SimBiology

Parameter Estimation and Fitting

SimBiology lets you estimate model parameters by fitting the model to experimental data. You can fit time-course data from a single individual using nonlinear regression. You can also use nonlinear mixed-effect (NLME) models to simultaneously fit data from a population using the following algorithms:

• Stochastic Approximation Expectation-Maximization (SAEM)
• First-order conditional estimate (FOCE)
• First-order estimate (FO)
• Linear mixed-effects approximation (LME)
• Restricted LME approximation (RELME)

SimBiology provides standard goodness-of-fit statistics, including:

• Root mean squared error (RMSE)
• Standard errors for estimated parameters
• Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)
• Population weighted residuals

SimBiology also generates diagnostic plots that can be used to visually inspect the quality of a fit.

SimBiology desktop (top) and diagnostic plots that show an individual fit (left), a scatter plot of predicted versus observed values (center), and a probability plot of residuals (right).

You can also fit data with algorithms from Statistics Toolbox™, Optimization Toolbox™, and Global Optimization Toolbox. SimBiology uses the Nelder-Mead simplex algorithm to perform unconstrained nonlinear optimization. Optimization Toolbox provides an interior-point solver for working with a relatively large sparse problem. Global Optimization Toolbox provides fitting algorithms and multistart algorithms to handle problems that contain local minima.