Biological Design Principles For Robustness, Performance and Selective Interactions with Noise: A Systems Engineering Perspective

Dr. Hana El-Samad

Department of Biochemistry and Biophysics
California Institute for Quantitative Biomedical Research (QB3)
University of California at San Francisco

Abstract --. Systems biology is a discipline that emphasizes the systemic characterization of biological networks. Such a view of biological organization is not aimed at replacing experimental approaches. To the contrary, it is aimed at complementing these approaches and drawing on mathematical methods developed in the context of control, dynamical systems, and computational theories in order to create powerful simulation and analysis tools that decipher existing data, devise new experiments, and guide experimental efforts for engineering biological systems with novel functionalities and applications.

In this talk, we advocate the use of a systems’ approach in the modelling and analysis of gene regulatory networks. As an illustration, we summarize our work on the modeling of the heat shock response, a cellular system of substantial physiological importance. The heat shock response refers to the mechanism by which organisms react to a sudden increase in the ambient temperature. The consequences of such an unmediated temperature increase at the cellular level is the unfolding, misfolding, or aggregation of cell proteins, which threatens the life of the cell. To combat such effects, cells have evolved an intricate set of feedback and feedforward mechanisms. We illustrate how mathematical models of these mechanisms provide valuable insight, explaining dynamic phenomena exhibited by wild type and mutant heat shock responses, corroborating experimental data and guiding novel biological experiments. Since gene regulatory networks, including the heat shock system, are permanently affected by various sources of noise, we briefly discuss some principles of noise attenuation and exploitation that seem to be at work in these circuits. Specifically, we point to some gaps in the current understanding of the nature of biochemical noise, and discuss its repercussions on the interpretation of single-cell experimental data. At the same time, we point to the presence of characteristic noise “fingerprints” of certain network features, which are useful for experimental system identification. We finally discuss how the successful exploitation of experimental noise measurements for system identification is intimately connected to the development of sound mathematical frameworks and efficient simulations techniques for the description of stochastic biological dynamics.

Friday, April 14, 2006

3:30 – 4:30p.m.

Rm. 1500 EECS