COMPUTATIONAL CELL BIOLOGY: ANALYSIS OF AUTOCRINE LOOPS

Massachusetts Institute Of Technology, Cambridge MA

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Abstract

We propose to develop mathematical models and computational tools enabling the spatially resolved analysis of reaction and transport effects in autocrine signaling. The spatial character of autocrine loops will be examined on the length scales of nanometers (using stochastic Brownian Dynamic simulations) and microns (using deterministic reaction-diffusion equations). Our modeling effort will naturally complement the experimental program in sponsor's group that is actively focusing on binding, trafficking, signaling and physiological aspects of autocrine loops in the Epidermal Growth Factor Receptor (EGFR) system. A combination of computational technique with experimental data available for the EGFR will enhance our understanding of the system that is critical in many aspects of cancer progression. A major difficulty with investigating autocrine systems is their highly localized nature. It is extremely difficult to directly measure parameters such as release rates, and diffusion distances. These parameters, however, determine the functional role of autocrine loops. According to the recently proposed 'cell sonar' hypothesis, spatially restricted networks of autocrine growth factors provide cells with positional information. Localized interruption of an autocrine loop (e.g. by binding of secreted ligand to components of the extracellular matrix) will be mirrored by a localized perturbation of a binding pattern that, in its turn, might be translated into a localized physiological response (e.g. protease activation of pseudopod extension). Our models will provide a critical quantitative evaluation of the hypothesized signaling mechanism that might be operative in a variety of normal and pathological contexts.