Written and maintained by CASRAI Editorial Board
Last updated
SimBiology is a MathWorks software product, built on MATLAB, for modeling, simulating and analyzing dynamic systems, with a focus on quantitative systems pharmacology (QSP), physiologically based pharmacokinetic (PBPK) and pharmacokinetic/pharmacodynamic (PK/PD) models. Researchers use it to build models graphically or in code, simulate them, fit them to data and run sensitivity analyses. It is a research modeling tool, not a clinical decision tool. (Source: MathWorks, SimBiology product page, read 2026-11-08.)
Definition and scope
MathWorks describes SimBiology as a tool for modeling, simulating and analyzing dynamic systems. Its documentation states that it simulates models based on ordinary differential equations, and lists uses such as testing the feasibility of a drug target, predicting drug efficacy and safety, and finding dosing schedules in a modeling context. Sensitivity analyses are used to identify key parameters and to assess variability (MathWorks SimBiology documentation, read 2026-11-08).
For the surrounding discipline, see what pharmacology is; for how modeling is used in regulatory work, see PBPK modeling for regulatory submissions.
How SimBiology is used in practice
Building a model
According to MathWorks, models can be built in the Model Builder block diagram editor or in MATLAB code. A model can be started from scratch, imported from an SBML (Systems Biology Markup Language) file, or begun from an example. Dose objects describe dosing strategies, and variant objects hold alternative values of model quantities, so one model structure can be run under several scenarios.
Simulating and analyzing
Models can be simulated with deterministic and stochastic solvers, through the Model Analyzer app or programmatically. The product page lists automatic unit conversion and report generation. MathWorks also lists parameter sweeps, global, Monte Carlo and local derivative-based sensitivity analysis, and Monte Carlo simulation.
Fitting models to data
Parameter estimation uses local, global or hybrid nonlinear regression and provides confidence intervals. Nonlinear mixed-effects (NLME) estimation handles fixed and random effects; MathWorks defines it as maximum likelihood estimation of population parameters.
Scaling up and sharing
Large models and Monte Carlo runs can be converted to compiled C code and distributed across cores, clusters or cloud; the page names Parallel Computing Toolbox as an optional component for this. Apps built with App Designer can be packaged with MATLAB Compiler and hosted on MATLAB Web App Server.
Model Builder versus Model Analyzer
The Model Builder app is for interactively building QSP, PK/PD and systems biology models. The Model Analyzer app is for simulating with deterministic or stochastic solvers, calibrating a model to data, running sensitivity analysis and Monte Carlo simulation, and for non-compartmental analysis of experimental and simulation data (MathWorks app documentation, read 2026-11-08).
Which task maps to which capability
| Task | SimBiology capability named by MathWorks |
|---|---|
| Build a mechanistic QSP, PK/PD or systems biology model | Model Builder app or MATLAB code; import from SBML |
| Compare dosing strategies or model variations | Dose and variant objects |
| Simulate time courses | Deterministic and stochastic solvers, via Model Analyzer or code |
| Estimate parameters from one dataset | Local, global or hybrid nonlinear regression with confidence intervals |
| Estimate population parameters with fixed and random effects | NLME (maximum likelihood estimation of population parameters) |
| Obtain PK parameters from concentration-time data without a compartmental model | Non-compartmental analysis (NCA), single or multiple dosing |
| Find which parameters drive the output | Global, Monte Carlo and local sensitivity analysis |
| Speed up large models | Compiled C code; parallel distribution |
NCA versus NLME
These answer different questions. NCA derives PK parameters directly from concentration-time data, with no compartmental model assumed. NLME fits a model and estimates population-level parameters including random effects. For a related PK scaling method, see allometric scaling in pharmacokinetics.
Where it matters for research groups
The following is an editorial checklist built from the features above, not a MathWorks recommendation.
- Exchange format: SBML import means a model published in that format can be a starting point rather than being rebuilt.
- Explicit scenarios: keeping doses and variants as named objects, rather than hand-edited parameter values, makes each scenario traceable.
- Units: automatic unit conversion is a documented feature; record the units used for each dataset alongside the model.
- Scripted runs: because simulations and analyses can be run programmatically, a project can keep the code that produced each result under version control. See computational reproducibility.
- Sharing: packaged apps let collaborators run a model without writing MATLAB code.
For MATLAB workflows more broadly, see integrating MATLAB. A community-built interface, gQSPSim, runs on top of MATLAB, SimBiology, Statistics and Machine Learning and GUI Layout toolboxes, and its authors note that MATLAB scripting knowledge is needed (PMC7080534, read 2026-11-08).
Common misunderstandings
- It is not a standalone program. SimBiology is MATLAB-based, so it runs inside the MATLAB environment.
- It does not replace clinical judgment. It simulates models that researchers build; outputs depend on the model structure and data supplied. Nothing here is medical advice.
- NCA is not model fitting. NCA needs no compartmental model; parameter estimation and NLME do.
- Systems biology is not limited to drugs. The tool also targets general dynamic systems, with pharmacology as its stated focus.
FAQ
What is SimBiology used for?
Modeling, simulating and analyzing dynamic systems, mainly QSP, PBPK and PK/PD models (MathWorks, 2026-11-08).
Can SimBiology import models from other tools?
MathWorks states it can import SBML files.
Does SimBiology run stochastic simulations?
Yes. Deterministic and stochastic solvers are both listed.
Can results be shared with people who do not use MATLAB?
MathWorks describes packaging apps with MATLAB Compiler and hosting them on MATLAB Web App Server.
Sources
- MathWorks, SimBiology product page, read 2026-11-08.
- MathWorks, SimBiology documentation, read 2026-11-08.
- MathWorks, Model Builder app and Model Analyzer app, read 2026-11-08.
- gQSPSim, PMC7080534, read 2026-11-08.
