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Theoretical modeling of signal processing systems in biology


The survival of biological organisms relies on their capacity to sense and respond to various environmental signals. Such capacity is limited by stochasticity of both intracellular and extracellular signaling events and by potential cross-talk of signaling pathways. Understanding the performance of biological signaling networks is essential for a comprehensive explanation of biological mechanisms and functions. This dissertation presents theoretical models, primarily developed by the dissertation author, for various biological signal processing systems, including eukaryotic gradient sensing, chemotactic response, enzymatic switch system under noisy input, and specificity of yeast signaling networks. In those studies, we have employed a wide range of concepts and techniques in statistical mechanics, estimation theory, stochastic process, stochastic differential equation, numerical optimization, and Monte-Carlo simulations. In the analysis of gradient sensing limits (Chapter 2), we found that the accuracy of spatial gradient sensing is highly sensitive to the cell size and that the spatial sensing strategy may also be applicable to some small bacteria with the aid of receptor cooperativity. In the study of chemotactic response (Chapter 3), we developed an exactly solvable model which incorporate different sources of noise and can well explain the experimentally observed heavy-tails in the directional distribution of chemotactic cells. Chapter 4 devotes to the study of a simple biological switching system (e.g., gene promoter and bacterial motor) governed by fluctuating input signals. Our analytical results question the common belief that input noise will always contributes additively to the output variation. Finally, in the context of signaling specificity, we discovered and proposed a general design principle, namely the asymmetric hierarchical inhibition mechanism, for signaling pathways to avoid cross-talk and achieve specificity

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