Model, simulate, and analyze biological systems


SimBiology provides apps and programmatic tools to model, simulate, and analyze dynamic systems, focusing on pharmacokinetic/pharmacodynamic (PK/PD) and systems biology applications. It provides a block diagram editor for building models, or you can create models programmatically using the MATLAB® language. SimBiology includes a library of common PK models, which you can customize and integrate with mechanistic systems biology models.

A variety of model exploration techniques let you identify optimal dosing schedules and putative drug targets in cellular pathways. SimBiology uses ordinary differential equations (ODEs) and stochastic solvers to simulate the time course profile of drug exposure, drug efficacy, and enzyme and metabolite levels. You can investigate system dynamics and guide experimentation using parameter sweeps and sensitivity analysis. You can also use single subject or population data to estimate model parameters.

Get Started:

Building Models

Construct quantitative systems pharmacology (QSP), physiologically-based pharmacokinetic (PBPK), or pharmacokinetic/pharmacodynamic (PK/PD) models just as you would draw them on a piece of paper.

Specifying Model Dynamics

Use the drag-and-drop block diagram editor or programmatic tools to build QSP, PBPK, or PK/PD models. Import existing models from Systems Biology Markup Language (SBML) files.

Diagram view of a diabetes QSP model.

Creating Model Variants

Use model variants to store a set of parameter values or initial conditions that differ from the base model configuration. Easily simulate virtual patients, drug candidates, alternate scenarios, and what-if hypotheses without creating multiple copies of your model.

Model variants table.

Evaluating Dosing Strategies

Define and evaluate dosing strategies. Assess the benefits of combination therapies and determine optimal dosing strategies by combining dosing schedules that target different model species.

Simulating Models

Simulate the dynamic behavior of your model using a variety of deterministic and stochastic solvers.

Choosing a Solver

Select one of several available deterministic solvers, including MATLAB ODE solvers and the SUNDIALS solvers, or choose one of the stochastic solvers, including stochastic simulation algorithm (SSA), explicit tau-leaping, and implicit tau-leaping.

Automating Unit Conversion

Choose the units most appropriate for your model; for example, specify the dose amount in milligrams, drug concentration in nanograms/milliliter, and plasma volume in liters. Unit conversion tools convert all quantities in your model and data to a consistent unit system.

Specifying units and performing unit conversion.

Accelerating Simulations

Accelerate simulation of large models or Monte Carlo simulations by converting models to compiled C code. Further improve performance by distributing simulations across multiple cores, clusters, or cloud computing resources using Parallel Computing Toolbox™.

Scaling up to clusters and cloud for improving performance.

Estimating Parameters

Estimate model parameters by fitting your model to experimental time-course data. Compute PK parameters by performing noncompartmental analysis (NCA).

Noncompartmental Analysis

Compute pharmacokinetic parameters of a drug from the time course measurements of drug concentrations without assuming a compartmental model. Perform NCA on both experimental and simulation data for single or multiple dosing, using sparse or serial sampling.

AUC calculation for concentration-time data shown in linear and semilogarithmic scales.

Nonlinear Regression

Estimate parameters using local or global estimation methods and calculate confidence intervals for parameters and model predictions. Fit each group independently to generate group-specific estimates or simultaneously fit all groups to estimate a single set of values.

Gaussian parameter confidence intervals of a two-compartment PK model.

Nonlinear-Mixed Effects Techniques (NLME)

Use NLME methods to fit population data using Stochastic Approximation of Expectation-Maximization (SAEM), first-order conditional estimate (FOCE), first-order estimate (FO), linear mixed-effects (LME) approximation, or restricted LME approximation.

Progress plots for nonlinear mixed effects method.

Analyzing Models

Perform sensitivity analysis, parameter sweeps, and Monte Carlo simulations to explore the influence of parameters and conditions on model behavior.

Built-In Tasks and Interactive Exploration Tools

Use built-in analyses to analyze models. Use sliders to interactively explore the effects of variations in parameters or dose schedules on model outcomes.

Task Editor showing the effects of various parameter values and dosing schedules.

Custom algorithm used to identify an optimal infusion rate.

Deploying Models

Create model exploration applications with MATLAB Compiler™ and share them with others who do not have access to MATLAB and SimBiology. Distribute models without exposing your intellectual property.

Creating Apps with SimBiology Desktop

Create standalone model exploration apps with one click using SimBiology Desktop.

App created with SimBiology Desktop showing anti-TNF treatment results.

Custom app showing simulation results for combination therapy.

Latest Features


Perform post-simulation calculations, for example to calculate area under the curve (AUC), and use it as a response for simulation, data fitting, or global sensitivity analysis

Global Sensitivity Analysis (GSA)

Explore the effects of variations in model quantities on model response by computing Sobol indices and by performing multiparametric GSA

See release notes for details on any of these features and corresponding functions.