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• W2013640963 abstract "Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories." @default.
• W2013640963 created "2016-06-24" @default.
• W2013640963 creator A5022135015 @default.
• W2013640963 creator A5031437953 @default.
• W2013640963 date "2013-12-01" @default.
• W2013640963 modified "2023-10-07" @default.
• W2013640963 title "Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates" @default.
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• W2013640963 doi "https://doi.org/10.1016/j.mbs.2013.03.011" @default.
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