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).
Get Started:
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.
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.
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.
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.
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.
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.
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 the nonlinear mixed-effects method.
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.
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.
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.
