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38 <strong>Helmholtz</strong>‐<strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology Dr. Lars Kaderali Institution University of Heidelberg, Bioquant BQ26 Contact Address Street Address Im Neuenheimer Feld 267 Zip /Postal Code 69120 City Heidelberg Country <strong>German</strong>y Phone +49‐6221‐54 51 357 Fax +49‐6221‐54 51 486 E‐Mail lars.kaderali@bioquant.uni‐heidelberg.de Website http://hades1.bioquant.uni‐heidelberg.de Short CV 1995‐2001 Studies Business and Computer Science, University of Cologne 2001 Research Sabbatical, Los Alamos National Laboratories, USA 2001‐2006 PhD, Computer Science, University of Cologne 2006‐2007 Postdoc, <strong>German</strong> Cancer Research Center, Heidelberg Since 2007 Independent Junior Group Leader, University of Heidelberg Research Interests Mathematical Modeling and analysis of complex processes in molecular and cell biology, with a particular focus on virus‐host interactions Method development for the analysis of experimental high‐throughput data, e.g. RNAi data, Microarray Data, etc. Machine learning tools and statistical learning algorithms in Bioinformatics and Systems Biology Five most important publications 1. Kaderali, Radde (2007). Inferring Gene Regulatory Networks from Gene Expression Data. In: A. Kelemen, A. Abraham, Y. Chen (Editors), Computational Intelligence in Bioinformatics. Springer‐ Verlag, in press. 2. Radde, Kaderali (2007). Bayesian Inference of Gene Regulatory Networks using Gene Expression Time Series Data. LNBI Lecture Notes in Bioinformatics, 4414, 1‐15. 3. Schramm, Vandesompele, Schulte, Dreesmann, Kaderali, Brors, Eils, Speleman, Eggert (2007). Translating Expression Profiling into a Clinically Feasible Test to Predict Neuroblastoma Outcome. Clinical Cancer Research 13(5), 1459‐1465. 4. Kaderali, Zander, Faigle, Wolf, Schultze, Schrader (2006). CASPAR: A Hierarchical Bayesian Approach to predict Survival Times in Cancer from Gene Expression Data. Bioinformatics 22, 1495‐ 1502. 5. Kaderali, Schliep (2001). Selecting signature oligonucleotides to identify organisms using DNA arrays. Bioinformatics 18, 1340‐1349. Contribution to <strong>Helmholtz</strong> <strong>Russian</strong><strong>German</strong> workshop on systems biology I will prepare (X) a poster ( ) a talk Title of poster / talk Reverse‐Engineering of Gene Regulatory Networks using Bayes’ regularized Differential Equations Abstract In this work, we present a novel methodological approach to reverse engineer gene regulatory networks from gene expression time series data. We combine differential equations with a dynamic Bayesian network approach, enabling us not only to infer a network from given data and make predictions of future states of the network, but also allowing it to assign confidences to network predictions, evaluate different likely network topologies, and make predictions of future states of the network together with confidence intervals on the predictions made. Last but not least, this approach makes it possible to give feedbak to experimental groups on which additional experiments would yield a maximal reduction in uncertainty about an underlying network topology. We show an evaluation of our approach on simulated data, as well as results on the gene regulatory network underlying the yeast cell cycle, by analyzing publicly available microarray time series data. In addition, we point to applications of our approach in the field of medical systems biology. Poster number: 16
<strong>Helmholtz</strong>‐<strong>Russian</strong>‐<strong>German</strong> <strong>Workshop</strong> on Systems Biology 39 Dr. Alexey Kazakov Institution Contact Address Institute for Information Transmission Problems RAS Street Address Bolshoy Karetny per. 19 Zip /Postal Code 127994 City Moscow Country Russia Phone +7 Fax +7 495 650 0579 E‐Mail kazakov@iitp.ru Website Short CV http://www.rtcb.iitp.ru/ Current degree Ph. D. in genetics (2000) Education 1996 ‐ 1999: PhD Student, National Research Center of Mental Health RAMS 1990 ‐ 1996: Student, Department of Biochemistry, <strong>Russian</strong> State Medical University Professional employment 2004 ‐ present: Senior Scientist Researcher, Institute for Information Transmission Problems RAS 2000 ‐ 2003: Scientist Researcher, Branch of corporation “Integrated Genomics, Inc.” 1999 ‐ 2000: Junior Scientist Researcher National Research Center for Mental Health RAMS Research Interests I am interested in transcriptional regulation of bacterial genes and organization of antimicrobial peptides biosynthetic systems. I use comparative genomics and bioinformatics to investigate different aspects of evolution of coding and regulatory sequences on a genome level. I also participate in development of database of bacterial regulatory motifs. Five most important publications 1. Novikova M, Metlitskaya A, Datsenko K, Kazakov T, Kazakov A, Wanner B, Severinov K. The Escherichia coli Yej Transporter Is Required for the Uptake of Translation Inhibitor Microcin C. J Bacteriol, 2007 Nov;189(22):8361‐8365. 2. Severinov K, Semenova E, Kazakov A, Kazakov T, Gelfand MS. Low‐molecular‐weight post‐ translationally modified microcins. Mol Microbiol. 2007 Sep;65(6):1380‐94. 3. Kazakov AE, Cipriano MJ, Novichkov PS, Minovitsky S, Vinogradov DV, Arkin A, Mironov AA, Gelfand MS, Dubchak I. RegTransBase – a database of regulatory sequences and interactions in a wide range of prokaryotic genomes. Nucleic Acids Res. 2007 Jan;35(Database issue):D407‐12. 4. Permina EA, Kazakov AE, Kalinina OV, Gelfand MS. Comparative genomics of regulation of heavy metal resistance in Eubacteria. BMC Microbiol. 2006 Jun 5;6:49. 5. Kazakov AE, Shepelev VA, Tumeneva IG, Alexandrov AA, Yurov YB, Alexandrov IA. Interspersed repeats are found predominantly in the “old” alpha satellite families. Genomics. 2003 Dec;82(6):619‐ 27 Contribution to <strong>Helmholtz</strong> <strong>Russian</strong><strong>German</strong> workshop on systems biology I will prepare (X) a poster ( ) a talk Title of poster / talk Identification of microcin C‐like antibiotic systems Abstract Microcins are a class of small peptide antibiotics secreted by enterobacteria. Genes of microcins biosynthesis, maturation and secretion are often organized in operons. Taking into account the arrangement of genes encoding microcin precursor and microcin maturation enzymes, we were able to identify 17 microcin C‐like antibiotic systems in 13 bacterial species belonging to beta‐, gamma‐ and epsilon‐Proteobacteria, Firmicutes and Cyanobacteria. Heptapeptides structurally similar to microcin C51 precursor were found in all gene clusters identified, except those from Bartonella henselae and two Synechococcus strains. In Synechococcus, two (strain CC9605) or three (strain RS9916) direct repeats each encoding 56 or 57 aminoacid polypeptides, respectively, were observed. The ORFs are sufficiently similar between the two Synechococcus strains and the C‐terminal amino acids of all the ORFs are asparagines, that is typical for microcin C51‐like precursors. So, the cyanobacterial microcin precursors may constitute a novel subtype of C51‐related microcins, with potentially different properties. Our analysis demonstrates that peptides structurally similar to microcin C51 from E. coli may be produced by a variety of bacterial groups. Poster number: 17
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