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The paper concerns the problem of fitting mathematical models of cell signaling pathways. Such models frequently take the form of sets of nonlinear ordinary differential equations. While the model is continuous in time, the performance index used in the fitting procedure, involves measurements taken at discrete time moments. Adjoint sensitivity analysis is a tool, which can be used for finding the gradient of a performance index in the space of parameters of the model. In the paper a structural formulation of adjoint sensitivity analysis called the Generalized Backpropagation Through Time (GBPTT) is used. The method is especially suited for hybrid, continuous-discrete time systems. As an example we use the mathematical model of the NF-kB regulatory module, which plays a major role in the innate immune response in animals.
Biology and genetics, modeling, ordinary differential equations, parameter learning

M. Kimmel, K. Fujarewicz, A. Świerniak and T. Lipniacki, "Adjoint Systems for Models of Cell Signaling Pathways and their Application to Parameter Fitting," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. , pp. 322-335, 2007.
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