Computational Biology Distinguished Seminar Series
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Title:Analysis of Molecular Networks
By: Prof. Mark Gerstein
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.
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.
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
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
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.
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.
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.