Colloquia, Seminars and Conference News
Title : Automatic Inference of Biological Networks Governing Animal Development
Date : March 21, 2008. (11:00 am) Tea starts half an hour before each seminar
Location: ITEB 336
Speaker : Prof. Pengyu Hong
Abstract:
A major focus of current biomedical research is on systems-level understanding biological networks that mediate cell responses to external stimuli and control organogeneses and oncogenic transformations. Researchers have generated overwhelming systems-level measurements of many types of cellular molecules in several species.
However, it remains a great challenge to analyze those data to both increase our understanding of biological networks and generate biological hypotheses to guide future experiments. We developed a mathematical model based on dynamic Bayesian networks (DBNs) to model such networks. Statistical machine learning algorithms were developed to automatically infer such a model from heterogeneous biological data (e.g., gene expression patterns, phenotypic data, and interaction data) across species. Our approach was successfully applied to model C. elegans vulval induction, which is a paradigmatic example of animal organogenesis with extensive experimental data. The inferred C. elegans vulval induction model contains six interconnected identical modules corresponding to six Vulval Precursor Cells (VPCs), and each VPC module contains 27 components. The model is capable of simulating not only vulval induction results under 73 different genetic conditions but also the dynamics of the vulval induction process. This work addresses one of most challenging problems in learning statistical graphical models, namely, inferring the structure of a DBN with hidden variables, and represents a significant advance in automatic inference of molecular networks governing collaborative differentiation in multi-cellular environments.
Bio: Dr. Hong received his Ph.D. in Computer Science from University of Illinois at Urbana-Champaign. After his postdoc training in Computational Biology at the Department of Statistics at Harvard University and Stanford University, he joined Brandeis University in
2005 as an Assistant Professor of Computer Science. More information about his research is at http://www.cs.brandeis.edu/~hong/.
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