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### Inside the Mind of the Amoeba:Simulation and Analysis of Biochemical Signal Transduction Channels

**Special Events**

Speaker: | Peter Thomas, Salk Institute |

Location: | 693 Kerr |

Start time: | Thu, Jan 22 2004, 4:10PM |

In lieu of nervous systems, single-celled organisms rely on complex networks of biocehmical reactions to extract information about their environments and determine responses to chemical messages. A wealth of quantitative biological data from bioinformatics to fluorescence microscopy has created the possibility of building detailed biophysically realistic working models of the information processing occuring inside cells, in analogy to the extensive progress made by modeling realistic neural networks. We are developing conceptual, analytic and numerical tools to shed light on several aspects of this problem. I. Chemical reactants are localized within subcellular volumes, invalidating "well-mixed" ODE formulations of cellular control networks. In collaboration with cell biologists and theoretical biophysicists at UCSD we have constructed a finite-element model for solving arbitrary boundary-coupled PDEs as a platform for studying spatially heterogeneous signal-transduction networks, and used it to develop a model for the orienting response of a eukaryotic cell during directed cell movement (chemotaxis). II. Stochastic effects rising from small copy numbers of chemical reactants force us to consider the consequences of fluctuations about the mean behavior described by deterministic ODE or PDE descriptions of chemical networks. For example, the amount of information a cell can extract from a chemical signal is governed by the size of fluctuations in local concentration and in the stochastic binding of signaling molecules to its receptors. In collaboration with signal-processing engineers at UCSD we have constructed a Monte Carlo simulator of molecule-by-molecule signal transduction and have obtained lower bounds on the information capacity of a simplified biochemical signal relay, and investigated how the capacity depends on key parameters such as system geometry and reaction rates. III. Biochemical reaction networks may be represented as Markov flows on graphs of connected chemical states; these graphs are often too complicated for ready simulation or comparison to experiment. In collaboration with experimental biochemists at Caltech we are currently developing criteria for evaluating optimal sectioning of reaction graphs to provide canonical "coarse-graining" rules for complex reation networks.