Sven Bergmann (Link to website)
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Sven Bergmann studied theoretical particle physics with Prof. Yosef Nir at the Weizmann Institute of Science (Israel) where he received his PhD in 2001. He then joined the laboratory of Prof. Naama Barkai in the Department of Molecular Genetics at the same institute, where he first worked as a Koshland postdoctoral fellow and later as staff scientist. His work in the field of computational biology comprised designing and applying novel algorithms for the analysis of large-scale expression data, as well as modeling of genetic networks pertaining to the development of the Drosophila embryo. He joined the Department of Medical Genetics in 2004 as Assistant Professor and leads the Computational Biology Group. He is affiliated with the Swiss Institute of Bioinformatics since 2005 and a Cavaglieri Fellow since 2006. |
Research
The "genomic" revolution in biology will have a fundamental impact on the improvement of diagnosis, prevention and treatment of disease. Microarrays have firmly established themselves as a standard tool in biological and biomedical research. This high-throughput technology was first used to access genomic gene expression, but is employed now also for measuring genotypic variability, including single nucleotide polymorphisms (SNPs) and copy number variations (CNVs). Biological studies frequently probe cells under a variety of experimental conditions (temperature, pH, mutations, etc.), while clinical studies usually provide additional phenotypic information (state of disease, type of treatment, blood chemistry of patient, etc.).
Our goal is to process the different types of data in order reduce their complexity. We do this by grouping fundamental elements (genes, samples, treatments) that share certain similarities into so-called "modules". This helps to make the huge amounts of data more amendable for analysis and may reveal the organization of and relation between the modules and their elements.
Such a modular approach is useful for each large dataset by itself: Analysis of massive gene expression data reveals "transcription modules" containing genes that are co-expressed in certain samples. Massive clinical data can be processed into "phenotypic modules" merging clinical variables that behave similarly across certain groups of individuals. Finally, we are exploring how higher-order structures in SNP data can be studied using "genotypic modules" containing SNP markers (or haplotypes) that co-segregate in subpopulations of a cohort.
The modular approach is also extendable for the joint analysis of different datasets. For example, we may identify a set of genes that is co-expressed in a group of cell-lines that all exhibit a similar sensitivity to a collection of treatments. Unsupervised identification of such "co-modules" provides testable hypotheses, e.g. we would predict that these genes are involved in pathways relevant for some cell-lines in response to related treatments.
A complementary direction of research pertains to relatively small genetic networks, whose components are well-known. We collaborate closely with experts of the field to identify biological systems that can be modeled quantitatively. Our goal in developing such models is not only to give an approximate description of system, but also to obtain a better understanding of its properties. For example, regulatory networks evolved to function reliably under ever-changing environmental conditions. This notion of robustness can guide computational analysis and provide constraints on models that complement those from direct measurements of the system's output.
In general, our group seeks an interdisciplinary approach, bridging the traditional gaps between physics, mathematics and biology. Our lab collaborates with experimental groups within and outside our department. In particular, due to our proximity to the CHUV we have close contacts to medical research groups and assist the analysis of clinical data.
Selected Publications (click here for full list)
Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR, Auton A, Indap A, King KS, Bergmann S, Nelson MR, Stephens M, Bustamante CD (2008). Genes mirror geography within Europe Nature Aug 31; doi:10.1038/nature07331.
Kutalik Z, Beckmann JS and Bergmann S (2008). A modular approach for integrative analysis of large-scale gene-expression and drug-response data Nature Biotechnology 26(5): 531-9.
Bergmann S, Tamari Z, Shilo B-Z, Schejter E and Barkai N (2008). Stability of the Bicoid Gradient? Cell 132: 15.
Bergmann S, Sandler S, Sberro H, Shnider S, Shilo B-Z, Schejter E and Barkai N (2007). Pre-Steady-State Decoding of the Bicoid Morphogen Gradient. PLoS Biology 5(2): e46.
Ihmels J*, Bergmann S*, Berman J, Barkai N (2005, *equal co-authorship). The differential clustering approach for comparative gene expression analysis: application to the Candida albicans transcription program. PLoS Genetics 1(3): e39.
Ihmels J, Bergmann S, Gerami-Nejad M, Yanai I, McClellan M, Berman J, Barkai N (2005). Rewiring of the Yeast Transcriptional Network Through the Evolution of Motif Usage. Science 309(5736): 938-40.
Ben-Chetrit E, Bergmann S, Schaner M, Sood R (2005). Mechanism of colchicine anti inflammatory effect in rheumatic diseases: A possible new outlook through microarray analysis. Rheumatology (Oxford): e1401.
Selmecki A, Bergmann S, Berman J (2005). Comparative genome hybridization reveals widespread aneuploidy in Candida albicans laboratory strains. Molecular Microbiology 55(5): 1553-65.
Ihmels J, Bergmann S (2004). Challenges and prospects in the analysis of large-scale gene expression data. Brief Bioinform. 5(4):313-27.
Ihmels J, Bergmann S, Barkai N (2004). Defining transcription modules using large-scale gene expression data. Bioinformatics 20(13): 1993-2003.
Bergmann S, Ihmels J, Barkai N (2004). Similarities and differences in genome-wide expression data of six organisms. PLoS Biology 2(1): E9.
Bergmann S, Ihmels J, Barkai N (2003). Iterative signature algorithm for the analysis of large-scale gene expression data. Physics Review E. 67(3 Pt 1): 031902.
Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N. (2002). Revealing modular organization in the yeast transcriptional network. Nature Genetics. 31(4): 370-7.
