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COBREXA is a toolkit for working with large constraint-based metabolic models, and running very large numbers of analysis tasks on these models in parallel. Its main purpose is to make the methods of Constraint-based Reconstruction and Analysis (COBRA) scale to problem sizes that require the use of huge computer clusters and HPC environments, which allows them to be realistically applied to pre-exascale-sized models.
In this package, you will find the usual COBRA-like functions that interface to underlying linear programming solvers. We use
JuMP.jl as the unified interface for many solvers; you can plug in whichever compatible solver you want, including the popular
Development history of COBREXA.jl.
A dedicated quick start guide is available for quickly trying out the analysis with COBREXA.jl.
Detailed example listing is available here. All examples are also avaialble as JuPyteR notebooks.
- Loading models
- Converting, modifying and saving models
- Exploring model contents
- Generic accessors
- Basic usage of
- Model construction and modification
- Flux balance analysis (FBA)
- Extending FBA with modifications
- Flux variability analysis (FVA)
- Gene knockouts
- Restricting and disabling individual reactions
- Parsimonious flux balance analysis (pFBA)
- Loopless FBA
- FBA with crowding
- Growth media analysis
- Maximum-minimum driving force analysis
- Minimization of metabolic adjustment (MOMA)
- Hit and run sampling
- Production envelopes
Detailed concept guide listing is available here.
- Screening many model variants
- Writing custom optimizer modifications
- Working with custom models
- Extending the models
Detailed table of contents of the API documentation is available here.
If you wish to contribute code, patches or improvements to
COBREXA.jl, please follow the contribution guidelines and hints.
COBREXA.jl is developed at the Luxembourg Centre for Systems Biomedicine of the University of Luxembourg (uni.lu/lcsb), cooperating with the Institute for Quantitative and Theoretical Biology at the Heinrich Heine University in Düsseldorf (qtb.hhu.de).
The development was supported by European Union's Horizon 2020 Programme under PerMedCoE project (permedcoe.eu) agreement no. 951773.