Modeling and analysis challenges in systems biology

Recent advances in molecular biology techniques provide an opportunity for developing detailed mathematical models of biological processes. An iterative scheme is introduced for model identification using available system knowledge and limited experimental measurements. The scheme includes a state regulator algorithm that estimates concentrations of the components of the system and also the reaction rates of their inter-conversion. The full system estimates are used for estimation of model parameters. An optimal experiment design using the D-optimality criterion is formulated to provide "rich" experimental data for improving parameter estimates in the subsequent iterations. The importance of model identifiability tests for optimal measurement selection is also considered. The iterative scheme is used to develop a model for the caspase function in apoptosis and it is demonstrated that model performance improves with each iteration. The proposed algorithm has general application to model a wide range of cellular processes from gene regulation networks, signal transduction to metabolic networks.