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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
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 Co... Read More →

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

Attendees (15)