The Systems Biology of Bugs


for Members

The Systems Biology of Bugs

Monday, November 8, 2010

The New York Academy of Sciences

Presented By


A Systems Biology approach helps understand important biological systems using tools from physics, mathematics and systems analysis. Systems biologists and computational biologists collaborate on projects that integrate computational analysis, experimental designs and data. This symposium brings together researchers actively involved in the development and application of Systems Biology methodologies to examine bacterial genomes and elucidate basic signal transduction mechanisms and the genetic basis of phenotypes in the large and growing population of bacteria of basic biological, environmental, and clinical importance.

This event will also be broadcast as a webinar.

Please note:
Transmission of presentations via the webinar is subject to individual consent by the speakers. Therefore, we cannot guarantee that every speaker's presentation will be broadcast in full via the webinar. To access all speakers' presentations in full, we invite you to attend the live event in New York City where possible.


Learning Biological Networks:  From Modules to Dynamics
Richard Bonneau, PhD, New York University

The Physics of Biological Information Processing:  E. coli's Memory and Computation
Yuhai Tu, PhD, IBM T.J. Watson Research Center

Genetic Basis of Adaptation to Extreme Environments
Saeed Tavazoie, PhD, Princeton University

Networking Reception to Follow.



Andrea Califano, PhD

Columbia University

Aris Economides, PhD

Regeneron Pharmaceuticals

Gustavo Stolovitzky, PhD

IBM Research

Jennifer Henry. PhD

The New York Academy of Sciences


Richard Bonneau, PhD

New York University

Richard Bonneau is an Assistant Professor at New York University’s Center for Genomics and Systems Biology and is jointly appointed at the Courant Institute for Mathematical Sciences. His lab is focused on a number of computational biology problems that aim to resolve key bottlenecks in biology and systems biology. His work focuses on two main categories of computational biology: learning networks from functional genomics data and predicting and designing protein and peptoid structure. In both areas Dr. Bonneau and his group have played key roles in achieving critical field-wide milestones. In the area of structure prediction Dr. Bonneau was one of the early authors on the Rosetta code, which was one of the first codes to demonstrate accurate and comprehensive ability to predict protein structure in the absence of sequence homology. His lab has also made key contributions to the areas of genomics data analysis with focus on two main areas: 1) methods for network inference that learn dynamics and topology from data (e.g. the Inferelator) , and 2) methods that learn condition dependent co-regulated groups from integrations of different genomics data-types (e.g. cMonkey integrative biclustering). Dr. Bonneau strives to develop new methods that let systems-biologists specify functional relevant to biology and parameters from data automatically.

Saeed Tavazoie, PhD

Princeton University

Saeed Tavazoie is a professor of molecular biology and a member of the Lewis-Sigler Institute for Integrative Genomics at Princeton University. He received a PhD in biophysics from Harvard University in 1999. Dr. Tavazoie uses genomic and computational methods to study the structure, function, and evolution of regulatory networks across organisms ranging from bacteria to humans.

Yuhai Tu, PhD

IBM T.J. Watson Research Center

Yuhai Tu graduated from the Gifted Youth Program at University of Science and Technology of China (USTC) in 1987. In the same year, he passed the CUSPEA exam and went to University of California, San Diego (UCSD) for his graduate studies in physics. In 1991, he received his PhD from UCSD and was awarded the Division Prize Fellow at California Institute of Technology (Caltech) for his postdoc research. In 1994, he joined the IBM’s Thomas J. Watson Research Center as a permanent research staff member. He has been the manager of the theory and computational physics group at the Research Center since 2002. His main research interests are statistical physics, nonlinear dynamics, surface physics and, most recently, computational biophysics. He has been an American Physical Society (APS) Fellow since 2004.


For sponsorship opportunities please contact Cristine Barreto at or 212.298.8652.



Learning Biological Networks: From Modules to Dynamics

Richard Bonneau, PhD, New York University

Learning regulatory networks from genomics data is one of the most important problems in biology today, with applications spanning biology and biomedicine. There are, however, a lot of reasons to believe that regulatory network inference is beyond our current reach due to the combinatorics of the problem, factors we can’t (or don’t often) collect genome wide measurements for, and dynamics that elude cost-effective experimental designs. In spite of these challenges multiple groups have recently shown that we can reconstruct large fractions of many prokaryotic regulatory networks from compendiums of genomics data and that these global regulatory models can be used to predict the dynamics of the transcriptome. Field-wide we are on the verge of characterizing several prokaryotic systems well enough that these global regulatory models can be combined with modeling of metabolic and signaling networks to model the global operation of cells with unprecedented completeness and accuracy. I’ll review an overall strategy for the reconstruction of global networks resulting from the recent progress of several genomics consortia that involves three main components: 1) coordinating experimental design with planned analysis, 2) estimation of condition dependent co-regulated groups, and 3) automatic inference of dynamic models of regulatory networks. I will provide specific examples of the application of this overall strategy to Halobacterium salinarum, a multi-species collection of gram-positive bacteria including B. subtilis, and T-cell differentiation. For a not totally out of date review on the topic see:

The Physics of Biological Information Processing:  E. coli's Memory and Computation

Yuhai Tu, PhD, IBM T.J. Watson Research Center

Biological organisms need to process environmental information accurately in order for them to make proper decisions critical for their survival and proliferation. Great progress has been made in identifying the key molecules responsible for different biological signaling systems. However, it remains a challenge to understand system-level behaviors from the molecular-level knowledge of various signaling pathways. In this talk, I will present some of our recent work in modeling bacterial chemotaxis (cells’ ability to sense and guide their motion towards favorable chemical environments). Based on molecular biology and biochemistry of the E. coli chemotaxis signaling pathway, we will address several system-level questions on E. coli’s signal processing and decision making processes: 1) Does E. coli have memory? 2) What kind of computation does the cell perform? 3) How does the cell use its memory and computation capability to sense and respond to a minute chemical gradient (nutrient or toxin) among a wide range of background? 4) What are the cost and limitation of the signaling processes?

Genetic Basis of Adaptation to Extreme Environments

Saeed Tavazoie, PhD, Princeton University

Microbial organisms adapt to their native habitats over geological timescales. These environments are defined by characteristic physico-chemical parameters that are structured in both space and time. What happens when a microbe is introduced into a new environment? How quickly does it adapt and what are the generic mechanisms by which adaptation comes about? Their rapid generation time and large population size has allowed us to explore these questions on the timescale of laboratory experiments. We have found that bacteria, such as E. coli, readily adapt to a variety of extreme environments. By using experimental evolution and systems-level analysis of genetic perturbations, we have identified the generic mechanisms that underlie adaptation. The emerging insights challenge prevailing views on beneficial mutations and reveal the dominant role of regulatory constraints on the expression of phenotypic capacities.

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