Global parameter estimation for a eukaryotic cell cycle model in systems biology

The complicated process by which a yeast cell divides, known as the cell cycle, has been modeled by a system of 26 nonlinear ordinary differential equations (ODEs) with 149 parameters. This model captures the chemical kinetics of the regulatory networks controlling the cell division process in budding yeast cells. Empirical data is discrete and matched against discrete inferences (e.g., whether a particular mutant cell lives or dies) computed from the ODE solution trajectories. The problem of estimating the ODE parameters to best fit the model to the data is a 149-dimensional global optimization problem attacked by the deterministic algorithm VTDIRECT95 and by the nondeterministic algorithms differential evolution, QNSTOP, and simulated annealing, whose performances are compared.

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