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Built-in Tasks

The desktop provides the following built-in tasks that you can use to analyze models.

Task NameDescriptionShows Live PlotsSupports DeploymentCommand-line Function
Simulate modelSimulates the dynamic behavior of a given model using a variety of deterministic and stochastic solvers. YesYessbiosimulate, SimFunction object
Fit dataEstimates model parameters by fitting the model to time-course data using nonlinear regression or nonlinear mixed-effects (NLME) techniques. You can fit data to a single individual to predict group-specific values or simultaneously fit all groups (pooled fit) to estimate a single set of values.


For nonlinear mixed-effects (NLME) estimation, Statistics and Machine Learning Toolbox™ is required. For nonlinear regression, Optimization Toolbox™, Global Optimization Toolbox, and Statistics and Machine Learning Toolbox are recommended.

No. The progress plot is shown instead.Nosbiofit for nonlinear regression, sbiofitmixed for NLME modeling
Calculate conserved cyclesCalculates a complete set of linear conservation relations for species in a model and returns a list of species that are conserved in the system, regardless of reaction rates.NoNosbioconsmoiety
Calculate sensitivitiesComputes the time-dependent derivatives of one or more species relative to either model parameters or species initial conditions. Calculating these time-dependent sensitivities helps you determine what effect the parameters or species have on another species or parameter.YesNosbiosimulate, SimFunctionSensitivity object
Run scanPerforms a parameter scan that lets you explore the model dynamics given different values of model quantities and doses.


Statistics and Machine Learning Toolbox is recommended for more sampling options.

YesNosbiosimulate, SimFunction object
Run scan with sensitivitiesPerforms a parameter scan while calculating sensitivities of model quantities. For each scanned value, you can see the corresponding time-dependent sensitivities of quantities with respect to the parameter of interest.


Statistics and Machine Learning Toolbox is recommended for more sampling options.

YesNosbiosimulate, SimFunctionSensitivity object
Run ensemble simulationPerforms a series of stochastic simulations of a model. When the behavior of a model is stochastic in nature, a single simulation run may not provide enough insight into the model. Use this task to perform a number of simulations.YesNosbioensemblerun
Run group simulationSimulates a model for each group in the data. For example, the grouped data can contain measurements of drug plasma concentration at different times for multiple patients and dosing information for each patient. This task performs a simulation for each patient using the corresponding dosing information from the data and compares the simulation results to the data visually in the live plots section.YesNosbiosimulate, SimFunction object
Create custom analysisLets you create a task for custom analysis using MATLAB® language. For instance, you can write a script to identify the optimal dosing strategy that suppresses tumor growth while satisfying safety constraints.NoNo
Search model(s)Lets you search models for keywords.NoNosbioselect
Generate reportCreates a report of the model and analysis results. You can select various information to be included in the report such as the diagram representation of the model, model equations, or imported data.NoNo

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