11-13 May 2012 Helsingør - Denmark www.networkbio.org

Network 

Medicine 

11-13 May 2012 

Helsingør - Denmark 

www.networkbio.org

Friday 11th may 2012 

09:00 - 15:00 Registration Open 

Session I: KINOME BIOLOGY 

Chair: Blagoy Blagoev - University of Southern Denmark Session 

Programme 

15:00 Welcome by Rune Linding 

15:10 - 16:00 OPENING LETURE: Eng Lim Goh (SGI, USA) - New Developments in Computing for the Life 

Sciences Researcher 

16:00 - 16:30 Andrea Califano (Columbia University, New York NY) - Dissecting and interrogating signaling 

networks in human malignancies 

16:30 - 16:45 Coffee Break and Sponsor Exhibition 

16:45 - 17:15 Rune Linding (DTU, Lyngby DK) - Modeling Cancer Kinome Networks 

17:15 - 17:30 Gianni Cesareni (Tor Vergata, Rome, Italy) - Mapping the human phosphatome on growth 

pathways 

17:30 - 17:50 Sponsored Talk (SBV Improver) - Julia Hoeng – Verification of System Biology Research in 

the age of Collaborative Competition 

17:50 - 18:00 Break 

18:00 - 18:15 Sol Efroni (Bar Ilan University, Israel) - Network-based metrics reveals a novel role for 

hsa-miR-9 and drug control over the p38 network in glioblastoma multiforme progression 

18:15 - 18:45 Garry Nolan (Stanford, San Francisco CA) 

19:00 - Poster Session, Drinks and Welcome Dinner at Hotel 

Saturday 12th may 2012 

Session II: DISEASE NETWORKS 

Chair: Ramneek Gutpa - Technical Univeristy of Denmark Session 

09:10 - 10:00 KEYNOTE LECTURE: Norbert Perrimon (Harvard Medical School, Boston USA) - Building 

and validating signaling networks in Drosophila 

10:00 - 10:30 Dana Pe’er (Columbia,USA) - On the road to personalized therapy, a systems approach 

10:30 - 11:00 Marc Vidal (DFCI, Boston USA) - Interactome Networks and Human Disease 

11:00 - 11:30 Coffe Break and Sponsor Exhibition 

11:30 - 11:45 Stephan M. Feller (Oxford, UK) - How are complex signal computations in cells accomplished 

by multiprotein complexes assembled on ‘intrinsically disordered’ platform proteins? 

The N-terminal folding nucleation (NFN)hypothesis 

12:45 -12:00 Theo Knijnenburg (NCI,Amsterdam, NL) - Drug sensitivity of cancer cell lines explained as a 

logic combination of mutations 

12:00 - 12:30 Søren Brunak (DTU, Lyngby DK) - Interfacing disease phenotypes from electronic patient 

records to the underlying network biology 

12:30 - 13:30 Lunch with Sponsored Talk (Thermo Fisher) - Christian Kelstrup (NNF-CPR, Copenhagen, 

DK) Optimized Fast and Sensitive Acquisition Methods for Shotgun Proteomics on a Quadru 

pole Orbitrap Mass Spectrometer 

Programme 

Session III: NETWORK DRUGS 

Chair: Christopher Workman - Technical University of Denmark 

13:30 - 14:20 KEYNOTE LECTURE: Michael Yaffe (MIT, Cambridge USA) 

14:20 - 14:50 Matt Onsum (Merrimack Pharmaceuticals, USA) - Using systems biology to accelerate onco 

logy drug development 


15:15 - 15:30 Ruth Hüttenhain (ETH Zurich, CH) - A mass spectrometric map for reproducible quantification 

of cancer associated proteins in body fluids 

15:30 - 16:00 Janine Erler (BRIC, Copenhagen DK) - Molecular networks associated with 

cancer progression 

16:00 - 16:30 Nevan Krogan (UCFS, San Francisco USA) - Functional Insights from Protein- 

Protein and Genetic Interaction Maps 

16:30 - 18:30 Poster Session and Sponsor Exhibition 

19:00 Transfer to Louisiana Museum for Gala Dinner 

20:00 Welcome Speech by SGI 

23:00 Buses back to Hotels 

Sunday 13th may 2012 

Session IV: INTEGRATIVE NETWORK BIOLOGY 

Chair: tba 

09:00 -09:50 KEYNOTE LECTURE: Ruedi Aebersold (ETH, Switzerland) - Network Driven Protein Biomar 

ker Discovery and Validation 

09:50 - 10:20 Anne Claude Gavin (EMBL, Heidelberg DE) - Expanding the interaction space; 

protein-metabolite networks 


10:45 - 11:00 Ulrich Stelzl (MPI Molecular Genetics, Berlin, Germany) A Y2H-seq approach to define the 

protein ethyltransferase interactome 

11:00 - 11:30 Marian Walhout (UMASS-MED, Worcester MA) - Gene regulatory networks 

11:30 - 12:00 Ben Lehner (CRG, Barcelona, Spain) - The biology of individuals 

12:00 - 12:30 Bernhard Pallson (DTU/UCSD, Lyngby, DK / San Diego, CA) - Systems Biology of 

Metabolism 

2 / INB 2012 • 11-13 May 2012 www.networkbio.org / 3

ENG LIM GOh, Ph.D. 

SVP & ChIEf TEChNOLOGY OffICER AT SGI 

abStract For SPeakerS 

Dr. Eng Lim Goh joined SGI in 1989, becoming a chief engineer in 1998 and 

then chief technology officer in 2001. He oversees technical computing programs 

with the goal to develop the next generation computer architecture for 

the new many-core era. 

In 2005, InfoWorld named Dr. Goh one of the World’s 25 Most Influential 

CTOs. That same year he was also included in the HPCwire list of “15 People 

to Watch.” In 2007, he was named “Champions 2.0” of the industry by BioIT 

World magazine, and received the HPC Community Recognition Award from 

HPCwire. Dr. Goh is a frequent industry speaker and he continues to discuss, 

in different forums, innovative technologies and their applications. 

Before joining SGI, Dr. Goh worked for Intergraph Systems, Schlumberger Wireline and Shell Research. 

A Shell Cambridge University Scholar, Dr. Goh completed his Ph.D. research and dissertation on parallel 

architectures and computer graphics, and holds a first-class honors degree in mechanical engineering from 

Birmingham University in the U.K. 

Dr. Goh has been granted four U.S. patents, two of which as the inventor and the others as co-inventor. 

New Developments in Computing for the Life Sciences Researcher 


ANDREA CALIfANO 

COLUMBIA UNIVERSITY, NEW YORK NY 

Dissecting and interrogating signaling networks in human malignacies 

We will discuss novel experimentally validated computational approaches 

to the dissection and interrogation of signal transduction networks in human 

malignancies. Specifically, we will address the issue of signaling pathway analysis 

from gene expression and phospho-proteomic profile data. We will first 

present the identification of KLHL9 deletions as key events in the etiology of 

the mesenchymal subtype of high-grade, associated with worst prognosis. We 

show that KLHL9, a substrate-specific adapter of a Cul3-based E3 ubiquitinprotein 

ligase complex, is responsible for the ubiquitination and proteasomal 

degradation of two previously identified master regulators of this glioma 

subtype, C/EBPb and C/EBPd. We will also discuss the identification of synergistic synthetic lethality in the 

tyrosine kinase signaling network that is dysregulated in non-small cell lung tumors, leading to a potential 

application of personalized combination therapy, using available kinase inhibitors. 


RUNE LINDING 

DTU, LYNGBY - DENMARK 


Dr Rune Linding is Professor and Research Group Leader for the Cellular Signal 

Integration Group (C-SIG) at the Technical University of Denmark (DTU), 

Center for Biological Sequence Analysis (CBS), Department of Systems Biology, 

Denmark. He performed his graduate work at the EMBL (Germany), where 

he pioneered computational analysis of cell signaling by developing popular 

tools like ELM, GlobPlot and DisEMBL for analysing post-translational modifications, 

intrinsic protein disorder and modularity of signaling proteins. Dr 

Linding was Human Frontiers Science Program Postdoctoral Research Fellow 

jointly with Profs Tony Pawson and Mike Yaffe at Samuel Lunenfeld Research 

Institute (SLRI, Canada) and Massachusetts Institute of Technology (MIT, 

USA), respectively. His postdoctoral work on the cellular phosphorylation networks and development of the 

NetworKIN algorithm pioneered Integrative Network Biology and led to the discovery of the quantitative 

importance of contextual kinase specificity. He started his own lab (the Cellular & Molecular Logic Team) 

at The Institute of Cancer Research (ICR) in London in 2007. At ICR his lab unravelled systems-level models 

of JNK and EphR kinase networks, demonstrated a link between specificity and oncogenecity of kinases 

and introduced the concept of Network Medicine. Dr Linding leads the NetPhorest community resource 

and have pioneered comparative phospho-proteomics and evolutionary studies of signalling networks. Dr 

Linding founded the Integrative Network Biology initiative (INBi) which aims to block cancer metastasis by 

integration of large-scale, high-dimensional quantitative genomic, proteomic and phenotypic data. His lab 

moved to DTU/Denmark in 2011 and the long-term focus of his research group is on studying cellular signal 

processing and decision making. 

Modeling of Cancer Kinome Networks 

Abstract: Biological systems are composed of highly dynamic and interconnected molecular networks that 

drive biological decision processes. A goal of integrative network biology is to describe, quantify and predict 

the information flow and functional behaviour of living systems in a formal language and with an accuracy 

that parallels our characterisation of other physical systems such as Jumbo-jets. Decades of targeted 

molecular and biological studies have led to numerous pathway models of developmental and disease 

related processes. However, so far no global models have been derived from pathways, capable of predicting 

cellular trajectories in time, space or disease. The development of high-throughput methodologies has 

further enhanced our ability to obtain quantitative genomic, proteomic and phenotypic readouts for many 

genes/proteins simultaneously. Here, I will discuss how it is now possible to derive network models through 

computational integration of systematic, large-scale, high-dimensional quantitative data sets. I will review 

our latest advances in methods for exploring phosphorylation networks. In particular I will discuss how the 

combination of quantitative mass-spectrometry, systems-genetics and computational algorithms (NetworKIN 

[1] and NetPhorest [4]) made it possible for us to derive systems-level models of JNK and EphR signalling 

networks [2,3]. I shall discuss work we have done in comparative phospho-proteomics and network 

evolution[5-7]. Finally, I will discuss our most recent work in analysing genomic sequencing data from NGS 

studies and how we have developed new powerful algorithms to predict the impact of disease mutations on 

cellular signaling networks and applied them to profiling of ovarian cancer cells. 

References: 

http://www.lindinglab.org 

Linding et al., Cell 2007. 

Bakal et al., Science 2008. 

Jørgensen et al., Science 2009. 

Miller et al., Science Signaling 2008. 

Tan et al., Science Signaling 2009. 


GIANNI CESARENI 

Mapping the human phosphatome on growth pathways 

fRANCESCA SACCO 1 

, PIER fEDERICO GhERARDINI 1 

, SERENA PAOLUzI 1 

, JULIO SAEz-RODRIGUEz 3 

, 

MANUELA hELMER-CITTERICh 1 

, ANTONELLA RAGNINI-WILSON 1,2 

, LUISA CASTAGNOLI 1 

AND GIANNI 

6 / INB 2012 • 11-13 May 2012 www.networkbio.org / 7 

CESARENI 1 

1 

DEPARTMENT Of BIOLOGY, UNIVERSITY Of ROME “TOR VERGATA”, ITALY 2hIGh-ThROUGhPUT MICROS- 

COPY fACILITY; DEPARTMENT Of TRANSLATIONAL AND CELLULARPhARMACOLOGY, CONSORzIO MARIO 

NEGRI SUD, SM. IMBARO, ITALY 3EMBL-EBI, hINxTON, UK AND EMBL-GENOME BIOLOGY UNIT, hEIDEL- 

BERG, GERMANY 

Large-scale siRNA screenings allow linking the function of poorly characterized genes to phenotypic 

readouts. According to this strategy, genes are associated to a function of interest if the alteration of their 

expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, the 

mapping procedure is low resolution and the resulting models provide little mechanistic insights. We have 

developed a new strategy that combines high-content multiparametric analysis of cell perturbation and logic 

modeling to achieve a more detailed functional mapping of human genes onto complex pathways. By this

JULIA hOENG 

Verification of Systems Biology Research in the Age of Collaborative-Competition 


JULIA hOENG1, MARJA TALIKKA1, STéPhANIE BOUé1, PABLO MEYER ROJAS2, RAqUEL NOREL2, JOhN J 

RICE2, JöRG SPRENGEL3, MANUEL PEITSCh1, GUSTAVO STOLOVITzKY2 

1PhILIP MORRIS INTERNATIONAL R&D, NEUChâTEL, SWITzERLAND, 2IBM COMPUTATIONAL BIOLOGY 

CENTER, YORKTOWN hEIGhTS, NY, USA, 3IBM GLOBAL BUSINESS SERVICES, SWITzERLAND 

Abstract: 

Modern society demands greater scrutiny of the potential health risks and benefits of long-term, and sometimes 

lifelong, exposure to drugs, chemicals, and substances found in consumer products and the environment. 

Organizations such as companies and academic consortia conduct large multi-year scientific studies that 

entail the collection and analysis of thousands of data points. The individual experiments are often conducted 

over many physical sites and with internal and outsourced components. To extract maximum value, 

the interested parties need to verify the accuracy and reproducibility of automated collection and analysis 

workflows in systems biology before the initiation of large multi-year studies. 

Traditional verification using the peer-review process has shortcomings, such as lack of scalability, which 

renders it insufficient for the assessment of high throughout research. A team of researchers at PMI and 

IBM, whose aim is to improve the effectiveness of scientific studies and verification of scientific findings 

propose a scheme called IMPROVER, for Industrial Methodology for Process Verification of Research. This 

methodology evaluates a research program by dividing its workflow into smaller building blocks, whereby the 

verification of each building block can be done internally or externally via challenge-based `crowdsourcing` 

to a research community. 

Scientific challenges will be broadcast to potential stakeholders in the form of an open call for participation 

with the intention of providing the community with the opportunity to test their computational methods on 

new data as well as to partake in a collaborative effort whose ultimate goal could contribute to solving a 

grand scientific problem. 

Considering cancer as the leading cause of death worldwide, we formulate the Diagnostics Signature Challenge 

to evaluate novel approaches for the identification of robust and predictive signatures for this disease. 

The goal of a Diagnostics Signature Challenge is to verify that transcriptomics data contains enough 

information for the determination and prognosis of certain human disease states that could profit from better 

diagnostics signatures. 

Here we will describe the approach, the necessary operational steps, and how we intend to engage the 

wider scientific community to assess the applicability of the IMPROVER approach to molecular diagnostics 

(i.e., genomic signatures). 


ROTEM BEN-hAMO, SOL EfRONI 

ThE MINA & EVERARD GOODMAN fACULTY Of LIfE SCIENCES, BAR ILAN UNIVERSITY, RAMAT GAN, 

ISRAEL 

Network-based metrics reveals a novel role for hsa-miR-9 and drug control over the p38 network in glioblastoma 

multiforme progression 

Contributors: 

Background 

Glioblastoma multiforme (GBM) is the most common, aggressive and malignant primary tumor of the brain 

and is associated with one of the worst 5-year survival rates among all human cancers. Identification of 

molecular interactions that associate with disease progression may be key in finding novel treatments. 

Using five independent molecular and clinical datasets with a set of computational algorithms we were able 

to identify a gene-gene and gene-microRNA network that significantly stratifies patient prognosis. By combining 

gene expression microarray data with microRNA expression levels, copy number alterations, drug 

response and clinical data, combined with network knowledge, we were able to identify a single pathway at 

the core of glioblastoma. 

This network, the p38 network, and an associated microRNA, hsa-miR-9, facilitate prognostic stratification. 

The microRNA hsa-miR-9 correlated with network behavior and presents binding affinities with network 

members in a manner that suggests control over network behavior. A similar control over network behavior 

is possible through a set of drugs. These drugs are part of the treatment regimen for a subpopulation of the 

patients that participated in the TCGA study and for which the study provides clinical information. Interestingly, 

the patients that were treated with these specific sets of drugs, all of which targeted against p38 

network members, demonstrate highly significant stratification of prognosis. 

Combined, these results call for attention to p38 network targeted treatment and present the p38 networkhsa-miR-9 

control mechanism as critical in GBM progression. 


GARRY NOLAN 

STANfORD, STANfORD, CA 

TBA 



NORBERT PERRIMON 

hARVARD MEDICAL SChOOL, BOSTON, MA 

Dr. Perrimon is the James Stillman Professor of Developmental Biology at 

Harvard medical School and an Investigator of the Howard Hughes Medical 

Institute. He has 30 years of experience in the fields of developmental 

genetics, signal transduction and genomics. He has made a number of contributions 

to the fields of Genetics, Developmental Biology, signal transduction 

and functional genomics. His group developed many methods that have 

significantly improved the Drosophila toolbox. These include: the FLP-FRT 

Dominant Female Sterile technique to generate mosaics in the female germline, 

the Gal4-UAS method to control gene expression both spatially and 

temporally; the “Positively Marked Labeling Method” for lineage analyses; 

and thermosensitive inteins to generate conditional alleles. His contributions 

include the characterization of: the maternal effects of zygotic lethal mutations; the logic of head patterning; 

the identification of Scribble and the organization of the cell polarity complexes; the discovery of adult 

gut stem cells; and the mechanisms of muscle growth and aging. His lab has characterized many signaling 

components of receptor tyrosine kinases, Wnt and JAK/STAT pathways, in particular. These include: Raf 

kinase and demonstration that it acts downstream of Ras; Corkscrew/SHP2 non receptor tyrosine phosphatase 

as a positive transducer of RTK signaling; Spitz as a ligand, and Kekkon as a negative regulator, of 

EGFR; Porcupine, Dishevelled and GSK3 as components of Wnt/Wg signaling; Unpaired, Hopscotch/JAK and 

Marelle/STAT as members of the JAK/STAT pathway; and Heparan Sulfate Proteglycans in Hedgehog, Wnt 

and FGF signaling. Regarding large scale functional genomics, his group established high-throughout genome-wide 

RNAi screens to systematically interrogate the entire Drosophila genome in various cell-based 

assays, demonstrated that long dsRNAs are associated with off target effects, established a cross-species 

method for rescue of RNAi phenotypes, developed RNAi methods in primary embryonic cell cultures, and 

generated algorithms for automated image analyses. In 2003 he created the Drosophila RNAi Screening 

Center (DRSC) at Harvard Medical School to make this technology available to the community. In addition, 

his group developed new shRNA vectors for in vivo RNAi and in 2008 established the Transgenic RNAi Project 

(TRiP) at Harvard Medical School to build a genome scale resource of transgenic shRNA flies. Currently, 

his laboratory is applying large-scale RNAi and proteomic methods to obtain a global understanding of the 

structure of a number of signaling pathways and their cross-talks. In addition, he is studying the roles of 

signaling pathways in homeostasis and tissue remodeling in Drosophila muscles and gut stem cells. Dr. 

Perrimon has trained more than 80 students and postdoctoral fellows, with most of them currently holding 

academic positions. 

Building and validating signaling networks in Drosophila 

Characterizing the extent and logic of signaling networks is essential to understanding developmental 

processes, mechanisms of oncogenesis, and resistance to chemotherapy. A major focus of our lab is to 

apply “Omics” approaches to describe the organization of signaling networks. We have spent considerable 

effort to implement complementary technologies of genome-wide RNAi HTS, tandem affinity purification/ 

mass spectrometry (TAP/MS), and transcriptome analyses in Drosophila cell lines, to identify core pathway 

components, pathway dynamics, and the extent of cross-talk between pathways. Recently, because studying 

networks in the fly is hampered by the paucity of phosphoantibodies available, we have used the Tandem 

Mass Tags Mass Spec method to measure quantitative changes in the phosphoproteome. Integration of 

various Omic approaches is necessary for network studies because much of the noise associated with each 

technology can be filtered by the integration of orthogonal data sets. 

We have applied these approaches successfully to date to the study of five pathways in established Drosophila 

cell lines: Insulin/PI3K, EGF/MAPK, JAK/STAT, JNK and Hippo and have generated high-confidence 

protein-protein interaction (PPI) networks that have been validated to various extents by RNAi and transcriptome 

analyses. Our networks are of high quality because they identify most previously known interactions, 

and by various means we have validated a number of new components and interactions. 

10 / INB 2012 • 11-13 May 2012 www.networkbio.org / 11

DANA PE’ER 

COLUMBIA UNIVERSITY, NEW YORK, NY 

Dana Pe’er is assistant professor at the Department of Biological Sciences at 

Columbia and Columbia’s Center for Computational Biology and Bioinformatics 

(C2B2). In her Ph.D. (Computer Science) at the Hebrew University of Jerusalem 

(with Nir Friedman), Dana pioneered the use of machine learning to uncover 

the structure and function of molecular networks from genomics data, 

based on Bayesian networks. She subsequently did a postdoc with George 

Church at Harvard Medical School and there she began to work towards understanding 

of how genetic variation alters the regulatory network between 

individuals and subsequently manifests in phenotypic diversity. This is now 

the focus of Dana’s lab at Columbia University, where she and her team are 

developing methods to infer how variation in sequence modulates signal processing and is manifested in 

cellular phenotypes, with applications towards personalized cancer treatment. Dana is recipient of the Burroughs 

Wellcome Fund Career Award, NIH Directors New Innovator Award, Stand Up To Cancer Innovative 

Research Grant and a Packard Fellow in Science and Engineering. 

On the road to personalized therapy, a systems approach 

Cancer is an individual disease—unique in how it develops and behaves in every patient. The emergence 

of revolutionary technologies has stimulated hope that treatment will improve by becoming more targeted 

and individualized in nature. Characterization of cancer genomes has revealed a staggering complexity of 

aberrations among individuals, such that the functional importance and physiological impact of most tumor 

genetic alterations remains poorly defined. Genomic and proteomic data in tumor samples, using a battery 

of using high-throughput, massively parallel technologies is accumulating at a astounding rates. A major 

challenge involves the development of analysis methods to integrate this data towards patient-specific tumor 

network models. We demonstrate progress on a number of fronts. 

• We demonstrate approaches to integrate heterogeneous genomic data types to identify the key 

alterations functionally driving the cancer and associate these with their tumorigenic phenotypes (e.g. 

proliferation, invasion). 

• To understand tumor response to drug and the heterogeneity of this response among patients it is 

critical to molecularly interrogate the tumor following drug perturbations. We will demonstrate how such 

interrogation of a tumor panel reveals variation in network wiring that connects to drug response. 

• Tumors are not only heterogeneous between patients, but there is also pervasive heterogeneity within 

a single patient. Mass-cytometry, a novel technology that can measure more than forty signaling mole- 

abStract For SPeakerS abStract For SPeakerS 

MARC VIDAL 

CENTER fOR CANCER SYSTEMS BIOLOGY (CCSB) AND DEPARTMENT Of 

CANCER BIOLOGY DANA-fARBER CANCER INSTITUTE & DEPARTMENT Of 

GENETICS hARVARD MEDICAL SChOOL BOSTON, MA 02115 

Marc Vidal is Professor of Genetics at Harvard Medical School and Director 

of the Center for Cancer Systems Biology (CCSB) at the Dana-Farber Cancer 

Institute. Dr. Vidal received his PhD in 1991 from Gembloux University (Belgium) 

for work performed at Northwestern University (Evanston, IL, USA) 

where he identified the yeast genes RPD3 and SIN3, and demonstrated that 

they encode global transcriptional regulators. These genes were subsequently 

found to encode Histone Deacetylase (HDAC) and its main recruiting 

factor, respectively. During postdoctoral training at the Massachusetts General Hospital Cancer Center, he 

developed the reverse two-hybrid system, a widely applicable method used to genetically characterize protein-protein 

interactions. Having developed interdisciplinary strategies together with collaborators from 

the fields of physics, computer science, mathematics, genomics and human genetics, he and his team have 

been charting protein-protein and other interactome networks for 15 years and are developing ways to integrate 

interactome maps with other large-scale functional genomic and proteomic maps, with the ultimate 

objective to discover novel network properties from a systems point-of-view. Dr. Vidal was elected Associate 

Member of the Royal Academy for Science and the Arts of Belgium and has received several awards, 

including a Chair from the Francqui Foundation (Belgium) and an Abbott Bioresearch Award. 

Interactome Networks and Human Disease 

For over half a century it has been conjectured that macromolecules form complex networks of functionally 

interacting components, and that the molecular mechanisms underlying most biological processes correspond 

to particular steady states adopted by such cellular networks. However, until a decade ago, systemslevel 

theoretical conjectures remained largely unappreciated, mainly because of lack of supporting experimental 

data. 

To generate the information necessary to eventually address how complex cellular networks relate to biology, 

we initiated, at the scale of the whole proteome, an integrated approach for modeling protein-protein 

interaction or “interactome” networks. Our main questions are: How are interactome networks organized at 

the scale of the whole cell? How can we uncover local and global features underlying this organization, and 

how are interactome networks modified in human disease, such as cancer? 

12 / INB 2012 • 11-13 May 2012 www.networkbio.org / 13

STEPhAN M. fELLER, WIMM, 

OxfORD UNIVERSITY, OxfORD, UK 


How are complex signal computations in cells accomplished by multi-protein complexes assembled on 

‘intrinsically disordered’ platform proteins? The N terminal folding nucleation (NFN) hypothesis 

Proteins with little recognisable secondary structure -according to current prediction programs -comprise 

one third of the human proteome. Some of these are large and serve as docking platforms for many proteins 

involved in the processing of cell signals, thereby creating large signalosomes in which signal computations 

are performed. It was always difficult to conceptualise how an intrinsically disordered, i.e. ‘chaotic’ signal 

computation platform protein should be able to mediate effective pathway crosstalk in cells. However, we 

have now first evidence that the cancer-relevant Gab family proteins display a previously unrecognised 

organisation (Simister et al. 2011, PLoS Biol; Simister & Feller 2012, Mol BioSystems). Gab proteins contain 

N terminal PH domains followed by long ‘tail regions’ supposedly lacking any structure. Our data point to 

several specific interactions of the Gab1 tail with is structured N-terminus. These generate, around the PH 

domain, tail loop structures, in which docking sites for pathway-specific signaling proteins are clustered. 

Functionally dedicated, distinct sub-complexes can assembly in these loops. If this model is correct, it is 

easy to see how effective signaling pathway crosstalk occurs: simply by interactions of distinct sub-complexes 

bound to specialised loops, which are held in place by their interactions with the PH domain. We believe 

that proving this hypothesis, which is also relevant to multiple similarly structured signaling protein families 

(p130Cas, IRS/Dok, Frs etc.), will have a profound impact on the understanding of molecular signal computing 

in cells and we will report on our first results. 


ThEO KNIJNENBURG 

NEThERLANDS CANCER INSTITUTE, DIVISION Of MOLECULAR CARCINOGENESIS 

PLESMANLAAN 121, 1066Cx AMSTERDAM, ThE NEThERLANDS 

MAThEW J. GARNETT 

CANCER GENOME PROJECT , WELLCOME TRUST SANGER INSTITUTE 

hINxTON CAMBRIDGE CB10 1SA, UNITED KINGDOM 

GUNNAR W. KLAU 

LIfE SCIENCES GROUP, CENTRUM WISKUNDE & INfORMATICA 

SCIENCE PARK 123, 1098 xG AMSTERDAM, ThE NEThERLANDS 

fRANCESCO IORIO 

EUROPEAN BIOINfORMATICS INSTITUTE, WELLCOME TRUST GENOME CAMPUS 

hINxTON CAMBRIDGE CB10 1SD, UNITED KINGDOM 

JULIO SAEz-RODRIGUEz 

EUROPEAN BIOINfORMATICS INSTITUTE, WELLCOME TRUST GENOME CAMPUS 

hINxTON CAMBRIDGE CB10 1SD, UNITED KINGDOM 

ULTAN MCDERMOTT 

CANCER GENOME PROJECT , WELLCOME TRUST SANGER INSTITUTE 

hINxTON CAMBRIDGE CB10 1SA, UNITED KINGDOM 

LODEWYK WESSELS 

NEThERLANDS CANCER INSTITUTE, DIVISION Of MOLECULAR CARCINOGENESIS 

PLESMANLAAN 121, 1066Cx AMSTERDAM, ThE NEThERLANDS 

Drug sensitivity of cancer cell lines explained as a logic combination of mutations 

Cancer arises as a result of the acquisition of DNA mutations. It is still unclear which and how combinations 

of mutations are involved in tumor initiation and development. Two major challenges need to be addressed in 

order to systematically unravel these genetic interactions. 

First, large amounts of high quality data are necessary in order to ensure the statistical power required 

to uncover these genotype-to-phenotype relationships. Current advances in high-throughput biology are 

enabling the generation of very large datasets that should facilitate the detection of higher order genetic 

interactions. Here, we report on the analysis of a panel of 1000 cancer cell lines. The mutation status of 66 

known cancer genes has been characterized for each cell line in this panel. Additionally, these cell lines 

have been screened to model drug response with a large number (250+) of anti-cancer therapeutics. This 

dataset is part of the Cancer Genome Project at the Wellcome Trust Sanger Institute. 

The second challenge is to design a computational framework that employs a mathematical formalism rich 

enough to capture the underlying biological complexity, while limiting the computational complexity that 

would otherwise prohibit finding optimal (or even good) solutions in the vast combinatorial search space. 

We contribute to addressing this second challenge by proposing a novel computational approach based on 

integer programming that infers logic combinations of mutations that predict the observed drug response. 

The use of a logic formalism enables the formulation of intelligible models, from which the cancer biologists 

can generate a deeper understanding based on domain knowledge and easily formulate novel hypotheses 

and experiments. 

Our models show that for most drugs, combinations of mutations explain the drug response better than 

single mutations. For example, of the 8 BRAF inhibitors in the panel, the drug sensitivity to 3 of them is better 

explained using a logic combination of a BRAF mutation and one or more mutations in other genes, such 

as TET2. These results immediately suggest putative drug combination therapies. 

Additionally, cancer signaling pathways as annotated in e.g. the Pathway Interaction Database can be 

employed to dramatically reduce the search space and offer a mechanistic explanation of the uncovered 

gene combinations. 



SøREN BRUNAK, 

TEChNICAL UNIVERSITY Of DENMARK & UNIVERSITY Of COPENhAGEN 

Søren Brunak, Ph.D., is professor of Bioinformatics at the Technical University 

of Denmark and professor of Disease Systems Biology at the University of Copenhagen. 

Prof. Brunak is the founding Director of the enter for Biological Sequence 

Analysis, which was formed in 1993 as a multi-disciplinary research 

group of molecular biologists, biochemists, medical doctors, physicists, and 

computer scientists. Søren Brunak has been highly active within data integration, 

where machine learning techniques often have been used to integrate 

predicted or experimentally established functional genome and proteome annotation. 

His current research does combine molecular level systems biology 

and healthcare sector data such as electronic patient records and biobank 

questionnaires. The aim is to group and stratify patients not only from their genotype, but also phenotypically 

based on the clinical descriptions in the medical records. 

Interfacing disease phenotypes from electronic patient records to the underlying network biology 

Interfacing sequencing and network biology data to personal healthcare sector information World-wide the 

healthcare sector is confronted with the availability of database information which describe the individual 

in great detail. These data range all the way from the molecular level, where they for example reveal the 

genetic makup of the patient, to the fine-grained descriptions of disease phenotypes as they are found in 

electronic patient records at hospitals. Linking these data is a huge undertaking which soon will represent a 

major challenge given that it already has become feasible to sequence the DNA of entire populations at low 

cost. Combining molecular level data with clinical information and data on the chemical environment may 

add complementary types of knowledge which - together with genotype and metagenomic information from 

the individual - can reveal disease mechanisms in novel ways. Electronic patient records remain a rather 

unexplored, but potentially rich data source for example for discovering correlations between diseases. We 

describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic 

and non-cohort dependent manner. By extracting phenotype information from the free-text in such 

records we demonstrate that we can extend the information contained in the structured record data, and use 

it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses 

a dictionary based on the WHO International classification of Disease ontology and is therefore in principle 

language independent. 

References 

Using electronic patient records to discover disease correlations and stratify patient cohorts. Roque FS, 

Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Søeby K, Bredkjær S, Juul A, Werge T, Jensen 

LJ, Brunak S. PLoS Comput Biol. 2011 Aug;7(8):e1002141. 

Knowledge engineering for health: A new discipline required to bridge the “ICT gap” between research and 

healthcare. Beck T, Gollapudi S, Brunak S, Graf N, Lemke HU, Dash D, Buchan I, Díaz C, Sanz F, Brookes 

AJ. Hum Mutat. 2012 Mar 5. doi: 10.1002/humu.22066 

Mining electronic health records: towards better research applications and clinical care Jensen PB, Jensen 

LJ, and Brunak S, Nature Reviews Genetics, June 2012, to appear. 


ChRISTIAN KELSTRUP 

NNf-CPR, COPENhAGEN, DENMARK 

Optimized Fast and Sensitive Acquisition Methods for Shotgun Proteomics on a Quadrupole Orbitrap Mass 

Spectrometer 


SPonSorPage 


MIChAEL YAffE 

MIT, CAMBRIDGE, MA 



MATThEW ONSUM 

Ph.D., MERRIMACK PhARMACEUTICALS, CAMBRIDGE, MA 

Dr. Onsum is an Associate Director of Translational Research at Merrimack 

Pharmaceuticals. He received his B.S., M.S., and Ph.D. degrees in Mechanical 

Engineering from the University of California, Berkeley. His doctoral work, 

under the supervision of Adam Arkin and Kameshwar Poolla, used both computational 

and wet biology to study how immune cells track and capture invading 

microbes. Additionally, he was a member of the Alliance for Cellular 

Signaling where he developed mathematical models of GPCR mediated calcium 

signaling and model validation software. He spent two years at Astra- 

Zeneca R&D Boston, where he used model simulations to help identify new 

drug targets. He is currently at Merrimack Pharmaceuticals where he is leading 

the translational research program for MM-111, a bi-specific antibody against ErbB3 that uses an ErbB2 

targeting arm to enhance avidity and inhibitor potency. 

Using systems biology to accelerate oncology drug development 

This session will discuss how Merrimack uses mathematical models of cancer signaling pathways to design 

novel therapeutics, identify predictive biomarkers, and guide clinical development plans. By combining the 

knowledge gained from our biochemical model together with biomarker measurements from a large panel 

of archived tumors and clinical data from the literature, we simulated the effect of our lead oncology drug in 

a variety of cancer indications and used these simulations to help prioritize our clinical development plans. 

We will also discuss how we use our mathematical models to assess other targeted oncology drugs and 

determine which of these drugs should be combined with our therapies. 


RUTh hüTTENhAIN 1 

, MARTIN SOSTE 2 

, NAThALIE SELEVSEK 1 

, hANNES RöST 1 

, ATUL SEThI 1 

, ChRISTINE 

CARAPITO 3 

, TERRY fARRAh 4 

, ERIC W. DEUTSCh 4 

, ULRIKE KUSEBAUCh 4 

, ROBERT L. MORITz 4 

, EMMA 

NIMèUS-MALMSTRöM 5 

, OLIVER RINNER 6 

AND RUEDI AEBERSOLD 1,7 

20 / INB 2012 • 11-13 May 2012 www.networkbio.org / 21 

1 

DEPARTMENT Of BIOLOGY, INSTITUTE Of MOLECULAR SYSTEMS BIOLOGY, ETh zURICh, zURICh, SWITzERLAND 

2 

DEPARTMENT Of BIOLOGY, INSTITUTE Of BIOChEMISTRY, ETh zURICh, zURICh, SWITzER- 

LAND 3 

LABORATOIRE DE SPECTROMéTRIE DE MASSE BIO-ORGANIqUE, INSTITUT PLURIDISCIPLINAIRE 

hUBERT CURIEN, UMR7178 CNRS-UNIVERSITé DE STRASBOURG, STRASBOURG, fRANCE 4 

INSTITUTE 

fOR SYSTEMS BIOLOGY, SEATTLE, WA, USA 5 

DEPARTMENT Of ONCOLOGY, LUND UNIVERSITY, LUND, SWE- 

DEN 6 

BIOGNOSYS AG, zURICh, SWITzERLAND 7 

fACULTY Of SCIENCE, UNIVERSITY Of zURICh, zURICh, 

SWITzERLAND 

A mass spectrometric map for reproducible quantification of cancer associated proteins in body fluids 

A major bottleneck in applying targeted proteomic approaches to protein biomarker research is the limited 

availability of accurate, reproducible and sensitive assays for testing hypotheses on cohorts of patient 

samples. Therefore, we aimed to generate a high-quality resource of selected reaction monitoring (SRM) 

assays for cancer associated proteins (CAPs), from an evidence-based list of 1172 proteins, that have been 

previously documented to be differentially expressed in various types of cancer1. Using a protein functional 

network, we demonstrated that these proteins are enriched among the interaction partners of genes mutated 

in cancer. 

Following the development of the SRM assays we examined their detectability in two types of samples which 

are highly relevant for biomarker studies, plasma and urine. The concentrations of the detected CAPs in 

plasma span six orders of magnitude demonstrating the high sensitivity of these assays for protein quantification. 

The developed SRM assays and detectability information are publicly available via PeptideAtlas 

SRM Experiment Library (PASSEL)2 to enable researchers to test hypotheses related to these CAPs in any 

sample of interest. 

We demonstrated the utility of this resource for biomarker research by measuring CAPs from the FDA-approved 

OVA1 biomarker panel across plasma samples collected from women diagnosed with a pelvic mass. 

The measurements were able to recapitulate the expected capability of this panel to stratify ovarian cancer 

(OC) patients and patients with benign ovarian tumors (BOT). Moreover, using this resource, we explored 

the prospect of discovering novel biomarker candidates by in silico prediction. Using a functional protein network, 

we derived a set of 21 CAPs, that interact with genes mutated in OC, and are measurable in plasma. 

12 of these network derived proteins showed a significant difference in abundance between patients with OC 

and patients with BOT. 

In sum, by developing a publicly accessible resource of SRM assays and testing their detectability in body 

fluids, this study will facilitate applying targeted proteomics to protein biomarker research. Furthermore, we 

explored the promise of combining genomic data and protein network analysis for predicting novel biomarker 

candidates. We demonstrated that this resource can be used to rapidly test these hypotheses in body fluids. 

Polanski, M. & Anderson, N.L. A list of candidate cancer biomarkers for targeted proteomics. Biomarker 

Insights 1, 1-48 (2007). 

Farrah, T. et al. PASSEL: The PeptideAtlas SRM Experiment Library. Proteomics, 10.1002/pmic.201100515 

(2012).

DR JANINE T ERLER, 

BRIC, UNIVERSITY Of COPENhAGEN 

COPENhAGEN, DENMARK 


Elucidating the molecular networks associated with metastasis 

Metastasis is responsible for over 90% of cancer patient deaths due to a 

lack of effective therapies against metastatic disease. We require a better 

understanding of the underlying molecular processes in order to identify and 

develop novel effective therapeutic strategies. We aim to identify the signaling 

networks associated with metastasis through several different approaches. 

Our goal is to identify key nodes in the network and target these to prevent 

disease progression, and translate these findings into the clinic. 

One approach we are taking is to identify and compare the signaling networks 

and associated genetic mutations in metastatic versus non-metastatic samples derived from patients. In 

our model, we have used non-metastatic and metastatic matched human cancer cell lines isolated from the 

same patient at different stages. We have performed quantitative mass spectrometry to compare the global 

proteome and phosphoproteome, and next generation sequencing to both identify and compare mutations, 

and investigate their impact on the signaling networks. We are deploying the NetPhorest and NetworKIN 

algorithms to predict phospho-binding modules likely to interact with the identified sites. Additionally, computational 

integration of the molecular data with quantitative phenotypic data acquired from high throughput 

invasion assays allows us to build predictive models of cancer progression. We are using RNAi to perturb 

these networks and refine our models to identify the core network predicted to drive metastasis. We will additionally 

integrate data collected from fresh frozen human patient samples. Finally, we will test our predictions 

using an in vivo model of metastasis. 

The results of this study will aid the development of a network-based therapeutic strategy for the treatment 

and prevention of metastatic disease. 


NEVAN KROGAN 

CELLULAR AND MOLECULAR PhARMACOLOGY/CALIfORNIA INSTITUTE fOR 

qUANTITATIVE BIOMEDICAL SCIENCES, UNIVERSITY Of CALIfORNIA, SAN 

fRANCISCO, CA, 94158 

Dr. Krogan is an Associate Professor in the Department of Cellular and Molecular 

Pharmacology at the University of California-San Francisco and is 

an expert in the fields of functional genomics and systems biology. He was 

born and raised in Regina, Saskatchewan, Canada and obtained his undergraduate 

degree from the University of Regina. As a graduate student at the 

University of Toronto, Dr. Krogan led a project that systematically identified 

protein complexes in the model organism, Saccharomyces cerevisiae, through 

an affinity tagging-purification/mass spectrometry strategy. This work led to 

the characterization of 547 complexes, comprising over 4000 proteins, and represents the most comprehensive 

protein-protein interaction map to date in any organism. To complement this physical interaction 

data, Dr. Krogan developed an approach, termed E-MAP (or epistatic miniarray profile), which allows for 

high-throughput generation and quantitative analysis of genetic interaction data. Dr. Krogan’s lab at UCSF 

focuses on applying these global proteomic and genomic approaches to formulate hypotheses about various 

biological processes, including transcriptional regulation, DNA repair/ replication and RNA processing. 

He is now developing and applying methodologies to create genetic and physical interactions between 

pathogenic organisms, including HIV and TB, and their hosts, which is providing insight into the human 

pathways and complexes that are being hijacked during the course of infection. 

Functional Insights from Protein-Protein and Genetic Interaction Maps 

Pathways and complexes can be considered fundamental units of cell biology, but their relationship to each 

other is difficult to define. Comprehensive tagging and purification experiments have generated networks 

of interactions that represent most stable protein complexes. We describe this work in various organisms, 

including budding yeast and in infectious organisms like HIV and TB, and show how the analysis of pairwise 

epistatic relationships between genes complements the physical interaction data, and furthermore can be 

used to classify gene products into parallel and linear pathways. 



RUEDI AEBERSOLD 

ETh zURICh AND UNIVERSITY Of zURICh, SWITzERLAND 

Dr. Ruedi Aebersold is a Professor in the field of proteomics and systems biology 

with joint appointments at the ETH (Swiss Federal Institute of Technology) 

Zurich, Switzerland and the University of Zurich. He has served on 

the faculties of the Universities of Washington and British Columbia. He cofounded 

the Seattle Institute for Systems Biology, and participates as a member 

of Scientific Advisory Boards for a number of academic and private sector 

research organizations. Dr. Aebersold, one of the pioneers in the field of 

proteomics and systems biology, is known for developing a series of methods 

and technologies for quantitative proteomics that can be applied to enhance 

our understanding of the structure, function, and control of complex biological 

systems. His group was instrumental in the landmark development of methods and reagents for stable 

isotopic labeling of protein samples enabling a quantitative dimension to biological mass spectrometry and 

the development of software tools for the statistically supported analysis of proteomics data. Recently, the 

group has pioneered the use of targeted mass spectrometry for the generation of consistent quantitative 

proteomic datasets on differentially perturbed systems. Dr. Aebersold has published more than 500 peer 

reviewed papers that have generated > 50.000 citations. He has reached an h-factor of 107 and is the recipient 

of numerous awards for his contribution to the field of protein sciences and proteomics including the 

MCP-HUPO lectureship (2011), the ASBMB Herbert Sober award (2009) the Otto Naegeli Prize (2009), the 

ABRF Award (2008), the FEBS Buchner Medal (2006), the HUPO Award (2005), the ASMS Biemann medal 

(2002) the Widmer award (2002), and the 2003 World Technology award. His group is currently focused on 

establishing novel label-free methods, leveraging new instrumentation and knowledge of representative 

“proteotypic” peptides, to rapidly and quantitatively profile global proteomes for discovery of new diagnostic 

markers for disease, and to facilitate a more complete understanding of the biochemical processes that 

control and constitute cell physiology. 

Network Driven Protein Biomarker Discovery and Validation 

A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Of all 

types of biomarkers, plasma protein biomarkers are particularly attractive because they can be measured 

in easily accessible samples. Unfortunately, the search for plasma protein biomarkers has been highly 

challenging and met with surprisingly low level of success. Specifically, the comparison of plasma sample 

proteomes of control and disease affected individuals has to date not uncovered any new markers. 

On the backdrop of the emerging personal genome information and large scale cancer genome projects we 

have developed and applied a biomarker strategy that is driven by cancer genetic and genomic information. 

In a fist stage we use comparative genomic data to computationally predict which signaling systems might 

be perturbed in a particular type of cancer. We use targeted proteomic measurements on human tissue 

samples or tissue samples from suitable mouse models to experimentally validate these predictions, i.e. to 

determine which proteins are disregulated in the specific disease. We then use then the such validated perturbed 

molecular networks to select proteins that are likely to be secreted or otherwise released into plasma 

and quantify these proteins in sets of plasma samples by selected reaction monitoring, a highly sensitive 

targeted mass spectrometry technique. 

In this presentation we will discuss this novel biomarker strategy, its present status and expected directions. 

A case study on PTEN dependent prostate cancer will illustrate the concept. 


ANNE-CLAUDE GAVIN 

EMBL, hEIDELBERG, GERMANY 

Expanding the interaction space; protein-metabolite networks 

Biological function emerges from the concerted action of numerous interacting 

biomolecules. Deciphering the molecular mechanisms behind cellular 

processes requires the systematic charting of the multitude of interactions 

between all cellular components. Since the sequencing of the first eukaryotic 

genome, Saccharomyces cerevisiae, more than 10 years ago, explosion of 

new analytical tools in the fields of transcriptomics, proteomics and metabolomics 

contributes ever-growing molecular repertoires of the building blocks 

that make up a cell. Biology does not rely on biomolecules acting in isolation. 

Biological function depends on the concerted action of molecules acting 

in protein complexes, pathways or networks. Biomolecular interactions are 

central to all biological functions. In human, for example, impaired or deregulated protein–protein or protein– 

metabolite interaction often leads to disease. Recent strategies have been designed that allow the study of 

interactions more globally at the level of entire biological systems. We will discuss the use of these biochemical 

approaches to genome-wide screen in model organisms. 

While protein–protein and protein– DNA networks have been the subject of many systematic surveys, others 

critically important cellular components, such as lipids, have to date rarely been studied in large-scale interaction 

screens. The importance of protein–lipid interactions is evident from the variety of protein domains 

that have evolved to bind particular lipids and from the large list of disorders, such as cancer and bipolar 

disorder, arising from altered protein–lipid interactions. The importance of lipids in biological processes and 

their under-representation in current biological networks suggest the need for systematic, unbiased biochemical 

screens. Here, we report a screen to catalog protein–lipid interactions in yeast using a lipid arrays. To 

illustrate the data set’s biological value, we studied further several novel interactions with sphingolipids, a 

class of conserved bioactive lipids with an elusive mode of action. Integration of live-cell imaging suggests 

new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. The 

dataset presented here represents an excellent resource to enhance the understanding of lipids function in 

eukaryotic systems. 



MAREIKE WEIMANN*, JONAThAN WOODSMITh*, ARNDT GROSSMANN, zIYA …zKAN, PETRA BIRTh, 

DAVID MEIERhOfER, SASChA SAUER, ULRICh STELzL OTTO-WARBURG LABORATORY, MAx-PLANCK 

INSTITUTE fOR MOLECULAR GENETICS (MPIMG) 

A Y2H-seq approach to define the protein methyltransferase interactome 

Protein methylation, in particular on arginine and lysine residues, is an important, widespread postranslational 

modification. The large number of human methyltransferases, potential demethylases and Merecognition 

domain containing proteins, which are not only expressed in a large variety of tissues but also at 

different subcellular localizations, indicate roles in many cellular processes other than epigenetic regulation. 

However, the transient nature of substrate enzyme recognition, the lack of affinity reagents and appropriate 

tools to detect methyltransferase substrate pairs largely hampered progress in defining the global role of 

non-histone protein methylation. 

Here we present a novel proteome wide Y2H protein interaction screening approach involving a second 

generation sequencing readout. The method has significantly improved sensitivity in comparison to our state 

of the art Y2H matrix screening protocol. Importantly, 2nd generation sequencing provides a quantitative 

readout that correlates very well with the retest success rate indicative of the quality of the PPI information. 

Y2H-seq will thus accelerate large scale interactome mapping efforts. 

We applied the Y2H-seq method to comprehensively screen proteins involved in methylation and demethylation, 

i.e. protein methyl transferases (PMTs) and JMJ-domain containing putative demethylases 

(PDeMs) such as LSM1, for interacting proteins. We found more than 500 interactions involving 22 PMTs 

and PDeMs and 324 potential methylation substrates. Exemplarily, 7 candidate proteins are characterized 

with respect to novel R and K methylation sites using a mass spectrometry approach. The final network is 

comprehensively annotated, validated in co-IP experiments and will serve as a major informational resource 

to define cellular roles of non-histone protein methylation. 


A.J. MARIAN WALhOUT 

UNIVERSITY Of MASSAChUSETTS MEDICAL SChOOL 

WORCESTER, MA 

A.J. Marian Walhout obtained her BS and PhD degrees in Biochemistry/Medicine 

from Utrecht University in the Netherlands. After a post-doc at Harvard 

Medical School, she became a Faculty member at the University of Massachusetts 

Medical School, Worcester, USA in 2003. She is currently a Professor 

in Molecular Medicine and a Co-Director of the Program in Systems Biology. 

Gene regulatory networks 

Transcriptional regulation of gene expression is pivotal to all biological processes. 

Each of our ~20,000 genes must be expressed at the right place, time 

and level, and under the right conditions. As a consequence, improper gene expression is associated with 

a myriad of human diseases, including congenital disorders, cancer and obesity. The basic mechanisms 

of RNA polymerase II transcription have been studied in great detail for decades. However, little is known 

about the gene regulatory networks (GRNs) that are composed of physical and regulatory interactions 

between transcription factors and their target genes, and that orchestrate spatiotemporal gene expression 

during development or upon physiological stresses and pathological insults. Our long-term goal is to comprehensively 

characterize the structure, function and evolution of complex metazoan GRNs. As a model we 

use the nematode C. elegans, which is amenable to a variety of genetic and genomic approaches. We have 

developed a variety of gene-centered methods for the elucidation of GRNs. Progress will be discussed. 



BEN LEhNER 

EMBL-CRG SYSTEMS BIOLOGY RESEARCh UNIT AND ICREA, CENTRE fOR 

GENOMIC REGULATION, UPf, BARCELONA, SPAIN 

Ben Lehner is an ICREA Research Professor at the EMBL-CRG Systems Biology 

Program in Barcelona. He has a degree (Natural Sciences) and PhD (protein interaction 

networks, antisense transcription) from the University of Cambridge 

and was a post-doctoral fellow in the Fraser lab at the Wellcome Trust Sanger 

Institute (genetic interactions, large-scale integrated networks). Since Dec 

2006 he has been at the CRG, funded by the ERC, EMBO YIP program, ERASys- 

Bio+, AGAUR, Plan Nacional, and the CRG. The main aim of the lab is to answer 

basic questions in genetics, using highly quantitative or systematic experimental 

and computational approaches in different model systems, as necessary. 

The biology of individuals 

To what extent is it possible to predict the phenotypic differences among individuals from their completely 

sequenced genomes? We use model organisms (yeast, worms) to understand when you can, and why you 

cannot, predict the biology of an individual from their genome sequence. 


onships are utilized. 

BERNhARD PALSSON 

UCSD / DTU - SAN DIEGO, CA / LYNGBY 

Systems Biology of Metabolism 

The full genome sequences that began to appear some 15 years ago enabled 

the bottom-up reconstruction of biochemical reaction networks that operate 

in a particular target organism. Such reconstructions can be converted into a 

mathematical format that represents mechanistic genotype-phenotype relationship. 

This relationship is fundamental in biology, and it has a very different 

characteristic that the basic physical laws elucidated about a century ago. In 

this talk we; 1) put the field of molecular systems biology into a historical 

context, 2) review the workflows and procedures that have been developed 

over the past decade for network reconstruction, and 3) go through a series 

of examples that show how mechanistic metabolic genotype-phenotype relati- 


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abStractS For PoSterS 

ThOMAS R COx1 , ERWIN SChOOf2, SARA zANIVAN3, RUNE LINDING2 AND JANINE T ERLER1 

1 BIOTECh RESEARCh AND INNOVATION CENTRE, UNIVERSITY Of COPENhAGEN, DENMARK 2 CENTER 

fOR BIOLOGICAL SEqUENCE ANALYSIS, TEChNICAL UNIVERSITY Of DENMARK, DENMARK 3 BEATSON 

INSTITUTE fOR CANCER RESEARCh, GLASGOW, UK 

Remodelling of the ECM as a critical mediator of tumour metastasis 

Tumour metastasis is a highly complex, dynamic and inefficient process involving multiple steps, yet accounts 

for over 90% of cancer patient deaths. The tumour microenvironment and in particular the extracellular 

matrix is a key component in driving this process at multiple stages. Both the biochemical and biomechanical 

properties or tumour extracellular matrix (ECM) contribute to progression. Metastatic tumours show 

elevated ECM remodelling and increased stiffness in comparison to their non-metastatic counterparts and 

these changes in stiffness are known to drive metastatic cell behaviour although the underlying molecular 

mechanisms remain elusive. The aim of this project is to utilise multiple molecular approaches and evaluate 

both the molecular and behavioural changes occurring in tumour cells in response to ECM remodelling and 

in particular changes in ECM stiffness. By computationally integrating molecular and phenotypic data, we 

aim to derive a molecular network associated with stiffness and identify key enablers of metastatic progression. 

Using breast and colorectal cancer models, we have found that metastatic tumours are stiffer than matched 

non-metastatic tumours. We have shown that increasing ECM stiffness can drive the invasive behaviour of 

the non-metastatic cancer cells. We observe associated cell signalling events and gene expression changes, 

and are further investigating the molecular networks associated with enhanced metastasis in response 

to increased stiffness. 

The goal of the study is to predict and test novel therapeutic strategies for the treatment and prevention of 

metastasis that could then be translated into the clinic. 


PETER hUSEN1, KIRILL TARASOV2, ALBERT CASANOVAS1, KIM EKROOS2, ChRISTER S. EJSING1 

1DEPARTMENT Of BIOChEMISTRY AND MOLECULAR BIOLOGY, UNIVERSITY Of SOUThERN DENMARK 

2zORA BIOSCIENCES OY, ESPOO, fINLAND 

A software routine for charting the composition and dynamicsof lipid metabolic networks 

Lipids constitute a large part of the metabolism of eukaryotic organisms where they play an important role as 

constituents of the membranes separating various cellular compartments from each other and the cell itself 

from its surroundings. The specific lipid composition of membranes is believed to play an important functional 

role in controlling membrane shape and to affect both affinity and function of membrane-embedded 

proteins. The importance of the lipidome and its entanglement in the overall cellular metabolic network call 

for efficient and reliable methods to quantify lipids as part of the general efforts in mapping the metabolome. 

We have established a software tool and framework for streamlined quantification of cellular lipidomes. The 

approach is based on high resolution mass spectrometry using direct infusion and application of internal 

standards to allow absolute quantification. The software platform is based on targeted extraction of spectral 

data using target lists taylored to the individual experiments from a lipid database. The spectral peak 

extraction supports offline calibration of the spectra using lock masses to improve mass accuracy and thus 

maintain high specificity even in crowded spectra. 

The efficient and automated lipidome-wide quantification facilitates studies of the kinematics and complex 

regulation of lipid metabolic pathways, e.g. by flux analysis. Particularly, we have employed this framework 

to chart the dynamics of the yeast metabolic network under various growth conditions. A total of ~240 lipid 

species were quantified simultaneously in this experiment at various time points for each growth condition. 

Using the software toolbox, the evolution of both individual lipid species abundances and overall lipidome 

features are now efficiently tracked, which provides a huge amount of information on the lipid metabolic 

network of this organism. 

abStractS For PoSterS abStractS For PoSterS 

JOAN ChANG,ANDREAS hADJIPROCOPIS, MARIE-CLAUDE DJIDJA, JOhN SINCLAIR, CLAUS JORGENSEN, 

RUNE LINDING, JANINE ERLER 

MoleCular Networks Associated with Hypoxia-regulated Metastasis 

Hypoxia has been shown to increase metastasis, however the underlying molecular mechanisms are still 

unclear. As of yet, there have been no systematic approaches to investigate the proteomic networks associated 

with hypoxia-driven tumour progression. Here, we investigated the molecular networks associated with 

hypoxia in both in vitro and in vivo samples, using a proteomics approach. 

4T1mouse mammary carcinoma cells were subjected to stable isotope labeling by amino acids in cell culture 

(SILAC), incubated for 24 hours in air or hypoxia, followed by quantitative mass spectrometry ( MS) using a 

ThermoFisher OrbiTrap-Velos. 4T1 cells were also implanted into mice to form in vivo tumours. 

Samples were isolated from regions of normoxia and hypoxia in the tumours using laser capture microdissection 

(Leica), and proteins were identified through MS (OrbiTrapVelos). Finally, sections of tumours 

were subjected to MALDI-MSI using an ABSciex 5800 to determine the distribution of various proteins in the 

tumour. 

We identified hypoxia-regulated proteins in cancer cells in vitro, and verified our findings with protein identification 

in in vivo samples. Furthermore, MALDI-MSI allowed us to visualize the distribution of proteins in 

the tumour sections, and confirm hypoxic localization. We developed a computer programme that analysed 

all proteins quantitatively detected in the in vitro SILAC system, and visualized them using Cytoscape. This 

enabled the build up of an interaction network of all proteins detected, including those that were not significantly 

up-or down-regulated, providing a snapshot of the global state of the protein networks within the cells. 

This is the first comprehensive study investigating hypoxia-regulated proteins both in vitro and in vivo. This 

presents an exciting prospect that with further optimization, these techniques will provide a powerful tool to 

identify specific proteins associated with the microenvironment that drive cancer progression. 


KRzYSzTOf fUJAREWICz 

SILESIAN UNIVERSITY Of TEChNOLOGY, POLAND 

Optimal sampling for parameter estimation of cell signaling pathway models based on multivariate measurements 

Mathematical modeling of cell signaling pathways became a very important and challenging problem 

in recent years. The importance comes from possible application of obtained models. It may help us to 

understand phenomena appearing in single cells and in cell populations. Furthermore, it may help us with 

discovering new drug therapies. 

Mathematical models of cell signaling pathways take different forms. The most popular way of mathematical 

modeling is to use a set of nonlinear ordinary differential equations (ODEs). 

Usually there are many hypotheses about the structure of the model (set of variables and set of phenomena). 

It leads to several rival models and one of them should be chosen among others. This choice may be 

supported by so called T-optimal experiment design. 

The next step, estimation of the model’s parameters, is also very complicated because of the nature of 

measurements. The blotting technique usually gives only semi-quantitative observations which are very 

noisy and they are collected only at limited number of time moments. Once more, the accuracy of parameter 

estimation may be significantly improved by proper experiment planning. 

We present two-stage experiment plan optimization algorithm. The first step is gradient-based D-optimization 

procedure to find all stationary points. The second step is pair-wise replacement for finding optimal 

numbers of replicates of measurements. The algorithm is applied to one of cell signaling pathway model, 

known from the literature. 

For multivariate measurements the presented approach gives, in general, different optimal time points for 

different variables. In practice it is better to take measurements at the same time points. We show how this 

constraint influences the final result of optimization. 

This work has been supported by Polish Ministry of Education and Science under grant N N514 411936. 


IA VAN DIJK, MJ OUDhOff, J VAN DE AMEIDE, JGM BOLSChER, ECI VEERMAN. 

DEPARTMENT Of ORAL BIOChEMISTRY, ACADEMIC CENTRE fOR DENTISTRY AMSTERDAM (ACTA), UNI- 

VERSITY Of AMSTERDAM AND VU UNIVERSITY AMSTERDAM, ThE NEThERLANDS. 

Histatin from saliva promotes wound healing: Salivary peptide histatin induces GPCR- and ERK-dependent 

cell migration 

Wounds in the oral cavity heal faster than at other sites in the human body, e.g. the skin. Several factors 

have been implicated in this phenomenon, including the presence of saliva, which in rodents is a reservoir of 

many growth factors, such as epidermal growth factor (EGF) and nerve growth factor (NGF). In humans the 

identity of the involved compounds has remained elusive, since the saliva concentration of growth factors is 

1,000 to 100,000 times lower than in rodent saliva. The present study was aimed at the identification of the 

factor(s) responsible for the alleged wound healing power of saliva, and a first characterization of the cellular 

processes. 

Using an in vitro scratch assay we found that human saliva is able to induce epithelial cell migration, without 

involvement of EGF. By testing protein fractions of human saliva obtained by HPLC, we identified salivary 

histatins as the main migration-inducing factors in saliva. The ᴅ-enantiomer of histatin is not active, pointing 

towards the involvement of a stereospecific receptor. N-to-C terminal cyclization of histatin potentiates the 

molar activity approximately 1,000-fold, suggesting that the activation of the putative receptor requires a 

specific spatial conformation of the peptide. Histatin activity was abolished both by inhibition of mitogen activated 

protein kinases ERK1/2 and MEK, and by pertussis toxin, an inhibitor of G protein coupled receptors, 

suggesting the involvement of a GPCR-dependent ERK1/2 signaling pathway. Using a functional phosphokinase 

assay, we found, in addition, activation as well as inhibition of a large number of other phosphokinases 

upon treating epithelial cells with histatin. 

Conclusion: Our results emphasize the importance of histatin in human saliva for tissue protection and recovery, 

and establish the experimental basis for the development of synthetic histatins as novel wound-healing 

agents. 


VEIT SChWÄMMLE AND OLE NøRREGAARD JENSEN DEPARTMENT Of BIOChEMISTRY AND MOLECULAR 

BIOLOGY, UNIVERSITY Of SOUThERN DENMARK, CAMPUSVEJ 55, DK-5230 ODENSE M, DENMARK 

A simple model of histone mark propagation reproduces dynamic features of chromatin structure and transcriptional 

regulation 

Heterochromatin, the transcriptionally inactive part of the genome, is densely packed and contains histone 

H3 that is methylated at Lys 9 (H3K9me). The propagation of H3K9me in nucleosomes along the DNA in 

chromatin is antagonized by methylation of H3 Lysine 4 (H3K4me), which is related to euchromatin and 

active genes. Both modification marks are assumed to be initiated within distinct nucleation sites in the 

DNA and to propagate bi-directionally. We propose a simple computer model that simulates the distribution 

of heterochromatin in human chromosomes. Our model explains how heterochromatin is prevented from 

occupying regions of active gene expression through continuous competition between the two marks. The 

computa¬tional simulations of heterochromatin distribution in chromosomes are in agreement with previously 

reported experimental observations. An extended model considers multiple co-existing histone marks 

and reproduces important features of chromatin, including switch-like behavior for activation/inactivation of 

chromatin domains, and temporal and spatial regulation of genes/chromatin by histone modification domain 

rearrangements. The simulations reproduce not only complex spatial conformations but also temporal 

features, such as oscillatory behavior found in cell cycle processes and circadian rhythms. The propagation 

rates of co-existing histone marks play a crucial role for the transition between distinct chromatin and gene 

regulatory states of the system. Our model demonstrates that the interplay of multiple co-existing histone 

modifications can explain different chromatin states, thereby controlling for instance cell growth, diff erentiation 

and metabolism in a spatio-temporal manner. 


ANNA-MARIA LAhESMAA-KORPINENA, SARI JALKANENB, JUKKA VAKKILAB, KIMMO PORKKAB, SATU 

MUSTJOKIB, SAMPSA hAUTANIEMIA 

A RESEARCh PROGRAMS UNIT, GENOME-SCALE BIOLOGY AND INSTITUTE Of BIOMEDICINE, BIOChEMIS- 

TRY AND DEVELOPMENTAL BIOLOGY, UNIVERSITY Of hELSINKI, fINLAND; B hEMATOLOGY RESEARCh 

UNIT, BIOMEDICUM, DIVISION Of MEDICINE, hELSINKI UNIVERSITY CENTRAL hOSPITAL AND UNIVER- 

SITY Of hELSINKI, fINLAND 

Single cell analysis of phosphoprotein network response to tyrosine kinase inhibitor therapy in chronic 

myeloid leukemia patients 

Tyrosine kinase inhibitors (TKI) are currently the therapy choice for treatment of chronic myeloid leukemia 

(CML) patients. TKIs have been shown to be immunosuppressive in vitro, while on the other hand some 

patients experience immunoactivation during dasatinib-therapy. Our previous results show an effect of dasatinib 

treatment on the basal activation status of the STAT3 signaling pathway, and the monocyte populations 

of CML patients at the diagnosis phase were found to respond poorly to cellular cytokine stimulation, which 

was restored to normal levels during TKI therapy. Previous studies, however, focused on protein expression 

medians of the cell populations studied, and were not able to utilize the single cell information from flow 

cytometry (FCM) measurements. 

To study the phenomenon in more detail, we used a computational framework designed for large scale FCM 

data analysis to automatically gate the lymphocyte populations and interesting sub-populations for individual 

patients and various cytokine stimulations. We studied the individual differences in phosphoprotein networks 

of CML patients after TKI therapy of patients at diagnosis (n=10), patients after dasatinib (n=10) or imatinib 

(n=10) treatment and control subjects (n=7). We measured the expression of phosphorylated ERK1/2, 

STAT1, STAT3, STAT5 and STAT6, and cell surface markers with FCM after various cytokine treatments. We 

constructed comprehensive signaling networks for the phosphoproteins using known databases for proteinprotein 

interactions and pathway information. Using each distinct cell population, we clustered the phosphoprotein 

profiles for the various patient groups and looked for clusters of differential signaling networks within 

patients and cell populations. We identified various cell population shifts showing differential phosphoprotein 

expression affecting immunological signaling pathways. 


BLAKE BORGESON(1), CUIhONG WAN(2), ANDREW EMILI(2), EDWARD MARCOTTE(1) 

1: ThE UNIVERSITY Of TExAS AT AUSTIN, CENTER fOR SYSTEMS AND SYNThETIC BIOLOGY 

2: ThE UNIVERSITY Of TORONTO, BANTING AND BEST DEPARTMENT Of MEDICAL RESEARCh 

Conservation and Divergence of Protein Complexes Across Evolution 

Despite agreement that the vast majority of life’s processes at a cellular level are carried out by complexes 

of multiple proteins, knowledge of all the complexes formed in a cell and their members is a distant goal. By 

using a new approach developed by collaborators Havugimana and Hart, et al, consisting of 1) subjecting 

biological samples to many levels of many types of fractionations, 2) using LC-MS/MS to quantify protein 

levels in each fraction, and 3) processing the data through a machine learning pipeline, we identify putative 

soluble (non¬membrane) complexes using a high-throughput all-by-all approach. By incorporating additional 

functional genomic information into our learning process, we are able to reconstruct maps of complexes that 

so far seem to rival in quality those generated using previous, more labor-intensive methods. Here, we apply 

the approach to biological samples from many organisms, including human, sea urchin, fly, worm, and multicellular 

amoebas, in order to rapidly learn soluble complexes in many species. From such maps we identify 

interesting conservation and divergence of complexes not previously well-understood or studied. 


ChRISTOPhER A. BARNES, ALESSIO MAIOLICA, RUEDI AEBERSOLD 

INSTITUTE Of MOLECULAR SYSTEMS BIOLOGY, ETh züRICh, SWITzERLAND 

A Global Kinase-Substrate Network in Yeast Mapped Using In Vitro Phosphorylation and Phosphoproteomics 

Kinase inhibitor drugs have been a major focus in the pharmaceuticals industry over the past decade. 

Despite extensive development of these inhibitors, their effectiveness has been limited. It is thought that 

the difficulties associated with kinase inhibitor drug effectiveness relates to network compensation events 

in the cell that allow other kinases to replace the lost function of the inhibited enzyme. To date, it has been 

difficult to link kinases to substrates at the amino acid level with methodologies suitable to creating a global 

kinase-substrate wiring diagram. Identifying direct kinase-substrate relationships for each kinase could help 

researchers to design pharmaceutical solutions that take into account network compensations. The aim 

of this project is to identify kinase-substrate relationships in yeast for all of the kinases in S. cerevisiae. In 

previous studies in the lab1, experiments were performed where kinases were sequentially deleted, but the 

identification of direct kinase-substrate relationships with these deletion studies proved difficult. Here, we 

have taken a large library of the most regulated phosphosites from these deletion experiments and synthesized 

unphosphorylated peptides suitable for in vitro phosphorylation followed by LC-MS/MS identification of 

the phosphorylated product peptides. We use endogenously expressed immunopurified kinases followed by 

a phosphopeptide enrichment step prior to mass spectrometry analysis. Our phosphorylation assays have 

shown good specificity in vitro as evidenced by the identification of specific known consensus motifs and 

we have begun to identify subfamily redundancies in the substrate networks as this yeast phosphorylation 

wiring diagram is beginning to emerge. 

References: 

1. Bodenmiller et al. Sci. Signal. 3, rs4 (2010). 


PAU CREIxELL1, DORIEN WIJTE1, ERWIN SChOOf1, AGATA WESOLOWSKA2, ThOMAS NORDAhL PETERS- 

EN2, RAMNEEK GUPTA2, hIROAKI ITAMOChI3, JANINE ERLER4 & RUNE LINDING1 

1 CELLULAR SIGNAL INTEGRATION GROUP (C-SIG), CENTER fOR BIOLOGICAL SEqUENCE ANALYSIS (CBS), 

DEPARTMENT Of SYSTEMS BIOLOGY, TEChNICAL UNIVERSITY Of DENMARK (DTU), DK-2800LYNGBY, 

DENMARK.2 fUNCTIONAL hUMAN VARIATION, CENTER fOR BIOLOGICAL SEqUENCE ANALYSIS (CBS), 

DEPARTMENTOf SYSTEMS BIOLOGY, TEChNICAL UNIVERSITY Of DENMARK (DTU), DK-2800 LYNGBY, 

DENMARK.3 DEPARTMENT Of OBSTETRICS AND GYNECOLOGY, TOTTORI UNIVERSITY SChOOL Of MEDI- 

CINE, 36-1NIShIChO, YONAGO 683-8504, JAPAN.4 BIOTECh RESEARCh & INNOVATION CENTRE (BRIC), 

COPENhAGEN UNIVERSITY (KU), DK-2200COPENhAGEN, DENMARK. 

Kinomewide Discovery of Functional Cancer Mutations. 

Systematic sequencing and targeted studies of tumors are revealing protein kinases as key drivers of cancer. 

More than a thousand distinct cancer mutations (equal to around 3fold enrichment) have been identified 

in protein kinases alone. However, pinpointing whichof these mutations are functional and how they dysregulate 

signaling networks in cells remains a challenge. We have developed an algorithm, ReKINect, that 

identifies functionalmutations and predicts how these loss-and gain-of-function mutations lead to changes 

insignaling network dynamics and architecture. By combining genome-wide sequencing datawith proteomics 

data we want to show how functional mutations can result in kinaseactivation, kinase inactivation, activation 

rewiring or substrate rewiring. We define a superSILAC-type strategy to validate our in silico predictions, 

which suggests that theeffect of these different types of mutations can be observed at the (phospho)proteomelevel. 

• Creixell et al. Navigating Cancer Network Attractors for Tumor-specific Therapy.Submitted. 

• Creixell et al. Kinome-wide Discovery of Functional Cancer Mutations. In preparation. 

• Creixell et al. Mutational Properties of Amino Acid Residues - Implications for Evolvabilityof Phosphorylatable 

Residues. Accepted in Philosophical Transactions of the RoyalSociety B. 


SIMONE DAMINELLI†, V. JOAChIM hAUPT†, MATThIAS REIMANN AND MIChAEL SChROEDER 

BIOTEC, TU DRESDEN, GERMANY. 

Drug Repositioning through Incomplete Bi-cliques in an Integrated Drug-Target-Disease Network 

Recently, there has been much interest in gene-disease networks and polypharmacology as a basis for drug 

repositioning. Here, we integrate data from structural and chemical databases to create a drug-target-disease 

network for 147 promiscuous drugs, their 553 protein targets, and 44 disease indications. Visualizing 

and analyzing such complex networks is still an open problem. We approach it by mining the network for 

network motifs of bi-cliques. In our case, a bi-clique is a subnetwork in which every drug is linked to every 

target and disease. Since the data is incomplete, we identify incomplete bi-cliques, whose completion introduces 

novel, predicted links from drugs to targets and diseases. We demonstrate the power of this approach 

by repositioning cardiovascular drugs to parasitic diseases, by predicting the cancer-related kinase PIK3CG 

as novel target of resveratrol, and by identifying for five drugs a shared binding site in four serine proteases 

and novel links to cancer, cardiovascular, and parasitic diseases. 

† These authors contributed equally to this work. 


ELENA NIKONOVA DIRK fEY† MIKhAIL TSYGANOV‡ BORIS KhOLODENKO 

Temporal dynamics of two layer GTPase cascade with GDI binding 

GTPases control intracellular signaling and are responsible for a variety of vital cellular mechanisms such 

as cytoskeleton formation, motility and vesicle transport. Functioning of GTPases occurs due to monomeric 

G proteins cycling between inactive GDP-bound and active GTP-bound states. The reaction is catalyzed 

by guanine nucleotide exchange factors (GEFs) for the transformation from GDP to GTP and by GTPase 

activating proteins (GAPs) for the reverse transformation. Malfunction of the GTPases often occurs due 

to the deregulated expression and activities of GAP and GEF, which was found to be one of the causes of 

tumorogenesis [2]. Guanine nucleotide dissociation inhibitors (GDIs) were also found to regulate the GTPase 

cycles by binding to the inactive GDB-bound form and transporting the formed complex away from the membrane 

to the cytosol. Previous characterization of spatiotemporal dynamics showed that models comprised 

of two GTPases without GDI binding can exhibit 3 distinct regimes: sustained oscillations, bistable switches 

and excitable behavior [1]. The current work explores the change in dynamics by allowing GDI to bind to 

the active and inactive sites of both GTPases. In particular, our results show that as the dissociation and 

association rates of GDI binding are varied, the two layer GTPase model can exhibit transformations within 

previously outlined regimes. 

References 

[1] M.A.Tsyganov, W. Kolch and B.N. Kholodenko Molecular BioSystems, 2012. 

[2] D. Vigil, J. Cherfils, K.L. Rossman and C.J. Der Nat. Rev. Cancer, 10(12), 842-857, 2010. 

*Systems Biology Ireland, University College Dublin, Belfield, Dublin 4 

†Systems Biology Ireland, University College Dublin, Belfield, Dublin 4 

‡Institute of Theoretical and Experimental Biophysics, Pushchino, Moscow Region, Russia §Systems Biology 

Ireland, University College Dublin, Belfield, Dublin 4 


ALExEY GOLTSOV (A), DANA fARATIAN(B), SIMON LANGDON(B), DAVID hARRISON(B), JAMES BOWN(A) 

(A) CENTRE fOR RESEARCh IN INfORMATICS AND SYSTEMS PAThOLOGY, UNIVERSITY Of ABERTAY 

DUNDEE, DUNDEE, UK (B) EDINBURGh BREAKThROUGh RESEARCh UNIT AND DIVISION Of PAThOLOGY, 

WESTERN GENERAL hOSPITAL, UNIVERSITY Of EDINBURGh, EDINBURGh, UK 

Systems biology of drug sensitivity-resistance transition in PI3K/AKT signalling in cancer 

Systems biology offers a useful approach to study dependence of drug efficacy on oncogene-driven 

transformations in drug target pathways relevant to cancer therapy. Systems approach was developed to 

elucidate mechanisms underlying the changes of the efficacy of monoclonal antibody therapy (trastuzumab, 

pertuzumab) targeting HER2 receptor at cancer genome transformation in the PI3K/AKT signalling network 

(SN) [Goltsov et al Cell. Signalling 2011]. In silico experiments showed that HER2 inhibition sensitises the 

SN both to external signals and to kinetic characteristics of the proteins and their expression levels. We suggested 

that a drug-induced increase in SN sensitivity to internal perturbations, and specifically mutations, 

causes SN fragility. In particular, the SN is vulnerable to mutations that compensate for drug action and this 

may result in a drug sensitivity-to-resistance transition in SN [Goltsov et al Cell. Signalling 2012]. Modelling 

showed the increase of SN sensitivity to typical aberrations in cancer causing drug resistance: loss of 

PTEN activity, PI3K and AKT mutations, HER2 overexpression, and overproduction of GSK3ᴅ controlling 

PTEN activity. In particular, the SN is vulnerable to mutations that compensate for drug action. PTEN loss or 

PIK3CA mutation was shown to cause resistance to HER2 inhibition and leads to the restoration of maximal 

pAKT signal with a consequent decrease in SN sensitivity. The drug-induced sensitivity of SN was tested in 

experiments on ovarian cancer cells which demonstrated that HER2 inhibition increased SN sensitivity to 

the second inhibitor targeting downstream pathway, in particular PI3K inhibition. The developed method is 

proposed to be used in the pointed development of combined treatment of cancer which provides both synergetic 

inhibition of SN activated in cancer and prevention of the SN from acquired drug resistance caused 

by oncogenic mutations. 


TRI hIEU NIM1, LE LUO2, JACOB K. WhITE1,3, MARIE-VéRONIqUE CLéMENT2, LISA 

TUCKER-KELLOGG4 

1 COMPUTATIONAL SYSTEMS BIOLOGY PROGRAMME, SINGAPORE-MIT ALLIANCE 2 DEPARTMENT Of 

BIOChEMISTRY, YONG LOO LIN SChOOL Of MEDICINE, NATIONAL UNIVERSITY Of SINGAPORE 3 DEPART- 

MENT Of ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, M.I.T. 4 MEChANOBIOLOGY INSTITUTE, 

NATIONAL UNIVERSITY Of SINGAPORE 

Mathematical modeling of Akt phosphorylation dynamics in serum stimulated fibroblasts 

Pathological over-activation of Akt is responsible for cell survival advantages in cancer, and may arise from 

mechanisms present in normal cells. In normal cells, peaks of high Akt phosphorylation (activation) occur 

briefly after serum stimulation, before reaching a moderate steady-state. It is not known whether canonical 

mechanisms of Akt regulation are sufficient to explain the observed dynamics of Akt phosphorylation. 

Methods: Possible mechanisms for the brief extreme of Akt phosphorylation were represented by a null 

hypothesis and four alternative hypotheses. Ordinary differential equation models of the hypotheses were 

constructed using our previously published measurements of phosphatidylinositol(3,4,5)-trisphosphate 

(PIP3), total-Akt, and Akt-phosphoThr308 (Aktp308) in mouse embryonic fibroblasts. Simulations revealed 

qualitative differences in the dynamics of membrane-cytosol translocation. Additional immuno-blots 

were performed to measure the dynamics of membrane localization. Results: Two of five hypotheses were 

incompatible with the observed dynamics of PIP3 and PDK1. Measurements of membrane Aktp308 and 

membrane total-Akt peaked at 5 and 30 minutes, while measurements of PIP3 peaked at 2 minutes; this 

could be explained by a hypothetical non-PIP3 mechanism that increases Akt membrane recruitment. Two 

remaining hypotheses (Aktp308 sequestration at membrane, and inhibited Akt dephosphorylation) were able 

to reproduce many but not all observed trends. Conclusion: We conclude that a non-canonical enhancement 

of the Akt pathway occurs downstream of PDK1. According to computational simulations analyzing the 

implied dynamics of different hypotheses, three mechanisms are possible, but the data are most consistent 

with a non-PIP3 mechanism to increase recruitment of Akt to the membrane. 


DASSI E, MALOSSINI A, RE A, MAzzA T, TEBALDI T, CAPUTI L, qUATTRONE A. 

LABORATORY Of TRANSLATIONAL GENOMICS - CENTRE fOR INTEGRATIVE BIOLOGY, UNIVERSITY Of 

TRENTO, VIA DELLE REGOLE, 101, 38123 MATTARELLO (TN), ITALY. 

AURA: Atlas of UTR Regulatory Activity. 

The Atlas of UTR Regulatory Activity (AURA) is a manually compiled and comprehensive catalog of human 

mRNA untranslated regions (UTRs) and UTR regulatory annotations. Through its intuitive web interface, 

it provides full access to a wealth of information on UTRs that integrates phylogenetic conservation, RNA 

sequence and structure data, single nucleotide variation, gene expression and gene functional descriptions 

from literature and specialized databases. 

Reference 

http://aura.science.unitn.it 


ROBERT J WEAThERITT1, NORMAN E DAVEY1, TOBY J GIBSON1 

1 STRUCTURAL AND COMPUTATIONAL BIOLOGY UNIT, EUROPEAN MOLECULAR BIOLOGY LABORATORY, 

MEYERhOfSTRASSE 1, 69117 hEIDELBERG, GERMANY 

Linear Motifs confer functional diversity onto splice variants 

The pre-translational modification of mRNAs by alternative promoter usage and alternative splicing is an 

important source of pleiotropy. Despite intensive efforts, our understanding of the functional implications 

of this dynamically created diversity is still incomplete. Using the recent expansion in our knowledge of the 

interaction modules within intrinsically disordered regions, we analysed the occurrences of protein modular 

architecture within alternative exons. We find that regions affected by pre-translational variation are enriched 

in linear motifs and phosphorylation sites suggesting that the modulating of exons containing these interaction 

modules is an important regulatory mechanism. In particular, we observe PDZ, PTB and SH2 binding 

motifs are particularly prone to be altered between splice variants. We also determine that regions affected 

by alternative promoter usage are enriched in IDRs suggesting that protein isoform diversity is tightly coupled 

to the modulation of IDRs. This study therefore demonstrates that short linear motifs, and to a lesser 

extent phosphorylation sites, are key components for establishing protein diversity between splice variants. 


MISS SOPhIE KERShAW, DR. JAMES OSBORNE, PROf. hELEN BYRNE, PROf. DAVID GAVAGhAN. 

DEPARTMENT Of COMPUTER SCIENCE, UNIVERSITY Of OxfORD; COLLABORATION WITh ThE WEAThER- 

ALL INSTITUTE fOR MOLECULAR MEDICINE, UNIVERSITY Of OxfORD. 

Proliferation and Cell Fate in the Colorectal Epithelium 

Endemic across the developed world, the prevalence of colorectal cancer (CRC) has not been matched by 

equivalent success in pharmaceutical development. Drug failure in clinical trial patients after early-stage 

success in laboratory tests motivates a more detailed consideration of the 

fundamental differences between in vitro and in vivo cell behaviour. To what extent does tissue geometry 

influence expression of the subcellular biochemistry in CRC? 

We therefore present a novel framework for in silico translation experiments, enabling experimental hypotheses 

to be explored through simulations of colorectal tissue. Developed in collaboration with biochemists 

at the Weatherall Institute of Molecular Medicine, we provide a representation of colorectal tissue that incorporates 

mathematical equations to govern the biochemical behavior of its constituent cells. Our focus rests 

on models for two key pathways implicated in early-stage CRC, namely Notch (involved in cell fate specification) 

and Wnt (a governor of proliferation). 

We present our model for Notch and Wnt signalling, based upon existing models in the literature, and incorporate 

crosstalk between the two pathways. We will discuss an embedding of our model within cells of both 

monolayer and colorectal geometries, discussing the spatial constraints imposed in these two scenarios and 

demonstrating the resultant cell patterning and proliferative behaviour. 

This work presents a new approach to CRC modelling on several counts. Firstly, via the inclusion of crosstalk 

between Notch and Wnt pathways to demonstrate coupled control over proliferation and lineage decisions. 

This research also represents a novel development in modelling the 

Notch-Wnt interaction in situ, in its application of multiscale frameworks to examine the impact of tissue 

configuration. 


RIKU LOUhIMO, TATIANA LEPIKhOVA, OUTI MONNI, SAMPSA hAUTANIEMI 

RESEARCh PROGRAMS UNIT, fACULTY Of MEDICINE, UNIVERSITY Of hELSINKI, fINLAND 

Comparative Analysis of Algorithms for Integration of Copy Number and Expression Data 

Genomic instability is a key enabling characteristic of cancer, and genes that display differential expression 

in regions with notable copy-number alteration are likely to be drivers ofcancer. Identifyingsuchdrivergenesfromhigh-throughput 

and sequencingdata requires computational tools that are capable of integrating data 

from several sources. Hence, several algorithms that integrate copy-number and expression data have been 

developed. Their performance, however, has not so far been assessed relative to one another. 

We have compared ten algorithms that integrate high-throughput copy number and transcriptomicsdata 

using simulated,head and neck squamous cell carcinoma(HNSCC) cellline andlung squamous cell 

carcinoma(LUSC) primary tumordata. The simulated data enabled us to assess algorithms’ sensitivity and 

specificity. For the comparison with the HNSCC and LUSC data we selected 30 genes, whose expression 

has been shown to be altered due to underlying copy-number aberrations in squamous cell carcinomas. 

Algorithms exhibit clear differences in sensitivity and specificity, and their performance decreases with small 

sample sets. 


APURV GOEL, MARC WILKINS 

SYSTEMS BIOLOGY INITIATIVE, SChOOL Of BIOTEChNOLOGY AND BIOMOLECULAR SCIENCES, UNIVER- 

SITY Of NEW SOUTh WALES 

Visualising the Dynamics of the Interactome 

An important part of network biology is the visualisation of networks. Visualisation can provide many benefits 

not only to communicate findings but as part of experiments themselves. Some network elements such 

as hubs, just-in-time assembly and network motifs were discovered with the help of networks. Protein-protein 

interaction networks are typically built with interactions collated from many experiments. These networks 

are thus composite and show all possible interactions that might occur in a cell. However, these are static 

and ignore the dynamics of protein-protein interactions. 

We have adapted a Java-based, open source software package known as GEOMI to visualise protein interaction 

data in 3 dimensions. In addition we have developed the capability for the software to co-visualise 

3-dimensional networks with non-traditional forms of interaction data (‘integrated networks’). In particular 

the integration of abundance-based information, specifically time course gene expression data, permits the 

construction of 4-dimensional visualisations. This allows the ‘dynamic’ range of protein abundance to be 

visualised with experimental time, in the context of the protein interactions. The software and demonstration 

videos are available at www.systemsbiology.org.au/downloads_geomi.html. 

We will demonstrate the utility of the software in visualising post-translational modifications, transcriptional 

regulation and synthetic lethality data in association with pairwise protein interaction data. We will also 

present results on other experiments we have conducted. These will include experiments that investigate the 

how the cell regulates the interactions of singlish hub proteins (one or two interaction interfaces, but many 

interaction partners). This is done in the context of the yeast cell cycle. 


MAREIKE WEIMANN*, JONAThAN WOODSMITh*, ARNDT GROSSMANN, zIYA özKAN, PETRA BIRTh, 

DAVID MEIERhOfER, SASChA SAUER, ULRICh STELzL 

OTTO-WARBURG LABORATORY, MAx-PLANCK INSTITUTE fOR MOLECULAR GENETICS (MPIMG), BERLIN 

A Y2H-seq approach to define the protein methyltransferase interactome 

Protein methylation, in particular on arginine and lysine residues, is a physiologically important post translational 

modification (PTM). Whilst a large number of human methyltransferases (PMTs), potential demethylases 

(DeMs) and methyl-recognition domain containing genes are annotated in the human genome, 

systematic characterisation of this PTM remains poorly developed. Canonically studied with regards to its 

role epigenetic regulation, methylation components or substrates have been shown to have diverse subcellular 

localisations and molecular functions, indicating a wider role in cellular physiology. However, the lack 

of affinity reagents and appropriate tools to detect methyltransferase substrate pairs has largely hampered 

progress in defining the global role of non-histone protein methylation. 

Here we present a novel proteome wide Y2H protein interaction screening approach involving a 2nd generation 

sequencing readout that has a significantly improved sensitivity in comparison to our state of the art 

Y2H matrix screening protocol. Furthermore, the sequencing readout provides a quantitation that correlates 

very well with the retest success rate, indicative of the quality of the PPI information. Importantly, the workflow 

presented here allows for rapid scalability using advances in sequencing technology to enable Y2H-seq 

to accelerate large scale interactome mapping efforts. 

We applied the Y2H-seq method to comprehensively screen proteins involved in either methylation or 

demethylation. We present a network of >500 interactions involving 22 PMTs or putative DeMs and 324 

potential methylation substrates. The network is validated using co-IP experiments and will serve as a major 

informational resource to define cellular roles of protein methylation Furthermore, 7 candidate proteins are 

characterized with respect to novel R and K methylation sites using a mass spectrometry approach, highlighting 

the utility of the network to identify enzyme-substrate pairs. 


MOhAMMAD MOBAShIR AND DR. TILO BEYER 

INSTITUTE Of MOLECULAR AND CLINICAL IMMUNOLOGY OTTO-VON-GUERICKE UNIVERSITY LEIPzIGER- 

STRASSE 44 39120 MAGDEBURG GERMANY CONTACT: MOhAMMAD.MOBAShIR@MED.OVGU.DE AND 

TILO.BEYER@MED.OVGU.DE 

Unraveling of Network Motifs in Signal Transduction Networks Using Evolutionary Approach 

Living organisms control their behavior and cellular function by propagating signals across multiple levels. 

In T-cell signaling, the receptor, after receiving the signal activates downstream molecules that transmit the 

signal to the nucleus, in order to control cellular functions such as proliferation, differentiation, and apoptosis. 

During this process the change in the protein-levels, post-translational modification of the proteins, and 

interaction strengths affect the final response of the cell. To investigate possible design priciples we developed 

an in-silico model using evolutionary algorithm and ordinary differential equations. From our current 

simulations, we found that (i) kinetics of the final response of signaling pathway is predominantly controlled 

by the expression levels of proteins, (ii) the variations in protein-levels within the cell plays critical roles in 

controlling the kinetic behavior of the cell and consequently the cellular functions, and (iii) an increase in 

the interaction strengths up to a certain level leads to strong activation patterns, i.e. in the cells become 

sensitive to stimuli. Thus, it appears that the kinetic properties of the signaling network mainly determine 

the response threshold of a cell while the quantitative behavior is controlled by the expression levels of the 

proteins. 


GRzEGORz SLODKOWICz1, ChRISTOPhER T. WORKMAN1, OLGA RIGINA1, hANS-hENRIK STæRfELDT1, 

KRISTOffER RAPACKI1, SøREN BRUNAK1, KASPER LAGE hANSEN1, 2 

1 CENTER fOR BIOLOGICAL SEqUENCE ANALYSIS, TEChNICAL UNIVERSITY Of DENMARK, 2800 KGS. 

LYNGBY, DENMARK 

2 PROGRAM IN MEDICAL AND POPULATION GENETICS, BROAD INSTITUTE, CAMBRIDGE, USA 

Prioritization of causal disease genes with InWeb, the inferred human interactome 

Protein-protein interactions are an important tool for understanding cellular processes, in particular inferring 

protein function, disease-gene finding and predicting protein complex co-membership. Several genomewide 

studies provide direct evidence for physical interactions in human but far more data is available for 

other species, in particular S. cerevisiae and D. melanogaster. In the IntAct database, for instance, out of 

292,000 reported interactions, only 77,000 (26%) are measured directly in human. 

We present InWeb, the human inferred interactome, a database aggregating interactions from BIND, 

ConsensusPathDB, DIP, DOMINO, GRID, HPRD, I2D, InnateDB, IntAct, MINT and others. Interactions 

from other model organisms are transferred to human by orthology. The aggregated interactions are then 

rigorously filtered, scored and validated against a gold standard of high confidence interactions. In total, 

InWeb integrates over 10 million redundant interactions from 21 databases. 

Earlier versions of InWeb have been previously used in several studies, including an integrative analysis of 

protein complexes implicated in genetic disorders (Lage, Karlberg et al., Nature 2007), a disease chemical 

biology database (Taboureau, Nielsen et al., Nucleic Acids Research 2011), a novel approach to SNP mapping 

(Rossin, Lage et al, PLoS Genetics 2011) and others. Here we present InWeb version 5 which provides 

a state-of-the-art view of the human interactome. 


KATRIN SAMEITh1, MARIAN GROOT KOERKAMP1, MARIEL BROK1, DIK VAN LEENEN1, NAThALIE BRA- 

BERS1, ChEUK KO1, TINEKE LENSTRA1, JORIS BENSChOP1, SANDER VAN hOOff1, BEREND SNEL2, 

PATRICK KEMMEREN1, fRANK hOLSTEGE1 

1MOLECULAR CANCER RESEARCh, UNIVERSITY MEDICAL CENTRE UTREChT, UNIVERSITEITSWEG 100, 

3584 CG UTREChT, ThE NEThERLANDS 2DEPARTMENT Of BIOLOGY, UTREChT UNIVERSITY, PADUALAAN 

8, 3584 Ch UTREChT, ThE NEThERLANDS. 

An atlas of genetic interactions between gene specific transcription factors in 

Saccharomyces cerevisiae 

Transcription plays a key role in cellular processes and its regulation is paramount for a cell’s homeostasis. 

Environmental cues, transmitted through signaling pathways, cause activation or repression of gene specific 

transcription factors (GSTFs). How GSTFs regulate each other and how they combine to regulate transcription 

across the genome are important research questions. To this end, we systematically measure the effect 

of deleting each individual GSTF on gene expression genome-wide. So far, a set of 183 gene expression 

profiles has been generated. These profiles show that around half of all GSTF deletions do not result in any 

transcriptional changes. This can partly be explained by redundancy relationships between GSTFs, whereby 

the loss of one GSTFs is compensated by the presence of another GSTF. To study GSTF redundancy 

relationships, we selected 98 GSTF pairs. The results demonstrate that gene expression profiling of double 

deletion mutants allow for detailed understanding of combinatorial control through GSTF pairs. Regulatory 

circuitries and redundancy relationships between GSTFs are investigated through the determination of genes 

that are directly affected in transcription upon the deletion of one GSTF, or upon the combined deletion 

of two GSTFs. To support novel relationships found between pairs of GSTFs, we are currently examining 

how DNA binding of one GSTF is altered in the deletion of its partner GSTF. 


ChRISTOf WINTER1, GLEN KRISTIANSEN2, STEPhAN KERSTING3, JANINE ROY1, DANIELA AUST4, 

ThOMAS KNOSEL5, PETRA RüMMELE6, BEATRIx JAhNKE3, VERA hENTRICh3, fELIx RüCKERT3, MARCO 

NIEDERGEThMANN7, WILKO WEIChERT8, MARCUS BAhRA9, hANS J. SChLITT10, UTz SETTMAChER11, 

hELMUT fRIESS12, MARKUS B¨UChLER13, hANS-DETLEV SAEGER3, MIChAEL SChROEDER1, ChRISTIAN 

PILARSKY3, ROBERT GRüTzMANN3 

1 DEPARTMENT Of BIOINfORMATICS, BIOTEChNOLOGY CENTER, TEChNISChE UNIVERSIT ¨ AT DRESDEN, 

GERMANY, 2 INSTITUTE Of PAThOLOGY, UNIVERSIT ¨ ATSSPITAL z¨ URICh, zURICh, SWITzERLAND, 3 

DEPARTMENT Of VISCERAL, ThORACIC, AND VASCULAR SURGERY, UNIVERSITY hOSPITAL CARL GUSTAV 

CARUS, TEChNISChE UNIVERSIT ¨ AT DRESDEN, GERMANY, 4 INSTITUTE Of PAThOLOGY, UNIVERSITY 

hOSPITAL CARL GUSTAV CARUS, TEChNISChE UNIVERSIT ¨ AT DRESDEN, GERMANY, 5 INSTITUTE Of PA- 

ThOLOGY, UNIVERSITY Of JENA, GERMANY, 6 INSTITUTE Of PAThOLOGY, UNIVERSITY Of REGENSBURG, 

GERMANY, 7 DEPARTMENT Of SURGERY, UNIVERSITY hOSPITAL MANNhEIM, GERMANY, 8 DEPARTMENT 

Of PAThOLOGY, ChARIT ´ E, BERLIN, GERMANY, 9 DEPARTMENT Of SURGERY, ChARIT ´ E, BERLIN, 

GERMANY, 10 DEPARTMENT Of SURGERY, UNIVERSITY hOSPITAL REGENSBURG, GERMANY, 11 DEPART- 

MENT Of SURGERY, UNIVERSITY hOSPITAL JENA, GERMANY, 12 DEPARTMENT Of SURGERY, TEChNIS- 

ChE UNIVERSIT ¨ AT MUNChEN, GERMANY, 13 DEPARTMENT Of SURGERY, UNIVERSITY Of hEIDELBERG, 

GERMANY 

Google goes cancer: Improving outcome prediction for cancer patients by network-based ranking of marker 

genes 

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors 

has received increasing interest in the past decade. Accurate predictors of out¬come and response to 

therapy could be used to personalize and thereby improve therapy. However, state of the art methods used 

so far often found marker genes with limited predic¬tion accuracy, limited reproducibility, and unclear biological 

relevance. To address this problem, we developed a novel computational approach to identify genes 

prognostic for outcome that couples gene expression measurements from primary tumor samples with a 

network of known relationships between the genes. Our approach ranks genes according to their prognostic 

rel¬evance using both expression and network information in a manner similar to Google’s Page-Rank. We 

applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, 

and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state 

of the art methods, such as Pearson correlation of gene expression with survival time, we improve the 

prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and 

Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using 

immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived 

from our candidate markers were independently predictive of outcome and superior to established clin¬ical 

prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual 

tumors grows rapidly, our algorithm meets the need for powerful computa¬tional approaches that are key to 

exploit these data for personalized cancer therapies in clinical practice. 


MARKO LAAKSO, RIKU LOUhIMO, SAMPSA hAUTANIEMI 

COMPUTATIONAL SYSTEMS BIOLOGY LABORATORY, RESEARCh PROGRAMS UNIT, GENOME-SCALE BIOL- 

OGY, AND INSTITUTE Of BIOMEDICINE, BIOChEMISTRY AND DEVELOPMENTAL BIOLOGY, UNIVERSITY Of 

hELSINKI 

Predicting drug targets for individual tumour sample 

The identifcation of therapeutic targets is a major challenge in cancer as the pattern of mu¬tations varies 

between individual tumours and during their progression. We have addressed this question by focusing on 

the known hallmarks of cancer and by tracing them back to the driving genes independently for each patient 

sample. We have analysed over 580 breast cancer samples from The Cancer Genome Atlas. The gene expression 

profle of each sample was frst converted to a vector of diferentially expressed genes (DEG), which 

in turn was transformed to a vector of afected biological processes representing the hallmarks. The DEGs 

that were now mapped to the afected processes were next estimated for their therapeutic relevance. We 

checked if drugs were already available for them and how likely the restoration of the gene function would 

restore the target process. 

The computational framework that has been used for the analysis is freely available at http: //csbi.ltdk. 

helsinki.fi/moksiskaan/. We believe an automated in silica pipeline, which takes in gene expression data 

is an efcient way of predicting therapeutic candidates. Our method considers many-to-many relationships 

between drugs, their targets and the processes driven by these targets and is readily applicable for the 

combinations of drugs. 


JAKOBSEN JS, WAAGE JE, PORSE BT 

1BRIC, UNIVERSITY Of COPENhAGEN AND fINSEN LABORATORY, RIGShOSPITALET, OLE MAALøES VEJ 5, 

DK¬2200 COPENhAGEN, DENMARK 

A C/EBP binary switch: Transcriptional networks behind the dynamic balancing of cell proliferation and 

metabolism in liver regeneration 

Understanding liver regeneration at a fundamental level may open up new opportunities in regenerative medicine 

for this crucial organ, for which donation falls dramatically short. During the regenerative process, a 

complex network of transcriptional regulators ensures the dynamic balancing between compensatory growth 

and metabolic functions. Central among the regulators are the bZIP transcription factors (TFs) C/EBPᴅ and 

C/EBPᴅ. C/ebpᴅ is essential for hepatocyte differentiation and correct expression of key metabolic enzymes, 

while C/ebpᴅ is required for a full mitogenic response to liver injury. However, the precise and global 

role of these factors in liver regeneration has not been comprehensively mapped on a genomic scale so far. 

Here, we employ a time course of chromatin-immunoprecipitation followed by deep sequencing (ChIP-seq) 

to map the binding of C/EBPᴅ, C/EBPᴅ and RNA-POL2 in the mouse model. Noticeably, the protein level 

ratios of C/EBPᴅ and C/EBPᴅ are precisely reflected in global binding patterns. Two major ‘timed’ binding 

patterns are clearly associated with expression of distinct sets of genes, related to either metabolic function 

or cell cycle. Using available databases (Uniprobe, Transfac and Jaspar) we generated an extensive list of 

TFs potentially involved in either the metabolic or the proliferative phase of liver regeneration. We identify 

distinct panels of feed-forward loops associated with C/EBP patterns involving TFs such as circadian clock 

regulators, E2fs, Klf-factors, Lxr/Fxrs, Fox-members. A follow-up ChIP-seq examination of EGR1¬binding 

surprisingly showed overlap with the C/EBP pattern under¬represented in EGR1¬binding sequence. 

Using mice lacking C/ebpᴅ, we demonstrate two binding modes of EGR1 in vivo; direct to its cognate DNA 

sequence or assisted/indirect via C/EBPᴅ. This suggests a global scale model for differential regulation of 

EGR1¬target genes: with or without dependency on C/EBPs for metabolic or cell cycle regulated genes, 

respectively. 

In summary, our study shows how ChIP-seq and genomics analyses can be used on a mammalian in vivo 

system to elucidate dynamic transcriptional networks required for appropriate physiological gene regulation, 

exemplified by the fine-tuned balancing of cell proliferation and homeostasis in liver regeneration. 


ANDREI zINOVYEV123, INNA KUPERSTEIN123, DAVID COhEN123, SIMON fOURqUET123, LAURENCE 

CALzONE123, STUART POOK4, PAOLA VERA-LICONA123, ERIC BONNET123, DANIEL ROVERA123, EM- 

MANUEL BARILLOT123 

(1) INSTITUT CURIE, 26 RUE D’ULM, f-75248 PARIS fRANCE, (2) INSERM, U900, PARIS, f-75248 

fRANCE, (3) MINES PARISTECh, fONTAINEBLEAU, f-77300 fRANCE, (4) SYSRA, f-75248 PARIS fRANCE 

Towards of Atlas of Cancer Signaling Networks 

Basis for the Institut Curie systems biology platform for data analysis and interpretation 

Cancer is a complex disorder that can be seen as a systems biology disease. There are numerous cell 

signaling mechanisms that are dysregulated in cancer. To understand involvement of different mechanisms 

in the disease, systematic representation and analysis of the processes are needed. To achieve the goal, 

we are currently in the process of creating the Atlas of Cancer Signaling Networks (ACSN), where signaling 

mechanisms are represented as comprehensive maps amenable for computational and mathematical analysis 

(http://acsn.curie.fr). Currently ACSN consists of four maps: cell-cycle regulation by RB-E2F, DNA repair, 

Cell Cycle and checkpoints, Apoptosis and energy metabolism and Cell Survival signaling networks. We 

will include in ACSN additional maps for Epithelia-Mesenchimal Transition (EMT), Telomeres maintenance, 

Centrosome maintenance, DNA replication and Inflammatory processes. We have developed a Google 

Map-based tool NaviCell (http://navicell.curie.fr) for exploring large signaling networks. The tool is characterized 

by the unique combination of three essential features: (1) map navigation based on Google Maps 

engine, (2) semantic zooming for viewing different levels of details of the map and (3) integrated web-based 

blog for collecting the community curation feedbacks. NaviCell facilitates curation of molecular interactions 

maps by the community helping to update and maintain maps in an interactive and user-friendly fashion. We 

have developed a series of tools for network analysis (BiNoM, OCSANA, etc), which enable structural analysis, 

target identification and integration and analysis of high-throughput data using ACSN maps. 


fLORIAN MARTIN1; TY M. ThOMSON2; ALAIN SEWER1; DAVID A. DRUBIN2; CAROLE MAThIS1; DExTER 

PRATT2; JULIA hOENG1; MANUEL C. PEITSCh1 

1PhILIP MORRIS INTERNATIONAL R&D, PhILIP MORRIS PRODUCTS S.A., NEUChâTEL, SWITzERLAND 

2SELVENTA, ONE ALEWIfE CENTER, CAMBRIDGE, MA, USA 

Assessment of Network Perturbation Amplitude by Applying High-Throughput Data to Causal Biological 

Networks 

BACKGROUND: High-throughput technologies have the potential to elucidate the biological impact of 

disease, drug treatment, and environmental agents on humans. An ongoing challenge has been the analysis 

of the generated data to more accurately characterize the perturbed biological processes at the mechanistic 

level. Here, a new approach was taken, which uses prior knowledge of cause and effect relationships 

structured into biological network models describing specific processes, such as inflammatory signaling or 

cell cycle progression. 

RESULTS: Four complementary methods and their companion statistics were devised to quantify treatmentinduced 

activity changes in processes described by network models. This approach is called Network 

Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed 

for biological network models. The NPA methods were tested on two transcriptomic data sets: normal 

human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFᴅ, and 

HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against 

network models representing different aspects of inflammatory signaling and cell cycle progression. The 

NPA scores successfully quantified the amplitude of the TNFᴅ-induced perturbations in treated NHBE cells, 

as confirmed by NF-ᴅB nuclear localization measurements. In HCT116 cells, the degree and specificity to 

which CDK-inhibition affected cell cycle and inflammatory signaling were also meaningfully determined by 

the NPA results. 

CONCLUSION: The NPA scoring method leverages high-throughput measurements and a priori knowledge 

in the form of causal network models to characterize the activity change for a broad collection of biological 

processes. Applications of this framework include quantitative assessment of the biological impact caused 

by drug treatments, environmental factors, or toxic substances. 


ALESSANDRO ORI1, AMPARO ANDRES-PONS1, NICCOLò BANTERLE1, MURAT ISKAR1, CLAUDIA ES- 

ChER2, OLIVER RINNER2, PEER BORK1, EDWARD A. LEMKE1 AND MARTIN BECK1 

1 STRUCTURAL AND COMPUTATIONAL BIOLOGY UNIT, EUROPEAN MOLECULAR BIOLOGY LABORATORY, 

hEIDELBERG, GERMANY. 2 BIOGNOSYS AG, SChLIEREN, SWITzERLAND. 

Compositional remodelling of the nuclear pore complex 

Structure determination of nuclear pores complexes (NPCs) turned out to be challenging due to their positioning 

in a membranous environment, their sheer size and their intricate composition. In order to generate 

structural models of the human NPC, data obtained by several different structure determination techniques 

have to be integrated into a common framework. To achieve this goal, the knowledge of human nucleoporin 

stoichiometry is critical. We have absolutely quantified human nucleoporins using targeted proteomics in 

combination with a heavy labeled reference peptide strategy. We used selected reaction monitoring (SRM) 

to establish multiple independent measurements per protein across several biological replicates and different 

biological states which allowed us to accurately define the absolute composition of fully assembled 

NPCs. Our data demonstrate that nucleoporin abundance is structured in discreet steps reflecting the 

geometrical organization of subcomplexes into NPC scaffold structure. The integration of these data with 

complementary techniques including fluorescence and electron microscopy enable deciphering the compositional 

inventory of the human NPC and thus provide crucial information for the generation of structural 

models at atomic resolution. We have furthermore absolutely quantified NPC composition in different human 

cell lines and identified several nucleoporins that are differentially incorporated into the NPC. Our data imply 

that the human nuclear pore is remodeled in a cell-type specific manner. 


ALI ALTINTAS1, ChRISTOPhER WORKMAN1 

1TEChNICAL UNIVERSITY Of DENMARK (DTU), DEPARTMENT Of SYSTEMS BIOLOGY, CENTER fOR BIO- 

LOGICAL SEqUENCE ANALYSIS (CBS) 

Growth Physiology and Time Dependent Metabolic Remodelling of Saccharomyces cerevisiae under Oxidative 

Stress 

Oxidative stress caused by an excess of reactive oxygen species (ROS), is known to damage cellular 

components and triggers a number of signalling pathways that contribute to a remodelling of transcription, 

translation and metabolism. ROS are produced during normal aerobic physiological processes or when 

cells are exposed to oxidizing agents such as hydrogen peroxide (HP). Cells have to sense ROS to trigger 

a response against its harmful effects. Protein kinases (PK) and protein phosphatases (PP) regulate this 

sense-response switch to create adaptation to oxidative stress. 

The objective of the project was to investigate the time-dependent growth effects of oxidizing environments 

on PK and PP deletion mutants of budding yeast. To do this, 38 different PK and PP mutants and two 

wild-type strains of S. cerevisiae were investigated for oxidative stress responses. These 38 mutants were 

selected for their known activities in both general/oxidative stress response and DNA damage response. HP 

was used to create an oxidizing environment at a number of different concentrations (0.25, 0.50, 1.0 and 2.0 

mM) ranging from mild to moderate stress. Growth physiology and the time dynamics of the stress response 

were characterized in a microfermentation system (m2p BioLector) for all deletion strains. 

The microfermentation platform allowed us to measure biomass with high time resolution (3 minute sample 

rate). Important metabolites of carbon metabolism were measured by HPLC before, during and after the 

diauxic shift (DS) of stressed and un¬stressed S. cerevisiae. The metabolic delay of DS after stress provided 

a quantitative measure of strain sensitivity. Measured DS delays, due to growth arrest and/or a decrease 

in the metabolic activity with increasing HP, allow for the detection of phenotypic variation in mild stress 

conditions. 

The most important growth physiology findings relate to the opposing mechanism of PK and PP, the central 

activity of OCA1 gene among responses of oxidative stress, and also the investigation of slow growing mutants: 

bud32∆, vps15∆, ptc1∆ and oca1∆. 


AGNIESzKA SzWAJDA1, LEENA KARhINEN1, SAWAN KUMAR JhA1, TEA PEMOVSKA1, BhAGWAN YA- 

DAV1, KRISTER WENNERBERG1, TERO AITTOKALLIO1 

1 INSTITUTE fOR MOLECULAR MEDICINE fINLAND, fIMM, UNIVERSITY Of hELSINKI, hELSINKI, fINLAND 

Identification of molecular drivers in breast cancer using kinome-wide drug-target network and drug 

sensitivity screening 

To systematically study how kinase inhibitors and their cellular targets function together as networks, we 

integrated three recent large-scale studies of kinase inhibitor specificities1-3, along with drug binding 

information from the ChEMBL database and individual experiments, into a quantitative data matrix for a 

total of 1679 chemical compounds and 445 kinase targets. When using relatively stringent thresholds for 

biochemical drug inhibition levels (drug dissociation constant and IC50 34% kinase activity 

inhibition at 0.5µM), the resulting drug-target network (26416 interactions between 1464 drugs and 444 

kinases) exhibited approximately a scale-free property with a degree distribution ( ) . Drugs with highly 

correlated target profiles (Spearman correlation > 0.5) were then linked to construct a drug-drug network. 

The interconnected drugs turned out to have similar chemical structure and mode of action, demonstrating 

that such global drug networks could be used to reveal mechanisms of drug action or suggest effective drug 

combinations. 

In a specific case study, we elucidated the signaling pathways driving 10 breast cancer cell lines by correlating 

cellular viability inhibition measurements (EC50 values) of 35 kinase inhibitors with the biochemical drug 

inhibition information from the drug-target network. The analysis was based on the assumption that kinase 

inhibitors targeting the essential kinases should effectively decrease cell numbers. As a result, we obtained 

a ranking of kinases according to their likelihood of being the key molecular drivers in each cell line. The 

ERBB2/HER2 receptor tyrosine kinase, a well-known oncogenic driver in a subset of breast cancers, served 

as a positive control and was found among the top hits in cell lines known to be driven by this kinase, but not 

in others. Other breast cancer- related kinases, such as PTK6/BRK or members of the PI3K pathway, were 

also highly ranked in relevant cell lines. The significance of the predicted driver kinases for cancer progression 

will be experimentally tested with siRNA-mediated knockdowns and other kinase inhibitors or their 

combinations. 

References: 

1. Davis et al., “Comprehensive analysis of kinase inhibitor selectivity”, Nat Biotechnol. 2011; 29:1046-1051. 

2. Metz et al., “Navigating the kinome”, Nat Chem Biol. 2011; 7:200-202. 

3. Anastassiadis et al., “Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor 

selectivity”, Nat Biotechnol. 2011; 29:1039-1045. 


NAThANIEL STANLEY, GIANNI DE fABRITIIS 

UNIVERSITAT POMPEU fABRA (UPf), RESEARCh UNIT ON BIOMEDICAL INfORMATICS (GRIB), PARC DE 

RECERCA BIOMèDICA DE BARCELONA (PRBB), DR. AIGUADER 88, 08003 BARCELONA, SPAIN 

Kinetic, Thermodynamic, and Conformational Characterization of Biological Network Components Using 

Molecular Dynamics: Towards better understanding and better drugs 

The full description of the kinetics and thermodynamics of interacting components in a biological system are 

critically important for us to accurately represent and understand them. With recent advancements in both 

methodology and hardware, molecular dynamics simulations are at a point where they can now be used 

to accurately calculate such data for those components where traditional experimental techniques prove 

inadequate or prohibitive. In addition, conformational changes in the structure of interacting molecules can 

be assessed to high degree of precision. Finally, target conformations can be subjected to high throughput 

fragment docking in order to build novel inhibitors and allosteric modulators. We present our work in this vein 

done thus far on multiple components of the endocannabinoid system. 


LUISIER R, LEMPIÄINEN h, MUELLER A, hAhNE f, UNTERBERGER E, BOLOGNANI f, BALWIERz P, BRAE- 

UNING A, DUBOST V, GOODMAN J, ChIBOUT SD, ThEIL D, hEARD D, MOULIN P ,GRENET O, SChWARz 

M, MOGGS J, VAN NIMWEGEN E, TERRANOVA R 

Prediction of early regulatory networks underlying liver non-genotoxic carcinogenesis upon xenobiotic 

exposure. 

Non-genotoxic carcinogenesis (NGC) is a common drug-induced toxicity that occurs in rodent models, for 

which no well-validated short-term assays exist. Here we have used genome-wide transcriptome profiling 

and computational modeling to investigate the temporal sequence of events and identify potential gene regulatory 

networks involved in NGC using a well-characterized rodent model for liver tumor promotion. 

Singular value decomposition was used to identify and quantify early dynamics of gene expression changes 

in mouse liver upon exposure to the non-genotoxic carcinogen phenobarbital (PB). Applying Motif Activity 

Response Analysis (MARA), which models gene expression dynamics in terms of predicted cis-regulatory 

sites, led to the identification of transcription factors perturbed upon PB treatment and potentially responsible 

for specific kinetic responses to treatment. Genetic analyses revealed the relation to cancer-relevant 

pathways of candidate NGC biomarkers and enabled the disentangling of tumor promotion pathways from 

the xenobiotic response. 

Altogether our results indicate that in vivo gene expression changes upon PB exposure are not linearly 

dependent on time, consistent with possible changes in tissue composition over time. Gene regulatory 

networks underlying tumor promotion upon PB are not fully “active” at early stages of tumor promotion, 

“inactive” in tumor-resistant mouse models and “active” in promoted end-tumors. 

Our data highlight novel potential early mechanisms and pathways for liver tumor promotion, providing new 

opportunities for assessing the carcinogenic potential of environmental cues including novel therapeutics. 


ROSSUKON ThONGWIChIAN 1*, hONOR ROSE 1*, fRANCOIS-xAVIER ThEILLET 1, JONAS KOSTEN1, AL- 

ExANDER DOSE 2, UWE BENARY 3, DIRK SChWARzER 2, JANA WOLf 3, PhILIPP SELENKO 1. 

1DEPARTMENT Of NMR-ASSISTED STRUCTURAL BIOLOGY IN-CELL NMR GROUP AND 2DEPARTMENT Of 

ChEMICAL BIOLOGY, LEIBNIz INSTITUTE fOR MOLECULAR PhARMACOLOGY (fMP), BERLIN, GERMANY. 

3MAThEMATICAL MODELING Of CELLULAR PROCESS, MAx DELBRUECK CENTER fOR MOLECULAR MEDI- 

CINE (MDC), BERLIN, GERMANY. ROSE@fMP-BERLIN.DE * EqUAL CONTRIBUTION. 

Systems level profiling of cellular kinase activities by multiplexed NMR spectroscopy. 

Complex kinase activities lie at the heart of most eukaryotic signaling networks. Here we introduce a novel 

concept to directly monitor cellular kinase activities by high-resolution NMR spectroscopy. Using isotopelabeled 

Kinase Activity Reporters (KARs) and multiplexed NMR readouts, we follow the activities of up to 15 

different, cellular kinases in parallel, and within a single set of time-resolved NMR experiments. Quantitative 

in situ monitoring of reversible kinase and phosphatase activities-, in connection with multiplexed kinase 

inhibitor screening-, enables high-accuracy mathematical modeling of cellular signal response behaviors, 

which, in turn, can be directly applied to network medicine approaches. 


Negative Feedback is a Subordinated Control Structure in Signal Transduction 

Protein signaling and metabolic systems often contain multiple feedback interactions which are a prominent 

motif of regulation. We hypothesized that feedback interactions influence concentration range, timing, signal 

transmission strength and the appearance of transients in a predictable and computable manner. In steadystate, 

feedback interactions are resolved, i.e. cyclic structures can be eliminated and subsumed by pure 

input-output relations. We use this fact in order to systematically analyze the effect of a multiple negative 

feedback structure (figure). We employ a tool for steady-state analysis that operates over input ranges and 

calculates systemic delays ([1]). 

We find that negative feedback universally shortens delays and limits concentration ranges. Furthermore, 

it linearizes signal transmission, increases the appearance of transients, but also shortens their decay 

times. In other words, the directed path of signal transduction from receptor to cytosolic and nuclear targets 

is adorned by negative feedback interactions, such that signal transduction becomes better controlled in 

timing, concentration and by reduction of nonlinearity. An additional property, the increased appearance of 

transients, shows that below the time resolution of steady-state analysis, feedback has an additional role 

in providing for fast interactions. However, in steady-state analysis, negative feedback has a subordinated 

control function compared to a primary role of signal transduction along pathways. 

[1] Scheler, G.: Transfer Functions in Signal Transduction: Applying a Protein Signaling Function (PSF) to a 

Model of Striatal Neural Plasticity. Plos Comp Biol, submitted. 


SEBASTIAN STUDENT, MAGDALENA SKONIECzNA, JOANNA RzESzOWSKA 

BIOSYSTEMS GROUP, INSTITUTE Of AUTOMATIC CONTROL, SILESIAN UNIVERSITY Of TEChNOLOGY, 44- 

100 GLIWICE, POLAND 

Osteopontin expression in human colon cancer cells 

Osteopontin is a phosphorylated glycoprotein which plays a crucial role in many normal physiological as 

well as pathological cellular processes. It was originally identified as a important factor in bone remodeling, 

cell adhesion, migration and cell survival, activation, chemotaxis and apoptosis. Osteopontin is an important 

factor in the progression of several cancer types and in various aspect of metastasis. Regulation of the 

osteopontin gene is only incompletely understood, and cell types may differ in the regulatory mechanisms of 

the gene and the roles of osteopontin. 

In this study we concentrated on identifying the relationships between osteopontin and the apoptosis signaling 

pathway in a model of colon cancer. We used different oligonucleotide microarray datasets to study the 

potential role of osteopontin, mainly in the p53-dependent apoptosis signaling pathway. Experiments were 

conducted with the Affymetrix platform on HCT 116 wild-type and p53-mutated colon cancer cell lines. The 

cells were irradiated with 4 Gy of ionizing radiation, and non-irradiated cells were used as a control group. 

Ionizing radiation induces the production of reactive oxygen species which play an important role in apoptotic 

cell death, and by inducing DNA damage activates the p53-dependent apoptosis signaling pathway which 

can cause cell cycle arrest and apoptosis. Not all interactions between apoptosis-related genes that are 

transcriptionally regulated by p53 have been identified. 

The computational analysis was carried out using the PLS (partial least squares)-based gene selection 

method, which enables assignment of biological meanings for the genes with the highest weights in the PLS 

model. The PLS method, in contrast to the PCA (principal component analysis) criterion based on maximization 

of the variance of a linear combination of genes, extracts components by maximizing the sample 

covariance between the class variable and a linear combination of genes. The information for genes included 

in components described by PLS can be directly related to the biological meaning by this analysis. The 

relationship between the expression level of the osteopontin gene and p53 was confirmed by real time PCR 

experiments on the same cell lines. The list of genes involved in apoptosis signaling pathways was prepared 

based on KEGG, Biocarta and other published apoptosis signal interaction models. 

Our results show that the osteopontin expression level depends strongly on the p53 status of colon cancer 

cells, and is lower in the HCT116 p53-mutated cell line. We also observed an inverse correlation with expression 

of the PTEN gene, which has a growth suppressive effect through its inhibition of the phosphatidylinositol 

3-kinase (PI3K) pathway. It is known that this pathway is involved in promoting cell growth, survival 

and tumorigenesis when overstimulated. Our results suggest that osteopontin plays an important role in 

PI3K regulation, and in this way can affect the apoptosis pathway. This analysis is the first step in the investigation 

of osteopontin’s role in the apoptosis pathway, which will require more detailed investigation. 

This work was supported by grants No. NN514411936 and N N518497639 from MNiSW and ZIS ¬BK/ 274 

/2011 t.13 from Silesian University of Technology in Gliwice. 


ANNA hEGELE 1,*, ATANAS KAMBUROV 1,*, ARNDT GROSSMANN 1, ChRYSOVALANTIS SOURLIS 1, SYL- 

VIA WOWRO 1, MAREIKE WEIMANN 1, CINDY L. WILL 2, VLAD PENA 2, REINhARD LühRMANN 2, ULRICh 

STELzL 1,# 

1 MAx-PLANCK INSTITUTE fOR MOLECULAR GENETICS (MPI-MG), OTTO-WARBURG LABORATORY, BER- 

LIN, GERMANY. AND 2 MAx-PLANCK INSTITUTE Of BIOPhYSICAL ChEMISTRY (MPI-BPC), DEPARTMENT 

Of CELLULAR BIOChEMISTRY, GöTTINGEN, GERMANY. 

Dynamic protein-protein interaction wiring of the human spliceosome 

Pre-mRNA splicing is catalyzed by the spliceosome, a highly complex, dynamic and protein rich ribonucleoprotein 

complex (RNP) that assembles de novo on each intron to be spliced. During spliceosome assembly, 

activation, catalysis and disassembly, defined large RNP complexes are formed in an ordered, stepwise 

manner. More than 200 proteins copurify at one or more stages with human spliceosomes assembled on 

prototype pre-mRNAs in cellular extracts. To better understand protein -protein interactions governing splicing, 

we systematically investigated interactions between human spliceosomal proteins. A comprehensive 

Y2H interaction matrix screen generated a protein interaction map comprising 632 interactions between 196 

proteins. 242 interactions were found between spliceosomal core proteins, and largely validated by co-immunoprecipitation. 

To reveal dynamic changes in protein interactions, we integrated spliceosomal complex 

purification information with our interaction data and performed link clustering. These data, together with 

interaction competition experiments, suggest that during step 1 of splicing, hPRP8 interactions with SF3b 

proteins are replaced by hSLU7, positioning this second step factor close to the active site, and that the 

DEAH-box helicases hPRP2 and hPRP16 cooperate through ordered interactions with GPKOW. Our data 

provide extensive information about the spliceosomal protein interaction network and its dynamics. 

Reference: 

Hegele et al., Mol Cell. 2012 Feb 24;45(4):567-80. 


CAMILLE TERfVE & JULIO SAEz-RODRIGUEz, EMBL-EBI, WELLCOME TRUST GENOME CAMPUS, CAM- 

BRIDGE, CB10 1SD, U.K. 

Logic modeling of signalling networks with high-content phosphoproteomic data 

Cells are continuously exposed to signals, which need to be received, interpreted and propagated in an integrated 

manner to produce the appropriate response. This information processing is performed through the 

use of highly dynamic and context specific networks whose deregulation is often involved in the development 

of diseases. Mathematical modeling is a very useful tool to make sense of this complexity. Many different 

formalisms can be applied depending on the type of data available and the question to be answered. 

Logic models are a flexible yet computationally simple way to approach this problem. Cause-effect relationships 

in biological pathways can often be found in the literature, but rarely include specific gates, nor cell-type 

specific information. An approach to provide predictive power and context-specificity models was introduced 

in [1], by using perturbation data to train a Boolean model from a generic prior knowledge network. Here we 

present CellNOptR, and R/Bioconductor package that implements this method and extends it including a 

palette of logic formalisms that handle both quantitative and time-dependent aspects with various levels of 

complexity. 

We also using this as a framework to investigate the challenges and opportunities [2] associated with using 

mass spectrometry proteomics as a primary data collection technology to build models of signalling networks. 

References 

[1] J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, 

S. Klamt, and P. K. Sorger. Discrete logic modelling as a means to link protein signalling networks with 

functional analysis of mammalian signal transduction. Molecular Systems Biology, 5:331, 2009. PMID: 

19953085. 

[2] C. Terfve and J. Saez-Rodriguez. Modeling signaling networks using high-throughput phospho-proteomics. 

Advances in Experimental Medicine and Biology, 736:19–57, 2012. PMID: 22161321. 


ThOMAS SChLITT1, NIKOLAOS BARKAS1, ChRISTOPhER TEBBE2, fRAUKE SPRENGEL2, VOLKER AhL- 

ERS2, BENJAMIN LEhNE1 

1DEPARTMENT Of MEDICAL AND MOLECULAR GENETICS, SChOOL Of MEDICINE, KING’S COLLEGE LON- 

DON, GUY’S hOSPITAL, LONDON SE1 9RT, UNITED KINGDOM 

2DEPARTMENT Of COMPUTER SCIENCE, fACULTY IV, UNIVERSITY Of APPLIED SCIENCES AND ARTS hAN- 

NOVER, P.O. BOx 920251, 30441 hANNOVER, GERMANY 

De-novo pathway discovery for genes linked to complex diseases 

Genome-wide association studies (GWAS) proved successful in the identification of sequence variants 

associated with complex diseases. However, the necessity to apply very stringent thresholds to ensure genome-wide 

significance suggests many disease associated genes might be missed. We developed a novel 

approach to support the discovery of genes and pathways underlying complex diseases that is independent 

of previous pathway annotation. Based on data from GWAS we derive gene-specific p-values by mapping 

SNPs to genes controlling for confounding effects such as the number of SNPs per gene. We use additional 

biological information in the form of protein or gene networks to identify additional genes that might be associated 

with disease. Our hypothesis is that genes truly linked to the disease under study (true-positives) are 

localised in proximity in gene networks while genes not linked to the disease (false-positives) will be scattered 

randomly over the network. Our novel algorithm -Region Growing Analysis (RGA) -maps gene-specific 

p-values to molecular gene networks and identifies regions enriched for genes with association signal. 

These regions could span several densely connected subnetworks (modules or cliques) and do not have to 

be identical to known pathways. We demonstrate the utility of our RGA algorithm by applying it to proteinprotein 

interaction networks to identify regions associated with Crohn’s disease and Type-1¬diabetes. We 

are able to identify several candidate disease genes in addition to the genes identified in the GWAS studies 

we used to obtain the gene-specific p-values; many of these candidate genes have been confirmed by larger 

meta-analysis and functional studies, others we are currently validating experimentally. 


abStractS For PoSterS 

ANDREI zINOVYEV1,2,3*, INNA KUPERSTEIN1,2,3, EMMANUEL BARILLOT1,2,3 , AND WOLf-DIETRICh 

hEYER4 

1INSTITUT CURIE, 26 RUE D’ULM, f-75248 PARIS fRANCE 

2INSERM, U900, PARIS, f-75248 fRANCE 

3MINES PARISTECh, fONTAINEBLEAU, f-77300 fRANCE 

4DEPARTMENTS Of MICROBIOLOGY AND Of MOLECULAR AND CELLULAR BIOLOGY, UNIVERSITY Of CALIfORNIA, 

DAVIS, ONE ShIELDS AVENUE, DAVIS, CA 95616-8665 

Synthetic lethality within one pathway and cancer treatment 

A synthetic lethal interaction is usually stated when defects in two non-essential genes cause cell death. 

Synthetic lethality and synthetic dosage lethality studies in model organisms and human cells give hope 

to develop cancer drugs that would kill cancer cells very selectively: if a cancer cell has a characteristic 

deletion or amplification of a gene, then inhibiting or overexpressing another nonessential gene forming a 

synthetic interaction pair, will lead to specific lethality of cancer cells. For example, some breast cancers are 

characterized by loss-of-function mutation in BRCA1 gene involved in DNA repair. The PARP1 gene forms a 

synthetic lethal pair with BRCA1 in cellular models and, therefore, inhibitors of PARP1 for treating BRCA1deficient 

breast cancer were developed and went to clinical trials. 

The genes from a synthetic interaction pair are generally assumed functioning in two parallel and mutually 

compensatory pathways (multi-pathway Synthetic Lethality). However, several examples of synthetic lethal 

relationships involving genes implicated in the homologous recombination DNA repair pathway extend this 

paradigm. In this situation defects in two genes which function in the same pathway lead to cell death (single-pathway 

Synthetic Lethality). We explored the inherent system properties of such a genetic relationship 

using mathematical modeling. We found that three circumstances are pre-requisites for the single-pathway 

Synthetic Lethality scenario: reversibility of pathway steps, presence of a compensatory pathway and toxicity 

of at least one pathway intermediate. Further modeling revealed the potential contribution of synthetic dosage 

lethal interactions in such a genetic system. 

We discuss implications of single-pathway synthetic lethality on cancer treatment modalities acting through 

DNA damage. 

SPonSorS 

PLATINUM LEVEL 

PREMIUM LEVEL 

PARTNER LEVEL 

SUPPORTER LEVEL 

PROMOTIONAL LEVEL 


Ruedi Aebersold 

aebersold@imsb.biol.ethz.ch 

ETH Zürich 

Zürich 

CH 

Hassan Ahmed 

hahmed@eisbm.org 

EISBM 

Massy, France 

Ali Altintas 

ali@cbs.dtu.dk 

Center for Biological Sequence Analysis 

(CBS) 

Department of Systems Biology 

Technical University of Denmark 

(DTU) 

Lyngby, Denmark 

Emmanuel BARILLOT 

Emmanuel.Barillot@curie.fr 

Institut Curie 

PARIS, FRANCE 

Christopher Barnes 

barnes@imsb.biol.ethz.ch 

ETH Zürich 

Copenhagen, Switzerland 

Tilo Beyer 

tilo.beyer@med.ovgu.de 

Otto-von-Guericke University, Institute 

of Molecular and Clinical Immunology 

Magdeburg, Germany 

Willy Björklund 

willy.bjorklund@thermofisher.com 

Thermo Fisher Scientific 

Kungens Kurva, Sverige 

Blagoy Blagoev 

bab@bmb.sdu.dk 

SDU 

Odense, Denmark 

Nikolaj Blom 

blom@cbs.dtu.dk 

DTU - Technical University of Denmark 

Kongens Lyngby, Denmark 

Blake Borgeson 

borgeson@utexas.edu 

University of Texas at Austin 

Austin, US 

Susanne Brix 

sbp@cbs.dtu.dk 

DTU Systems Biology 

Kgs. Lyngby, Denmark 

Søren Brunak 

brunak@cbs.dtu.dk 

Center for Biological Sequence 

Analysis 


Andrea Califano 

califano@c2b2.columbia.edu 

Columbia University 

New York, MA, USA 

Gianni Cesareni 

cesareni@uniroma2.it 

University of Rome Tor Vergata 

Roma, Italy 

Joan Chang 

joan.chang@icr.ac.uk 

Institute of Cancer Research 

London, UK 

Sara Holm Christiansen 

sara@intomics.com 

Intomics 

Lyngby DK, Denmark 

Morten Colding-Jørgensen 

mcj@novonordisk.com 

Novo Nordisk A/S 

Søborg DK, Denmark 

Thomas Cox 

coxthomasr@gmail.com 

Biotech Research & Innovation Centre 

(BRIC) 

Copenhagen, Denmark 

Pau Creixell 

creixell@cbs.dtu.dk 

Cellular Signal Integration Group (C- 

SIG) - Center for Biological Sequence 

Analysis (CBS) - Department of Systems 

Biology - Technical University 

of Denmark (DTU) 


Simone Daminelli 

simone.daminelli@biotec.tu-dresden. 

de, Biotechnology Center, Technische 

Universitet Dresden 

Dresden, Germany 

Federico De Masi 

fdemasi@cbs.dtu.dk 

CBS - DTU 


ParticiPantS ParticiPantS 

Sol Efroni 

sol.efroni@biu.ac.il 

Bar Ilan University 

Ramat Gan, Israel 

Janine Erler 

Janine.erler@bric.ku.dk 

BRIC 


Stephan Feller 

stephan.feller@imm.ox.ac.uk 

WIMM, Oxford University 

Oxford, United Kingdom 

Mogens Fenger 

mogens.fenger@hvh.regionh.dk 

Hvidovre Univeristy Hospital 

Hvidovre, Denmark 

Robin Friedman 

robin.friedman@gmail.com 

Institut Pasteur 

Paris, France 

Krzysztof Fujarewicz 

krzysztof.fujarewicz@polsl.pl 

Silesian University of Technology 

Gliwice, Poland 

Adam Galuszka 

adam.galuszka@polsl.pl 



Christian Garde 

garde@cbs.dtu.dk 

Center for Biologcal Sequence Analysis 

(CBS), Department of Systems 

Biology, Technical University of 

Denmark (DTU) 


Anne Claude Gavin 

gavin@embl.de 

EMBL-Heidelberg 

Heidelberg, Germany 

Pier Federico Gherardini 

federico.gherardini@gmail.com 


Rome, Italy 

Apurv Goel 

a.goel@student.unsw.edu.au 

University of New South Wales 

Sydney, Australia 

Eng Lim Goh 

englim@sgi.com 

SGI 

USA 

Alexey Golstov 

a.goltsov@abertay.ac.uk 

Abertay University 

Dundee DD1 1HG, SCOTLAND 

Valborg Gudmundsdottir 

valborgg@gmail.com 

CBS 

Kemitorvet, Building 208 


Alejandro Guiliani 

guiliani@gmail.com 

BRIC 


Ramneek Gupta 

ramneek@cbs.dtu.dk 


Analysis 


Frederik Gwinner 

frederik.gwinner@pasteur.fr 


Paris,France 

René Normann Hansen 

rnha@novonordisk.com 


Søborg, Denmark 

V. Joachim Haupt 

joachim.haupt@biotec.tu-dresden.de 

Biotechnology Center, Technische 



Peter Henriksen 

peterhe@cpr.ku.dk 

Copenhagen University, CPR 

København, Denmark 

Markus Herrgard 

herrgard@biosustain.dtu.dk 

DTU Biosustainability 

Hørsholm, Denmark 

Sanna Herrgard 

herrgard@cbs.dtu.dk 

Danish Technical University 

Kongens Lyngby, Denmark 

Carl-Magnus Høgerkorp 

cmgh@novonordisk.com 

Novo Nordisk 

Måløv, Denmark 

Heiko Horn 

heiko.horn@cpr.ku.dk 

NNF Center for Protein Research, 

Faculty of Health Sciences, University 

of Copenhagen 

København, Danmark 

Martin Hornshaw 

martin.hornshaw@thermofisher.com 

Thermo 

Hemel Hempstead, UK 

Luisa Hugerth 

luisa.hugerth@scilifelab.se 

Science for Life Laboratory 

Solna, Sweden 

Peter Husen 

phusen@bmb.sdu.dk 

Department of Biochemistry and Molecular 

Biology, University of Southern 

Denmark 

Odense M, Denmark 

Ruth Hüttenhain 

huettenhain@imsb.biol.ethz.ch 

Institute for Molecular Systems Biology, 

ETH Zurich 

Zurich, Switzerland 

Tae Hyun Hwang 

hwang071@umn.edu 

University of Minnesota 

Minneapolis, USA 

Janus Jakobsen 

janus.jakobsen@bric.ku.dk 

University of Copenhagen 

Copenhagen N, Denmark 

Janette Jones 

janette.jones@unilever.com 

Unilever 

Bebington, Wirral, UK 

Haja Kadarmideen 

hajak@life.ku.dk 

Faculty of Health and Medical 

Sciences 

Frederiksberg C, Danmark 

Kumaran Kandasamy 

kkandasamy@cemm.oeaw.ac.at 

Center for Molecular Medicine 

Vienna, Austria 

Sophie Kershaw 

sophie.kershaw@keble.ox.ac.uk 

University of Oxford 

Oxford, United Kingdom 

Theo Knijnenburg 

t.knijnenburg@nki.nl 

Netherlands Cancer Institute 

Amsterdam 1066 CX, Netherlands 

Lisette Kogelman 

lkog@life.ku.dk 

Copenhagen University 

Frederiksberg C, Denmark 

Alexey Kopylov 

kopylov.alex@gmail.com 

Chemistry Department of Moscow 

State University, Apto-pharm 

Moscow, Russian Federation 

Nevan Krogan 

Nevan.Krogan@ucsf.edu 

UCSF 

San Francisco, CA, USA 

Inna Kuperstein 

inna.kuperstein@curie.fr 


Paris, France 

Marie Kveiborg 

marie.kveiborg@bric.ku.dk 

Copenhagen University 


Anna-Maria Lahesmaa-Korpinen 

anna-maria.lahesmaa@helsinki.fi 

University of Helsinki 

Finland 

Janne Marie Laursen 

jml@cbs.dtu.dk 

Technical University of Denmark 


Ben Lehner 

lehner.ben@gmail.com 

EMBL-CRG 

Barcelona, Spain 

Rune Linding 

linding@cbs.dtu.dk 

C-SIG, CBS, DTU 

Lyngby, DENMARK 

Riku Louhimo 

Riku.Louhimo@Helsinki.FI 

University of Helsinki 

Finland 

Raphaëlle Luisier 

raphaelle.luisier@unibas.ch 

Basel University, Biozenturm 

Basel, Switzerland 


Kenji Maeda 

maeda@embl.de 

EMBL-Heidelberg 


Mohammad Mobashir 

mohammad.mobashir@med.ovgu.de 

Otto-von-Guericke University 

Magdeburg, Germany 

Gian Luca Negri 

gian.negri@ucd.ie 

University College of Dublin 

Dublin, Ireland 

Elena Nikonova 

elena.nikonova@ucdconnect.ie 

University College Dublin 

Dublin, Ireland 

Tri Hieu Nim 

nimtrihieu1111@gmail.com 

Singapore-MIT Alliance 

Singapore, Singapore 

Garry Nolan 

gnolan@stanford.edu 

Stanford University 

San Francisco, CA, USA 

Matt Onsum 

monsum@merrimackpharma.com 

Merrimack Pharmaceuticals 

Cambridge, MA, USA 

Alessandro Ori 

alessandro.ori@embl.de 

EMBL 


Bernhard Palsson 

bpalsson@ucsd.edu 

UCSD/DTU 

San Diego/Lyngby, USA/Denmark 

Galina Pavlova 

lkorochkin@mail.ru 

Institute of Gene Biology, Apto-pharm 

Moscow, Russian Federation 

Dana Pe’er 

dpeer@biology.columbia.edu 

Columbia University 

New York, MA, USA 

Helle Krogh Pedersen 

hellekp@cbs.dtu.dk 

DTU 


Stine Falsig Pedersen 

sfpedersen@bio.ku.dk 

University of Copenhagen 

Copenhagen 2100, Denmark 

Lars Hagsholm Pedersen 

lap@bioneer.dk 

Bioneer 

Hørsholm, Danmark 

Norbert Perrimon 

perrimon@receptor.med.harvard.edu 

Harvard Medical School 

Boston, MA, USA 

Ian Prior 

iprior@liv.ac.uk 

University of Liverpool 

Liverpool, UK 

Christian Hove Rasmussen 

chvr@novonordisk.com 


Søborg, Søborg 

Angela Re 

re@science.unitn.it 

Trento University 

Trento, Italy 

Xavier Robin 

Xavier.Robin@unige.ch 

University of Geneva 

Genève, Switzerland 

Honor May Rose 

rose@fmp-berlin.de 

Leibniz Institute of Molecular Pharmacology 

(FMP Berlin) 

Berlin, Germany 

Janine Roy 

janine.roy@biotec.tu-dresden.de 

Biotechnology Center, Technische 



Francesca Sacco 

francesca.sacco@uniroma2.it 


Roma, Italia 

Katrin Sameith 

k.sameith@umcutrecht.nl 

Molecular Cancer Research, UMC 

Utrecht 

Utrecht, The Netherlands 

ParticiPantS ParticiPantS 

Gabriele Scheler 

gscheler@gmail.com 

Carl-Correns Foundation for Mathematical 

Biology 

Mountain View, USA 

Thomas Schlitt 

thomas.schlitt@kcl.ac.uk 

King’s College London 

London, UK 

Erwin Schoof 

schoofe@cbs.dtu.dk 


Analysis 


Veit Schwemmle 

veitveit@gmail.com 

SDU 

Odense, Denmark 

Benno Schwikowski 

benno@pasteur.fr 


Paris, France 

Alain Sewer 

Alain.Sewer@pmi.com 

PMP SA 

Neuchatel, Switzerland 

Greg Slodkowicz 

greg@cbs.dtu.dk 

DTU 


Nathaniel Stanley 

nathaniel.stanley@gmail.com 

Universitat Pompeu Fabra 

Barcelona, Spain 

Ulrich Stelzl 

stelzl@molgen.mpg.de 

MPI-MG 


Sebastian Student 

sebastian.student@polsl.pl 



Anthony Sullivan 

anthony.sullivan@absciex.com 

AB SCIEX 

Warrington WA1 1RX, UK 

Damian Szklarczyk 

damian.szk@gmail.com 

NNF Center for Protein Research 


Agnieszka Szwajda 

agnieszka.szwajda@helsinki.fi 

Institute for Molecular Medicine Finland 

(FIMM) 

Helsinki FI-00014, Finland 

Camille Terfve 

terfve@ebi.ac.uk 

EMBL-EBI 

Hinxton, United Kingdom 

Rossukon Thongwichian 

thongwichian@fmp-berlin.de 

Leibniz Institute of Molecular Pharmacology 

(FMP Berlin) 


Ala Trusina 

trusina@nbi.dk 

Niels Bohr Institute 

Copenhagen 


Kalliopi Tsafou 

ptsafou@gmail.com 

NNF Center for Protein Research, 

Faculty of Health Sciences, University 

of Copenhagen 

København, Danmark 

Bora Uyar 

bora.uyar@embl.de 

EMBL 


Anatoly Uzdensky 

auzd@yandex.ru 

Southern Federal University 

Rostov-on-Don, Russia 

Irene van Dijk 

I.v.Dijk@acta.nl 

Academic Centre Dentistry Research 

(ACTA) 

Amsterdam, Netherlands 

Hanne Varmark 

hvarmark@gmail.com 

Dept. of systems biology 


Jose Velazquez 

jvelazqu@mail.nih.gov 

National Institute on Aging 

Bethesda, USA 

Marc Vidal 

marc_vidal@dcfi.edu 

DCFI, HMS 

Boston, USA 

Marian Walhout 

marian.walhout@cbs.dtu.dk 

UMASS Medical School 

Worcester, USA 

Robert Weatheritt 

robert.weatheritt@embl.de 

EMBL 

Heidelberg , Germany 

Rasmus Wernersson 

raz@intomics.com 

Intomics 


Lodewyk Wessels 

l.wessels@nki.nl 

Netherlands Cancer Institute 

Amsterdam, Netherlands 

Dorien Wijte 

dorien.wijte@gmail.com 

DTU 


Christof Winter 

christof.winter@med.lu.se 

Lund University 

Lund, Sweden 

Jonathan Woodsmith 

woodsmit@molgen.mpg.de 

MPIMG 


Christopher Workman 

workman@cbs.dtu.dk 

DTU 


Michael Yaffe 

myaffe@mit.edu 

MIT 

Cambridge, MA, USA 

Andrei Zinovyev 

andrei.zinovyev@curie.fr 


Paris, France 


conFerence venue 

Comwell Borupgaard 

Nørrevej 80 

3070 Snekkersten 


+45 4838 0399 

WiFi 

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Password: comwell 

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11-13 May 2012 Helsingør - Denmark www.networkbio.org

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