Parameter Estimation for Signal Transduction Networks from Experimental Time Series Using Picard Iteration

Abstract Biological signal transduction models allow to explain and analyze biological cause-effect relationships and to establish and test new hypotheses about biological pathways. Yet their predictive capability crucially depends on the parameters involved. These parameters are usually determined from experimental data. However, due to the appearing nonlinearities, the resulting inverse problem is often ill-posed and difficult to solve. We outline how parameters can be estimated based on Picard iterations. In case of linear parameter dependence and good measurements of the involved entities, the method allows to retrieve good parameter estimates for medium size problems. The proposed method is applied to an IL-6-dependent Jak-STAT3 signalling pathway model. As shown it it is well suited for data generated by life cell imaging where accurate time series are available.

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