COBRA.jl - COnstraint-Based Reconstruction and Analysis
|Documentation||Coverage||Continuous integration - ARTENOLIS|
Installation of COBRA.jl
If you are new to Julia, you may find the Beginner's Guide interesting. A working installation of Julia is required.
At the Julia prompt, add the
COBRA.jl module by running:
julia> using COBRA
COBRA.jl has been tested on
Julia v0.5+ on Ubuntu Linux 14.04+, MacOS 10.7+, and Windows 7 (64-bit). All solvers supported by
MathProgBase.jl are supported by
COBRA.jl, but must be installed separately. A COBRA model saved as a
.mat file can be read in using
MAT.jl are automatically installed during the installation of the
Tutorial, documentation and FAQ
COBRA.jl package is fully documented here. You may also display the documentation in the Julia REPL:
julia> ? distributedFBA
Testing and benchmarking
You can test the package using:
Shall no solvers be detected on your system, error messages may be thrown when testing the
julia> using Requests julia> include("$(Pkg.dir("COBRA"))/test/getTestModel.jl") julia> getTestModel()
How to cite
The corresponding paper can be downloaded from here. You may cite
distributedFBA.jl as follows:
Laurent Heirendt, Ines Thiele, Ronan M. T. Fleming; DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics 2017 btw838. doi: 10.1093/bioinformatics/btw838
- At present, a COBRA model in
.matformat is loaded using the
MAT.jlpackage. A valid MATLAB license is not required.
- The inner layer parallelization is currently done within the solver. No log files of each spawned thread are generated.
- The current benchmarks have been performed using default optimization and compilation options of Julia and a set of solver parameters. The performance may be further improved by tuning these parameters.
- B. O. Palsson., Systems Biology: Constraint-based Reconstruction and Analysis. Cambridge University Press, NY, 2015.
- Heirendt, L. and Arreckx, S. et al., Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0 (submitted), 2017, arXiv:1710.04038.
- Steinn, G. et al., Computationally efficient flux variability analysis. BMC Bioinformatics, 11(1):1–3, 2010.
- Orth, J. et al., Reconstruction and use of microbial metabolic networks: the core escherichia coli metabolic model as an educational guide. EcoSal Plus, 2010.