 

21:05) direction Rechnoy Vokzal metro station. 

Then by underground to the Teatralnaya metro 

station (9 stops), change to the Okhotny Ryad 

metro station and take the train to Sokolniki 

metro station (6 stops). Get off Sokolniki metro 

station. The Hotel is across the street. 

By Sheremetyevo Bus: 

(06:15 ‐ 21:17) to the train station Lobnya. Then 

by express‐train to Savyolovskaya station, 

change for underground and go to 

Mendeleevskaya metro station (1 stop) change 

the line for Novoslobodakay metro station and 

go to Komsomolskaya (2 stops), change for the 

red line, get off at Sokolniki metro station (2 

stops). The Hotel is across the road. 

From Domodedovo Airport (DME) 

• Distance: 54 KM North East to Hotel 

• Taxi Charge (one way): 2610.0 (RUR) 

• Time by taxi: 1,5 hour 

• Train Charge (one way): 300.0 (RUR) 

• Time by train: 40 minutes 

• From airport drive alond M4 Road via 

Kashirskoye shosse up to 3rd Ring. Enter 3rd 

Ring and take the right. Drive straight to the 

turn to Rusakovskaya st. 

Subway: 

• Subway Station Name: Sokolniki 

• Distance: 0.1 KM South to Hotel 

• The hotel is located at only 2 minutes by 

walk just opposite to Sokolniki metro 

station 

Distance to Attractions: 

• Red Square (6 KM ) 

• Bolshoi Theatre (6 KM) 

• Pushkin Museum of Arts (7.5 KM) 

• Christ the Saviour Cathedral (8 KM) 

<strong>Helmholtz</strong> <strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology 77

Helmholtz Russian-German Workshop on Systems Biology Moscow ...

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