| Poster PS31A | |
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| Stephen Fleming |
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| Case Western Reserve University |
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| Title | Accuracy of Gradient Sensing Based on Maximum Likelihood |
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| Abstract | Studies of neutrophil chemotaxis have revealed that chemotactic signals are mediated by extracellular chemokines such as interleukin-8 and fMLP. Single-cell imaging has revealed that receptor-specific binding occurs at the cell membrane, and that this process triggers several intracellular signaling pathways. These signaling cascades transmit information about the cell’s surroundings to the machinery that directs cell motility. We propose that maximum likelihood estimation based on the state of a cell’s receptors at a given moment in time can be used to construct a guess of the direction of a chemoattractant gradient surrounding the cell and describe the direction of cell migration. The information a cell receives from its receptors thus limits the accuracy of its best estimate of the true gradient direction. For any given value of the relative concentration gradient, maximum likelihood estimates predict an optimal value of the mean concentration for movement accuracy as measured by chemotactic index. This optimal value corresponds to the equilibrium constant assumed for the receptor-ligand interaction, as observed experimentally. We also construct a maximum likelihood estimate based on receptor states that fluctuate over time. The distribution of estimates obtained via a maximum likelihood procedure appears to be well fit by a von Mises angular distribution. For shallow gradients we obtain an analytic approximation for the von Mises concentration parameter as a function of relative gradient steepness. |
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| Coauthors | Heather McGinnis, Peter Thomas, Harihara Baskaran, Saheli Sarkar |
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| Location | Woodward Lobby (Monday-Tuesday) |
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