SimBiology 

SimBiology lets you estimate model parameters by fitting the model to experimental timecourse data, using either nonlinear regression or nonlinear mixedeffects (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 groupspecific 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, interiorpoint, pattern search, genetic algorithm, and particle swarm optimization. By default, SimBiology performs an ordinary leastsquares regression. You can perform a weighted leastsquares regression by specifying either a weights vector or a weighting function of observed or predicted responses.
SimBiology provides nonlinear mixedeffects (NLME) methods to simultaneously fit population data. The following NLME algorithms are included:
SimBiology provides standard goodnessoffit statistics and diagnostic plots that can be used to determine the quality of a fit and guide model selection. Goodnessoffit statistics include: