Installation

Installation of the SBPOP PACKAGE

  1. Unpack downloaded file (SBPOP_PACKAGE_Rev_>>Revision Number<<.zip) file to the desired location
  2. Start MATLAB (>= R2010a)
  3. Change into the SBPOP_PACKAGE_Rev_>>Revision Number<< folder
  4. The first time you install a new revision, run the installSBPOPpackageInitial script. This will not only make the SBPOP PACKAGE functions available in your MATLAB session, it will also compile several C-code functions and libraries for your system
  5. Ready!
The installation of the SBPOP PACKAGE is NOT saving the MATLAB path. This means that each time you close MATLAB and start it again you need to re-install SBPOP. To speed up re-installation, not compiling C-code functions and libraries, you only need to call: installSBPOPpackage, instead of installSBPOPpackageInitial.

Server installation of the SBPOP PACKAGE

This is easily possible. Please ask your system administrator to install the SBPOP Package. The sysadmin will need to run the installSBPOPpackageInitial script and then the user just starts MATLAB, runs the installSBPOPpackage script and can use SBPOP.

Recommended Third Party Packages

Installation of MONOLIX

  1. Please follow the installation information on the following webpage: http://www.lixoft.eu
  2. SBPOP detects the availability of Monolix automatically, or in some cases, where it is needed, tells the user explicitly when the path to Monolix needs to be provided and how.

Installation of the SSm Global Optimization Toolbox

  1. Download the toolbox from here
  2. Add the root folder of the SSm toolbox to the MATLAB path
  3. Save the MATLAB path - or do this each time you start MATLAB
  4. Ready!

Optional Third Party Packages

Installation of the SBMLToolbox (UNIX, Linux, MAC only)

  1. Download the SBML Toolbox from here, and follow the installation instructions included in the downloaded package
  2. Then you need to make sure that both the TranslateSBML and the OutputSBML functions are in the MATLAB path by adding the corresponding folders to the path. You can do this manually in MATLAB or by editing the path information in the installSB.m file (in the SBTOOLBOX2 folder), by setting the variable PATH_SBMLTOOLBOX

Installation of XPPAUT

XPPAUT is already included in the SBTOOLBOX2 distribution. In case of compilation problems, please refer to the XPPAUT installation documentation (SBTOOLBOX2/auxiliary/xppaut/unix_mac/install.pdf).

 

SBPOP PUBLICATIONS

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.
SBPOP NEWS
  • 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