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A Look at the Tools and Comparative Approaches of Systems Biology


for Members

A Look at the Tools and Comparative Approaches of Systems Biology

Thursday, June 10, 2010

The New York Academy of Sciences

Presented By


A Systems Biology approach to life science problems involves the application of a wide array of computational tools and strategies. This symposium brings together researchers actively involved in the development and application of Systems Biology methodologies, including computational and mathematical models, graph-theory algorithms, machine-learning techniques and dynamical modeling. These quantitative tools are used to unravel the complexity of regulatory networks in biological systems, to analyze emerging high-throughput proteomics and large-scale genomics experimental data, to perform integrative surveys and to mine protein and gene regulatory networks.


The Intersectome: Integration of Knowledge in Systems Biology for Hypothesis Generation
Avi Ma’ayan, Mount Sinai School of Medicine

Molecular Characterization of the Lymphoma Genome: From Integrative Data Analysis to Targeted Therapy
Stefano Monti, Broad Institute of MIT & Harvard

Analysis of Molecular Networks
Mark Gerstein, Yale University

Networking Reception



Andrea Califano

Columbia University

Aris Economides

Regeneron Pharmaceuticals

Gustavo Stolovitzky

IBM Research

Jennifer Henry

New York Academy of Sciences


Mark Gerstein, PhD

Yale University

Mark Gerstein is the Albert L. Williams professor of Biomedical Informatics at Yale University. He is co-director the Yale Computational Biology and Bioinformatics Program, and has appointments in the Department of Molecular Biophysics and Biochemistry and the Department of Computer Science. He received his AB in physics summa cum laude from Harvard College in 1989 and his PhD in chemistry from Cambridge in 1993. He did post-doctoral work at Stanford and took up
his post at Yale in early 1997.

Since then he has received a number of young investigator awards (e.g. from the Navy and the Keck foundation) and has published appreciably in scientific journals. He has >250 publications in total, with a number of them in prominent journals, such as Science, Nature, and Scientific American. (His current publication list is at .) His research is focused on bioinformatics, and he is particularly interested in large-scale integrative surveys, biological database design, macromolecular geometry, molecular simulation, human genome annotation, gene expression analysis, and data mining.

Avi Ma’ayan, PhD

Mount Sinai School of Medicine

Dr. Ma’ayan received his PhD in Biomedical Sciences in 2006 from Mount Sinai School of Medicine in New York. He is currently an Assistant Professor in the Department of Pharmacology and Systems Therapeutics at Mount Sinai and the Director of the Information Management Unit of the Systems Biology Center New York. Dr. Ma’ayan specializes in Computational Systems Biology. In his relative short tenure as a scientist he has published several seminal studies in high profile journals such as Science, Nature and PNAS. Dr. Ma'ayan and his laboratory developed acclaimed software and algorithms utilized to understand cell signaling networks in mammalian neuronal cells, and gene regulatory networks in stem cells.

Stefano Monti, PhD

Broad Institute of MIT and Harvard

Stefano Monti is a Senior Computational Biologist at the Broad Institute of MIT & Harvard, where he is a member of the Cancer Program and the Computational Biology and Bioinformatics Program. Dr. Monti has a decade-long experience in the design and development of computational tools for the analysis and integration of high-dimensional genomics data to further the understanding of the molecular mechanisms of cancer formation and treatment.


For sponsorship opportunities please contact Cristine Barreto at or 212.298.8652.

Bronze Sponsor


Molecular Characterization of the Lymphoma Genome: From Integrative Data Analysis to Targeted Therapy

Stefano Monti, PhD, Broad Institute of MIT & Harvard

Through some case studies, I will discuss how the integration of data from high-throughput genomics assays can help us shed light onto the molecular mechanisms driving cancer formation, and I will illustrate how the application of computational tools to the analysis of this data can help us define distinctive molecular profiles of therapeutic relevance, thus moving us closer to the development of targeted therapies.

The Intersectome: Integration of Knowledge in Systems Biology for Hypothesis Generation

Avi Ma’ayan, PhD, Mount Sinai School of Medicine

I will discuss how we utilize data collected from the public domain, describing regulatory interactions in mammalian cells, to analyze results from experiments that profile cells using a variety of cutting edge genome-wide profiling technologies. The results from our analyses produce rational hypotheses for further experimental validation as well as provide a global view of cell regulation across multiple layers.

Analysis of Molecular Networks

Mark Gerstein, PhD, Yale University

My talk will be concerned with understanding protein function on a genomic scale. My lab approaches this through the prediction and analysis of biological networks, focusing on protein-protein interaction and transcription-factor-target ones. I will describe how these networks can be determined through integration of many genomic features and how they can be analyzed in terms of various topological statistics. In particular, I will discuss a number of recent analyses:
(1) Improving the prediction of molecular networks through systematic training-set expansion; (2) Showing how the analysis of pathways across environments potentially allows them to act as biosensors; (3a) Analyzing the structure of the regulatory network indicates that it has a hierarchical layout with the "middle-managers" acting as information bottlenecks; (3b) Showing these middle managers tend be arranged in various "partnership" structures giving the hierarchy a "democratic character" ; (4) Showing that most human variation occurs on the periphery of the protein interaction network; and (5) Developing useful web-based tools for the analysis of networks (TopNet and tYNA).

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