Biologists in the COnstraint-Based Reconstruction and Analysis (COBRA) [7] community are gearing up to develop computational models of large and huge-scale biochemical networks with more than one million biochemical reactions. The growing model size puts a strain on efficient simulation and network exploration times to the point that accelerating existing COBRA methods became a priority. Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks has long been hampered by performance limitations of current implementations in Matlab/C (The COBRA Toolbox [8] and fastFVA [3]) or Python (cobrapy [2]). Julia [1] is the language that fills the gap between complexity, performance, and development time. DistributedFBA.jl [4], part of the novel COBRA.jl package, is a high-level, high-performance, open-source Julia implementation of flux balance analysis, which is a linear optimization problem. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes using optimization solver interfaces implemented in MathProgBase.jl [5]. Julia’s parallelization capabilities led to a speedup in latency that follows Amdahl’s law. For the first time, a flux variability analysis (two flux balance analyses on each biochemical reaction) on a model with more than 200k biochemical reactions [6] has been performed. With Julia and COBRA.jl, the reconstruction and analysis capabilities of large and huge-scale models in the COBRA community are lifted to another level. Code and benchmark data are freely available on github.com/opencobra/COBRA.jl References:
