Numerical Tools for analyzing cell signaling circuits in normal and tumor cells The proposal, part I of a phased STTR award, describes the development of software tools, workflow and proof-of-principle case studies for detailed kinetic modeling of cell signaling pathways (and similarly complex biological processes). The most critical step in building kinetic models of real biological processes is optimizing the match between model prediction and experimental data. Achieving this optimization requires efficient means to explore alternative pathway topologies (which give rise to the structure of the model) and to calibrate models to data (which constrains model parameters). We address both of these problems with particular emphasis on the algorithms and software needed to perform calibration of highly parametric models based on deterministic ordinary differential equations. The size of these models, the incompleteness of experimental data, and the wide range of time scales makes effective extremely calibration challenging both in theory and in practice, making the proposal innovative and significant with respect to both its biology and its applied mathematics The project is a close collaboration between a software company (Numerical) with deep expertise in chemical modeling, combustion engineering and numerical simulation and two systems biology groups (Sorger and Gunawardena) with expertise in experimental biology and computer science respectively. Three phase I specific aims include (1) Development of the Lisp-based little b programming environment as a means to represents biological pathways and multi-component protein complexes in a rapidly- revised, reusable form suitable (2) Optimizing numerical analysis tools for calibration of biological models with a sophisticated workflow and new algorithms for adjoint-based sensitivity analysis and global parameter estimation (3) Methods for validating models and applying key findings to significant problems in systems pharmacology and drug discovery.
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