SimBiology lets you estimate model parameters by fitting the model to experimental time-course data, using either nonlinear regression or nonlinear mixed-effects (NLME) techniques.
SimBiology provides nonlinear regression methods to fit data for a single individual or a population. With population data, you can either fit each group independently to generate group-specific estimates or simultaneously fit all groups (pooled approach) to estimate a single set of values.
You can perform nonlinear regression using optimization algorithms from Statistics Toolbox™, Optimization Toolbox™, and Global Optimization Toolbox, including simplex search, interior-point, pattern search, genetic algorithm, and particle swarm optimization. By default, SimBiology performs an ordinary least-squares regression. You can perform a weighted least-squares regression by specifying either a weights vector or a weighting function of observed or predicted responses.
SimBiology provides nonlinear mixed-effects (NLME) methods to simultaneously fit population data. The following NLME algorithms are included:
SimBiology provides standard goodness-of-fit statistics and diagnostic plots that can be used to determine the quality of a fit and guide model selection. Goodness-of-fit statistics include: