Overview and Features of SBPD

The SBPD extension package (SBPD) for the Systems Biology Toolbox 2 adds high-speed simulations, combination of models, experiments, and measurement data in so called projects. Functions are available that support the complete model building process (modeling, simulation, identifiability analysis, model reduction, parameter estimation (multiple experiment and multiple measurement fitting), validation, etc.). The projects are a powerful construct that allows to keep a perfect overview over your modeling task at any time. Graphical user interfaces support the workflow.  

Main Features SBPD

  • Combining models, experiments, and measurement data to projects
  • Automatic generation of C-code simulation models, resulting in 30-200 x faster simulation than MATLAB integrators
  • Parameter estimation (multiple experiment, multiple measurement)
  • Parameter fit analysis
  • Analysis of residuals
  • Identifiability analysis
  • Model reduction
  • 37 preinstalled kinetic rate laws
  • Dynamic sensitivity analysis (Parameters, Initial conditions)
  • Manual tuning of model parameters
  • Intuitive and powerful GUIs
  • SBPD can easily be used by users who have no programming knowledge!

Project Representation

Projects combine models, experiments, and measurements and allow you total control over your modeling projects. Functions, such as manual parameter tuning, parameter estimation, model reduction, identifiability analysis, etc. can directly be applied to such projects.

High Performance Simulation

For parameter estimation purposes the simulation speed is of crucial importance. Therefor, the SBPD package does not rely on the standard integrators and compilers that are inbuild in Matlab. Instead the simulations are performed by converting models to C-code and using the CVODEs integrator package from SUNDIALS. The benchmark ODE15s vs. SBPD shows that due to that the simulation performance increases by a factor somewhere between 30 and 150.

Manual Parameter Tuning

The manual parameter tuning function allows you to change parameter values and displays the results of the insilico experiments that are defined in the project in realtime. This is a very valuable function for getting an insight into the effect of different parameters and a good way of giving a model its finally accepted parameter settings.


Parameter Estimation

Parameter estimation can be applied directly to a project. Any optimization method that is available in MATLAB can be used. However, to be independent of the Optimzation Toolbox several local and global optimization methods are available in the SBTOOLBOX2. The parameter estimation functionality in SBPD automatically takes care of multiple experiments and multiple datasets in your projects.

Identifiability Analysis

Prior to performing parameter estimation it is a good idea to assess the identifiability of the models parameters. Otherwise the estimation might return unreliable results. Identifiability of parameters depends on the structure of the model, but also on the experiments that have been performed and the components that have been measured. The SBPD package contains functionality to directly apply identifiability analysis to projects.

Model Reduction

Models of biochemical systems are usually highly overparameterized with regard to the available measurement data. This renders the parameter estimation task difficult due to indentifiability issues. One possibility is to reduce a models complexity. The SBPD package contains model reduction functionality that can directly be applied to a chosen model in a project.

Additional Features

Additionally, the SBPD package contains many auxiliary functions that are useful for facilitating the access to projects or data elements, writing own scripts, and last but not least to make it independent of other commercial MATLAB toolboxes and third party packages. However, for just a few function the presence of the Symbolic Toolbox is required.


User's Reference Complete SBPD user's reference guide. All functions of the package are explained in detail and by examples.
Benchmark Benchmark between SBPD simulation and standard MATLAB simulation using ODE15s.


Publications, discussing results obtained with the help of the SBPOP PACKAGE, are asked to reference the most relevant of the following papers and additionally the link to this webpage:

  • Systems Biology Toolbox for MATLAB: A computational platform for research in Systems Biology, Bioinformatics, 22(4), 514-515, 2006, doi:10.1093/bioinformatics/bti799
  • SBaddon: high performance simulation for the Systems Biology Toolbox for MATLAB, Bioinformatics, 23(5), 646-647, 2007, doi:10.1093/bioinformatics/btl668
  • SBPOP Package: Efficient support for model based drug development from mechanistic models to complex trial simulation, PAGE meeting, Glasgow, UK [abstract]
  • Enhancing population pharmacokinetic modeling efficiency and quality using an integrated workflow, Journal of Pharmacokinetics and Pharmacodynamics, doi:10.1007/s10928-014-9370-4, 2014.
  • 16th March 2015: Many small improvements ... also SBML export now fully working again
  • 20th January 2015: Many improvements ... Windows 64bit support and compatibility with MATLAB versions >=R2014B
  • 28th July 2014: Happy to announce that our paper about efficient conduct of popPK anlalyses has been published (doi:10.1007/s10928-014-9370-4)
  • 3rd July 2014: Update to Revision 1361 (due to packaging bug in Rev 1352)
  • 18th June 2014: Update to Revision 1352 (popPK modeling workflow MONOLIX and NONMEM, "median" modeling support)
  • 25th March 2014: Update to Revision 1278
  • 7th May 2013: Revision 1172 had a minor bug due to packaging of the public version - main impact on running SBPDgui. Fixed now in Revision 1176
  • 2nd May 2013: SBTOOLBOX2 and SBPD are now integrated into the same package, called "SBPOP PACKAGE". The new combined package additionally includes "SBPOP", focusing on PK/PKPD/PBPK models, population modeling, nonlinear mixed effect parameter estimation, clinical trial simulation