Model, simulate, and analyze biological systems

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SimBiology Community

A gathering place for scientists working in QSP, PBPK, and PK/PD modeling using SimBiology and MATLAB.

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 using SimBiology Model Builder.

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.

Store alternative quantity values as model variants.

Store alternative quantity values as model variants.

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 with a variety of deterministic and stochastic solvers using SimBiology Model Analyzer or programmatic tools.

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.

Specify units and automatically perform unit conversion.

Specify units and automatically perform 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.

Improve performance of simulations by scaling up to clusters and cloud.

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.

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.

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 the nonlinear mixed-effects method.

Progress plots for the 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.

Global and Local Sensitivity Analyses

Explore the effects of variations in model quantities on model response by performing local or global sensitivity analysis. Use global sensitivity analysis to understand which model inputs drive model response across a parameter space and inform parameter estimation strategy.

Custom Analysis

Use SimBiology programmatically with MATLAB scripts to automate analyses and create custom analyses. You can also use community contributed tools as add-ons to perform custom analyses on your SimBiology model such as virtual population simulations.

Community contributed tools from SimBiology Online Community.

Community contributed tools from SimBiology Online Community.

Deploying Models

Create model exploration applications using App Designer and package them with MATLAB Compiler. Share SimBiology simulations with collaborators, who do not have access to MATLAB and SimBiology, without needing to expose modeling details.

Building and Deploying Web Apps

Create apps using App Designer, package them using MATLAB Compiler, and host them using MATLAB Web App Server. Collaborators can access and run the web apps using a browser without installing any software.