University of California San Francisco; Hebrew University
The Role of Computational Methods in Creating a Systems Level View from Biological Data
1:00 – 3:00pm Monday, December 04, 2006
Recent advances in high-throughput experimental methods alongside computational breakthroughs have radically changed the way biological experiments are conceived, performed and analyzed. This revolution has led to the rapid emergence of the new field of Systems Biology that is based on the premise that the study of biological processes at the systems level allows insights into cellular systems that could not have been obtained using conventional biological tools. In particular, System biologists aim to understand biological systems and processes at multiple levels: What are the defined components of a system, how do they dynamically interact to create new information and how does that output re-define the system. Such answers are also crucial for understanding the principles underlying biological systems and how they evolved. In this tutorial we will provide an introduction to the field of systems biology and discuss its importance amongst the other biological disciplines. We will examine the interdisciplinary aspects of research on system biology questions and the consequences of the requisite interactions between experimental biologists and machine learning and computational analysis experts. We will show how this interplay between two very different scientific schools influences the contributions to the field and their assessment and discuss the potential to use this collaborative cross talk to strategically advance data acquisition and analysis in ways that will most impact to the field; We will map out the future goals on the road to obtaining a comprehensive systematic view of the cellular machinery and how computational analysis, and in particular machine learning methodologies, might play a role in achieving these goals. This tutorial is aimed for the general NIPS community and does not assume prior background in biology.
Maya Schuldiner is a post-doctoral fellow working with Prof. Jonathan Weissman at the Quantitative Biology Institute at the University of California, San Francisco. She received her Ph.D degree from the Hebrew University, Jerusalem, Israel. Her research focuses on the modeling of simple biological systems utilizing the rich information found in genetic interaction networks.
Nir Friedman received a Ph.D. in Computer Science from Stanford in 1997. After a postdoctoral scholarship at University of California, Berkeley, he joined the Hebrew University, Jerusalem, Israel in 1998 as a faculty member at the School of Computer Science & Engineering. Dr. Friedman's major research interests involve learning and inference with probabilistic models and their applications to computational biology. He has been extensively working on learning and inference with complex probabilistic models, mainly Bayesian networks and their relational extensions. Since he joined the Hebrew University in 1998, Dr. Friedman's research has been focused on application of these tools to systems biology. In particular, his group has used probabilistic models to combine gene expression profiles with other genomics data sets to elucidate biological hypotheses relating to human disease, reconstruction of cellular pathways, and understanding of regulatory circuits.