SimBiology

Model, simulate, and analyze biological systems

SimBiology^® provides apps and programmatic tools for modeling, simulating, and analyzing dynamic systems, focusing on quantitative systems pharmacology (QSP), physiologically-based pharmacokinetic (PBPK), and pharmacokinetic/pharmacodynamic (PK/PD) applications. You can build models interactively using the SimBiology block diagram editor or programmatically using the MATLAB^® language. Your models can be created from scratch, imported as SBML formatted files, or built on the model examples provided in SimBiology.

SimBiology provides a variety of techniques for analyzing ODE-based models ranging in complexity and size. You can run simulations to assess target feasibility, predict drug efficacy and safety, and identify optimal dosing schedules. You can identify key pathways and parameters using local and global sensitivity analyses and assess biological variability by running parameter sweeps. To estimate parameters you can fit data using nonlinear regression and nonlinear mixed-effects techniques and perform non-compartmental analysis (NCA).

What Is SimBiology?

SimBiology Community

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

Explore now

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.

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.

Build Models

Using Observables to Perform Post-Simulation Calculations

SimBiology Tutorials: Building a Model – Differential Equations

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.

Variants

Simulate Model of Glucose-Insulin Response with Different Initial Conditions

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.

Doses

SimBiology Tutorials: Working with Doses in SimBiology

Simulating Models

Simulate the dynamic behavior of your model with a variety of deterministic and stochastic solvers using SimBiology Model Analyzer or programmatic tools.

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.

Choosing a Simulation Solver

Comparing SSA and Explicit Tau-Leaping Stochastic Solvers

SimBiology Tutorials: Simulating a Model in SimBiology

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.

Perform Unit Conversion

Perform Dimensional Analysis

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™.

Accelerating Model Simulations and Analyses

SimFunction Object

PK/PD Modeling and Simulation to Guide Dosing Strategy for Antibiotics

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.

Noncompartmental Analysis

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.

Nonlinear Regression

Supported Methods for Parameter Estimation

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.

Nonlinear Mixed-Effects Modeling

Supported Methods for Parameter Estimation

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.

Built-In Programs and Interactive Exploration Tools

Compose analysis programs using built-in analysis steps with the SimBiology Model Analyzer app. Use sliders to interactively explore the effects of variations in parameters or dose schedules on model outcomes.

SimBiology Model Analyzer

SimBiology Tutorials: Stratifying Data for Visualization in SimBiology (7:00)

SimBiology Tutorials: Sampling Parameters from Covariate Models in SimBiology (3:31)

SimBiology Tutorials: Generating Simulation Scenarios by Sampling Model Quantities

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.

Global Sensitivity Analysis App

Sensitivity Analysis in SimBiology

Parameter Scanning, Parameter Estimation, and Sensitivity Analysis

Global Sensitivity Analysis with SimBiology

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.

gQSPSim: A MATLAB App for QSP Model Analysis, Including Simulation of Virtual Populations

QSP Toolbox: Workflow Components for Quantitative Systems Pharmacology

VQMTools: for Quantitative Systems Pharmacology (QSP) modelling

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.

Deploy a SimBiology Model