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NJIT Mathematical Biology Seminar

Tuesday, April 28, 2009, 4:00pm
Cullimore Hall 611
New Jersey Institute of Technology

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Stochastic phenomena in chemotaxis

Peter Thomas

Department of Mathematics, Case Western State University

Chemical reaction networks by which individual cells gather and process information about their chemical environments have been dubbed "signal transduction" networks. Despite this suggestive terminology, there have been few attempts to analyze chemical signaling systems with the quantitative tools of information theory. Gradient sensing in eukaryotic cells such as the social amoeba (Dictyostelium discoideum} and human polymorphonuclear leukocytes (neutrophils) comprise a well characterized class of signal transduction systems that perform chemotaxis (directed cell motion guided by chemical gradients). During gradient sensing, a cell estimates the direction of a source of diffusing chemoattractant molecules based on the spatiotemporal sequence of ligand-receptor binding events at the cell membrane. The local directional signal results from a combination of diffusion of signaling molecules from nearby cells and interactions between these molecules and receptor proteins on the cellular surface. The signal ``received'' by the ensemble of membrane bound receptors is ``processed'' further by an intracellular signaling cascade. Eukaryotic cells show exquisite ability to navigate even in shallow gradients and low concentrations of signaling molecules. Under such conditions the intrinsically stochastic nature of both the chemical reactions and diffusion of intracellular signaling molecules give rise to natural variability around the mean response. This talk will explore several aspects of the problem, including (1) estimates of the optimal gradient detection accuracy within a maximum likelihood framework, (2) exploration of the information capacity of purely diffusion-mediated singaling processes, and (3) explicit Monte Carlo methods for exploring the propagation of uncertainty through the intracellular reaction network. Our long range goal is to provide a quantitative framework for addressing the question: how much could the cell know, and when?