Computational Biology Distinguished Seminar Series

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Friday April 20, 2:00pm

Title:Analysis of Molecular Networks
By: Prof. Mark Gerstein

Venue:CSI 3117

My talk will be concerned the analysis of networks and the use of networks as a "next-generation annotation" for interpreting personal genomes. I will initially describe current approaches to genome annotation in terms of one-dimension browser tracks. Then I will describe various aspects of networks. In particular, I will touch on the following topics: (1) I will show how analyzing the structure of the regulatory network indicates that it has a hierarchical layout with the "middle-managers" acting as information-flow bottlenecks and with more "influential" TFs on top. (2) I will show that most human variation occurs at the periphery of the network. (3) I will compare the topology and variation of the regulatory network to the call graph of a computer operating system, showing that they have different patterns of variation. (4) I will talk about web-based tools for the analysis of networks (TopNet and tYNA).

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks. KK Yan, G Fang, N Bhardwaj, RP Alexander, M Gerstein (2010). Proc Natl Acad Sci U S A 107:9186-91.

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels. N Bhardwaj, KK Yan, MB Gerstein (2010). Proc Natl Acad Sci U S A 107:6841-6.

Positive selection at the protein network periphery: evaluation in terms of structural constraints and cellular context. PM Kim, JO Korbel, MB Gerstein (2007). Proc Natl Acad Sci U S A 104:20274-9.

The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks. KY Yip, H Yu, PM Kim, M Schultz, M Gerstein (2006). Bioinformatics 22:2968-70.

2:00 pm Monday April 16, 2012

Title: Integrative analysis of functional genomics data: from yeast to tissue-specific modeling of human disease
By: Olga Troyanskaya, Princeton University

Venue: AVW 2460

Video Recording (UMD only)
The ongoing explosion of new technologies in functional genomics offers the promise of understanding gene function, interactions, and regulation at the systems level. This should enable us to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems. However, the complexity and scale of human molecular biology make it difficult to integrate this body of data, understand it on a systems level, and apply it to the study of specific pathways or genetic disorders. These challenges are further exacerbated by the biological complexity of metazoans, including diverse biological processes, individual tissue types and cell lineages, and by the increasingly large scale of data in higher organisms.
I will describe how we address these challenges through the development of bioinformatics frameworks for the study of gene function and regulation in complex biological systems and through close coupling of these methods with experiments, thereby contributing to understanding of human disease. I will specifically discuss how integrated analysis of functional genomics data can be leveraged to study cell-lineage specific gene expression, to identify proteins involved in disease in a way complementary to quantitative genetics approaches, and to direct both large-scale and traditional biological experiments.

Speaker bio:
Olga Troyanskaya is an Associate Professor in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University, USA, where she runs the Laboratory of Bioinformatics and Functional Genomics. Her work bridges computer science and molecular biology in an effort to develop better methods for analysis of diverse genomic data with the goal of understanding and modeling protein function and interactions in biological pathways. Her group includes computational and experimental aspects, and tackles diverse questions including developing integrative technologies for pathway prediction and the study of biological networks in complex human disease. Dr. Troyanskaya is an Associate Editor for Bioinformatics, PLOS Computational Biology, and editorial board member of Journal of Biomedical Informatics, Briefings in Bioinformatics, and Biology Direct. She is also a member of the Board of Directors of the International Society for Computational Biology. She received her Ph.D. from Stanford University and is a recipient of the Sloan Research Fellowship, the NSF CAREER award, the Howard Wentz faculty award, and the Blavatnik Finalist Award. She has also been honored as one of the top young technology innovators by the MIT Technology Review and is the 2011 recipient of the Overton Prize in computational biology.

2:00 pm Monday April 9, 2012

Title: Evolution of a Complex Signal Transduction System
By: Prof. Igor B. Zhulin

Venue: AVW 2460

Video Recording (UMD only)
Molecular machinery that governs bacterial motility (chemotaxis) is one of the best studied signal transduction systems in Nature. Sophisticated behavior of Earth’s smallest organisms fascinated naturalists of the last century as well as modern molecular biologists, who hoped that the properties of the underlying molecular navigation system in bacteria would resemble those of higher organisms. However, the latest structural and functional studies revealed no such similarity in the molecular design. Using the wealth of information encoded in hundreds of bacterial genomes and a variety of bioinformatics tools we reconstructed the natural history of this system. Here we show that the chemotaxis system is the evolutionary youngest and most sophisticated signal transduction pathway in prokaryotes. It appeared in Bacteria after the separation of the three domains of Life and has been later transferred into Archaea, but not into Eucarya. It developed gradually from the simplest signal transduction systems comprised of a single protein with sensory and regulatory capabilities. The chemotaxis system differentiated into several functional classes that evolved to control not only motility, but also other cellular functions. Detailed computational analysis of individual system components allowed us to reveal novel structural and functional insights that have not been identified by previous experimental studies.

Speaker bio:
Igor B. Zhulin is a Distinguished Scientist at the Computing and Computational Sciences Directorate of the Oak Ridge National Laboratory and a Joint Faculty Professor at the Department of Microbiology, University of Tennessee. He received his B.Sc. degree in Biology from Saratov State University and his Ph.D. in Microbiology from St. Petersburg State University. During his postdoctoral studies he transitioned from experimental to computational biology. He is an editor of “Computational Biology” and “Genomics and Proteomics” sections of the Journal of Bacteriology. He was/is a chair and a permanent member of several NIH panels including “Computational tools for Human Microbiome Project”, “Biodata Management and Analysis”, “Prokaryotic Cell and Molecular Biology”. His research interests are in the area of computational genomics and protein sequence analysis with a focus on signal transduction, protein-protein interactions, molecular modeling and dynamics, all being viewed through the prism of Evolution.

12:30 pm Wednesday, February 22, 2012

Title:Population Scale Detection of Common and Rare Genomic Rearrangements and Transcriptomic Aberrations
By: Prof. Cenk Sahinalp (Simon Fraser University)

Venue: AVW 2460

Massively parallel (MP) sequencing technologies are on their way to reduce the cost of whole shotgun sequencing of an individual donor genome to USD 1000. Coupled with algorithms to accurately detect structural (in particular expressed) differences among many individual genomes, MP sequencing technologies are soon to change the way diseases of genomic origin are diagnosed and treated. In this talk we will briefly go through some of the algorithm development efforts at the Lab for Computational Biology in SFU for simultaneously analyzing large collections of MP sequenced genomes and transcriptomes, and in particular for identifying and differentiating common and rare, expressed and unexpressed large scale variants with high accuracy. Our algorithms, which we collectively call CommonLAW (Common Loci structural Alteration detection Widgets) move away from the current model of detecting genomic variants in single MP sequenced donors independently, and checking whether two or more donor genomes indeed agree or disagree on the variations. Instead, we propose a new model in which structural variants are detected among multiple genomes and transcriptomes simultaneously. One of our methods, Comrad, for example, enables integrated analysis of transcriptome (i.e. RNA) and genome (i.e. DNA) sequence data for discovering expressed rearrangements in multiple, possibly related, individuals.

Speaker bio:
S. Cenk Sahinalp is a Professor of Computing Science at Simon Fraser University, Canada. He received his B.Sc. degree in Electrical Engineering from Bilkent University and his Ph.D. in Computer Science from the University of Maryland at College Park. Sahinalp is an NSF Career Awardee, a Canada Research Chair and a Michael Smith Scholar for Health Research. He was/is the conference general chair of RECOMB'11, PC chair of APBC'13, sequence analysis area chair for ISMB, and CSHL Genome Informatics Conferences and has co-founded the RECOMB-Seq conference series on Massively Parallel Sequencing. He co-directs the SFU undergraduate program in Bioinformatics and the SFU Bioinformatics for Combating Infectious Diseases Research Program. His research interests include computational genomics, in particular algorithms for high throughput sequence data, network biology, RNA structure and interaction prediction and chemoinformatics algorithms.