Kulkarni, Vishwesh V.

Raman, Karthik

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Efficient Computation of Synthetic LethalsVishwesh Kulkarni? Karthik Raman†School of Engineering, University of Warwick Department of Biosciences, IIT MadrasEmail: V.Kulkarni@warwick.ac.uk Email: kraman@iitm.ac.inObjectives1. Perform synthetic reaction and gene lethality analysis for genome-scalemetabolic networks of E. coli, S. enterica, and Mycobacterium tuberculosis.2. Characterize synthetic lethal gene deletions for these networks.3. Improve on theFlux Balance Analysis (FBA) predictions using quadraticprogramming. Characterize the improvement via public domain data on thetranscription factor (TF) knock-out experiments.IntroductionI Synthetic lethality arises when a combination of deficiencies in theexpression of two or more genes leads to cell death, whereas a deficiency inonly one of these genes does not. The deficiencies can arise throughmutations, epigenetic alterations or inhibitors of one of the genes.I The phenotype resulting from a gene/reaction deletion is termed a lethalphenotype, if the maximum growth-rate predicted by FBA is less than acut-off, typically 1% of the maximum in silico wild-type growth rate.I FBA [1] solves a Linear Programming (LP) problem in which, typically, fluxthrough a set of biomass reactions is to be maximized subject to theconstraints obtained from the stoichiometry of the metabolic network.Materials1. SBML models of the organisms: E.coli: iAF1260 [2]; S. entericaTyphimurium: LT2 STM v1.0 [3]; M. tuberculosis: iNJ661 [4].2. MATLAB (R2015b) interfaced with COBRA Toolbox v2.X.3. A desktop: 2.4GHz Intel Xeon E5645 processor with 16 GB DDR3 RAMrunning Windows 8.1 using the IBM CPLEX v12.5.1 solver.MethodsFigure 1: We enrich the constraint-based reconstruction & analysis (COBRA) phylogeny [5].Mathematical SectionI Solve the problem of minimizing the `1 norm of the fluxes subject to theFBA constraints such that the biomass produced is the same as the wildtype. Let Jnz denote the set of reactions that have non-zero flux.Figure 2: Details of our computation of synthetic lethals.Results: Synthetic Lethal Discovery and Speed-UpFigure 3: Our algorithms significantly speed up the synthetic lethal identification process.Results: Quadratic Programming for Growth PredictionsFigure 4: Our quadratic programming solution outperforms other algorithms.Conclusion and Future DirectionsI We have extended the scope of FBA for a more comprehensive and moreefficient determination of synthetic lethals.I Our quadratic programming algorithms predict growth better than otheralgorithms but require more data.I We aim to conduct more TF-knock out experiments and develop techniquesto include miRNA datasets.References[1] J. Orth et al.Nature Biotechnology 28(3):245-248, 2010. [2] A. Feist et al.Mol. Syst. Biol., 3:121, 2007. [3] I. Thiele et al. J. Bacteriol., 187: 58185830, 2005. [4]N. Jamshidi and B. Palsson. BMC Syst. Biol., 1:26, 2007. [5] N. Lewis et al.Nature Reviews Microbiology 10, 291-305, 2012. [6] S. Chandrasekaran and N. Price.PNAS 107(41):17845-50, 2012.AcknowledgmentsI VK acknowledges funding from Warwick Institute of Synthetic Biology(WISB), BBSRC, and EPSRC. KR acknowledges funding from IIT Madrasand the grant BT/PR4949/BRB/10/1048/2012 from Govt. of India.Contact InformationI Web: http://www2.warwick.ac.uk/fac/sci/eng/staff/vk/I Email: V.Kulkarni@warwick.ac.ukI Phone: +44 (024) 765 24806I We are looking to work with additional datasets. Do contact usif you have omics data to share!Warwick Institute of Synthetic Biology (WISB) and School of Engineering, University of Warwick. V.Kulkarni@warwick.ac.uk

Fast identification of synthetic lethals using quadratic programming

http://wrap.warwick.ac.uk/84221/1/uksynbio2016.pdf

Fast identification of synthetic lethals using quadratic programming

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