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Friday, June 23 • 1:42pm - 2:18pm
COBRA.jl: Accelerating Systems Biology

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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:

  • [1] Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B., “Julia: A Fresh Approach to Numerical Computing”, arXiv:1411.1607 [cs] (2014). arXiv: 1411.1607
  • [2] Ebrahim, Ali and Lerman, Joshua A. and Palsson, Bernhard O. and Hyduke, Daniel R., “COBRApy: COnstraints-Based Reconstruction and Analysis for Python”, BMC Systems Biology 7 (2013), pp. 74.
  • [3] Gudmundsson, Steinn and Thiele, Ines, “Computationally efficient flux variability analysis”, BMC Bioinformatics 11, 1 (2010), pp. 489.
  • [4] Heirendt, Laurent and Thiele, Ines and Fleming, Ronan M. T., “DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia”, Bioinformatics btw838 (2017).
  • [5] Lubin, Miles and Dunning, Iain, “Computing in Operations Research using Julia”, INFORMS Journal on Computing 27, 2 (2015), pp. 238–248. arXiv: 1312.1431
  • [6] Magnúsdóttir, Stefanía and Heinken, Almut and Kutt, Laura and Ravcheev, Dmitry A. and Bauer, Eugen and Noronha, Alb…, “Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota”, Nat Biotech 35, 1 (2017), pp. 81–89.
  • [7] Palsson, Bernhard Ø, Systems Biology: Constraint-based Reconstruction and Analysis (Cambridge, England: Cambridge University Press, 2015).
  • [8] Schellenberger, Jan and Que, Richard and Fleming, Ronan M. T. and Thiele, Ines and Orth, Jeffrey D. and Feist, Adam M. and Ziel…, “Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0”, Nat. Protocols 6, 9 (2011), pp. 1290–1307. 00182

avatar for Laurent Heirendt

Laurent Heirendt

Research Associate, University of Luxembourg / LCSB
Laurent Heirendt was born in 1987 in Luxembourg City, Luxembourg (Europe). He received his BSc in Mechanical Engineering from the Ecole Polytechnique Fédérale de Lausanne, Switzerland in 2009. A year later, he received his MSc in Advanced Mechanical Engineering from Imperial College... Read More →

Friday June 23, 2017 1:42pm - 2:18pm PDT
East Pauley Pauley Ballroom, Berkeley, CA