OptGpSampler for sampling constraint-based genome-scale metabolic networks

About optGpSampler

OptGpSampler is a parallel implementation of the Artificial Centering Hit-and-Run algorithm. With this tool, you can efficiently sample the steady-state solution space of a metabolic network. Precompiled binaries are available for Linux and Windows systems. Follow the instructions, by using the quick install for either Matlab or Python. In case these binaries do not work, or you want to compile the code yourself, then use the instructions for compiling yourself.

OptGpSampler requires a linear programming solver to find the initial point in the solution space. At the moment, you can use either:

  • IBM Ilog Cplex (version 12.6)
  • Gurobi (version 5.6)
  • GLPK (version 4.53)
Please make sure that at least one of these is installed. Other versions need a slight workaround.

Requirements

OptGpSampler has been tested on Windows 8 and Ubuntu 12.10, using Python 2.7 or Matlab R2012b.

Reference

For more information or citation, please see the article about optGpSampler in PLOS One