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Molecular signal processing and detection in T-cells

Biological cells in mammalian immune systems use groups of molecules to process signals for the purposes of detecting and eliminating pathogens from the body. Immunologists strive to understand the key molecular components of these living detectors. The size and complexity of cells makes detailed, quantitative data very difficult to collect. One approach to modeling with limited quantitative data is to assume that the system is optimal (e.g., due to natural selection) for the function it performs. In our work, we apply this approach to an extended, stochastic version of McKeithan's model for T-cell signal transduction [9]. This model is interpreted as a binary detector on which we impose mutual information as the optimality criterion. With a model structure, a performance metric, and an optimization algorithm, we then estimate the parameters of a molecular signal processing model.