Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model
Parameter estimation from experimental data is critical for mathematical modeling of protein regulatory networks. For realistic networks with dozens of species and reactions, parameter estimation is an especially challenging task. In this study, we present an approach for parameter estimation that is effective in fitting a model of the budding yeast cell cycle (comprising 26 nonlinear ordinary differential equations containing 126 rate constants) to the experimentally observed phenotypes (viable or inviable) of 119 genetic strains carrying mutations of cell cycle genes.
optimization model reduction budding yeast cell cycle ode model phenotypic constraints latin hypercube sampling differential evolution sensitivity analysis phenotype competition