| Abstract | Chemotaxis is the directed migration of cells guided by chemical gradients. This process plays a role in embryogenesis, immune response, wound healing and tumor metastasis. During chemotaxis, a cell detects extracellular chemoattractants and translates these signals into a complex cellular response resulting in morphological reorganization and motility. The accuracy with which a cell can determine an external chemical gradient is limited by fluctuations arising from the discrete nature of second messenger release and diffusion processes within the small volume of a living cell. These sources of intrinsic noise have the potential to attenuate or disperse gradient information contained in the membrane bound receptors. At the same time, models of the intracellular signaling network have been devised that use a combination of local excitation and global inhibition to sharpen the intracellular gradient signal. In this study, we implement a stochastic version one such model, the "balanced inactivation" model (Levine et. al. 2006), in a two dimensional geometry. We develop a fixed timestep approach in which the probabilities of individual molecules making spatial or chemical transitions is handled as a system of multinomial random variables. With this numerical platform we investigate the relationship between the amplification of the gradient signal, the propagation of noise in the signaling pathway, and fundamental limits on the accuracy of the gradient sensing mechanism. |
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