sybil – Efficient constraint-based modelling in R

BMC Systems Biology

Martin J Lercher¹  Claus Jonathan Fritzemeier¹  Abdelmoneim Amer Desouki¹  Gabriel Gelius-Dietrich¹ 

[1] Institute for Computer Science, Heinrich-Heine-University, Universitätsstr 1, 40225 Düsseldorf, Germany

关键词: GNU R;    ROOM;    MOMA;    FBA;    Flux-balance analysis;    Constraint-based modelling;   

Others  :  1141814
DOI  :  10.1186/1752-0509-7-125

 received in 2013-04-19, accepted in 2013-11-01,  发布年份 2013

【 摘 要 】

Background

Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users.

Results

Here, we present sybil, an open source software library for constraint-based analyses in R; R is a free, platform-independent environment for statistical computing and graphics that is widely used in bioinformatics. Among other functions, sybil currently provides efficient methods for flux-balance analysis (FBA), MOMA, and ROOM that are about ten times faster than previous implementations when calculating the effect of whole-genome single gene deletions in silico on a complete E. coli metabolic model.

Conclusions

Due to the object-oriented architecture of sybil, users can easily build analysis pipelines in R or even implement their own constraint-based algorithms. Based on its highly efficient communication with different mathematical optimisation programs, sybil facilitates the exploration of high-dimensional optimisation problems on small time scales. Sybil and all its dependencies are open source. Sybil and its documentation are available for download from the comprehensive R archive network (CRAN).

【 授权许可】

   
2013 Gelius-Dietrich et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Kauffman KJ, Prakash P, Edwards JS: Advances in flux balance analysis. Curr Opin Biotechnol 2003, 14(5):491-496.
• [2]Edwards JS, Covert M, Palsson BØ: Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 2002, 4(3):133-140.
• [3]Orth JD, Thiele I, Palsson BØ: What is flux balance analysis? Nat Biotechnol 2010, 28(3):245-248.
• [4]Schuster S, Pfeiffer T, Fell DA: Is maximization of molar yield in metabolic networks favoured by evolution? J Theor Biol 2008, 252(3):497-504.
• [5]Terzer M, Stelling J: Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics 2008, 24(19):2229-2235.
• [6]Girbig D, Selbig J, Grimbs S: A MATLAB toolbox for structural kinetic modeling. Bioinformatics 2012, 28(19):2546-2547.
• [7]Raman K, Chandra N: Flux balance analysis of biological systems: applications and challenges. Brief Bioinform 2009, 10(4):435-449.
• [8]Dandekar T, Fieselmann A, Majeed S, Ahmed Z: Software applications toward quantitative metabolic flux analysis and modeling. Brief Bioinform 2012. doi:10.1093/bib/bbs065
• [9]Lakshmanan M, Koh G, Chung BKS, Lee DY: Software applications for flux balance analysis. Brief Bioinform 2012. doi:10.1093/bib/bbs069
• [10]Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 2011, 6(9):1290-1307.
• [11]Schuster S, Fell DA, Dandekar T: A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol 2000, 18(3):326-332.
• [12]Papin JA, Price ND, Palsson BØ: Extreme pathway lengths and reaction participation in genome-scale metabolic networks. Genome Res 2002, 12(12):1889-1900.
• [13]Wiback SJ, Palsson BØ: Extreme pathway analysis of human red blood cell metabolism. Biophys J 2002, 83(2):808-818.
• [14]Gavai AK: BiGGR. [ http://CRAN.R-project.org/package=BiGGR webcite]
• [15]Schellenberger J, Park JO, Conrad TM, Palsson BØ: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 2010, 11:213. BioMed Central Full Text
• [16]Gangadharan A, Rohatgi N: abcdeFBA. [ http://CRAN.R-project.org/package=abcdeFBA webcite]
• [17]R Development Core Team: R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2012. [ http://www.R-project.org webcite]. [ISBN 3-900051-07-0]
• [18]Mahadevan R, Schilling CH: The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 2003, 5(4):264-276.
• [19]Reed JL, Palsson BØ: Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res 2004, 14(9):1797-1805.
• [20]Segrè D, Vitkup D, Church GM: Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 2002, 99(23):15112-15117.
• [21]Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, Herrgård MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007, 2(3):727-738.
• [22]Shlomi T, Berkman O, Ruppin E: Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci U S A 2005, 102(21):7695-7700.
• [23]Palsson BØ: Systems Biology: Properties of Recontructed Networks. Cambridge: Cambridge University Press; 2006.
• [24]Edwards JS, Ramakrishna R, Palsson BØ: Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol Bioeng 2002, 77:27-36.
• [25]Price ND, Reed JL, Palsson BØ: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004, 2(11):886-897.
• [26]Akesson M, Förster J, Nielsen J: Integration of gene expression data into genome-scale metabolic models. Metab Eng 2004, 6(4):285-293.
• [27]Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE: Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009, 5(8):e1000489.
• [28]van Berlo RJP, de Ridder D, Daran JM, Daran-Lapujade PAS, Teusink B, Reinders MJT: Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM Trans Comput Biol Bioinform 2011, 8:206-216.
• [29]Covert MW, Schilling CH, Palsson BØ: Regulation of gene expression in flux balance models of metabolism. J Theor Biol 2001, 213:73-88.
• [30]Amer Desouki A: sybilDynFBA. [ http://CRAN.R-project.org/package=sybilDynFBA webcite]
• [31]Varma A, Palsson BØ: Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 1994, 60(10):3724-3731.
• [32]Amer Desouki A: sybilEFBA. [ http://CRAN.R-project.org/package=sybilEFBA webcite]
• [33]Fritzemeier CJ: RSeed. [ http://CRAN.R-project.org/package=RSeed webcite]
• [34]Borenstein E, Kupiec M, Feldman MW, Ruppin E: Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc Natl Acad Sci U S A 2008, 105(38):14482-14487.
• [35]Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 2007, 3:121.
• [36]Gudmundsson S, Thiele I: Computationally efficient flux variability analysis. BMC Bioinformatics 2010, 11:489. BioMed Central Full Text
• [37]Klamt S, Saez-Rodriguez J, Gilles ED: Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol 2007, 1:2. BioMed Central Full Text
• [38]Wright J, Wagner A: The systems biology research tool: evolvable open-source software. BMC Syst Biol 2008, 2:55. BioMed Central Full Text
• [39]Rocha I, Maia P, Evangelista P, Vilaça P, Soares S, Pinto JP, Nielsen J, Patil KR, Ferreira EC, Rocha M: OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst Biol 2010, 4:45. BioMed Central Full Text
• [40]Hoppe A, Hoffmann S, Gerasch A, Gille C, Holzhütter HG: FASIMU: flexible software for flux-balance computation series in large metabolic networks. BMC Bioinformatics 2011, 12:28. BioMed Central Full Text
• [41]Ebrahim A, Lerman JA, Palsson BØ, Hyduke DR: COBRApy: constraints-based reconstruction and analysis for python. BMC Syst Biol 2013, 7:74. BioMed Central Full Text
• [42]Gelius-Dietrich G: glpkAPI. [ http://CRAN.R-project.org/package=glpkAPI webcite]
• [43]Giorgetti N: glpkmex. [ http://glpkmex.sourceforge.net webcite]
• [44]Hornik K, Theussl S: Rglpk. [ http://CRAN.R-project.org/package=Rglpk webcite]
• [45]Szappanos B, Kovács K, Szamecz B, Honti F, Costanzo M, Baryshnikova A, Gelius-Dietrich G, Lercher MJ, Jelasity M, Myers CL, Andrews BJ, Boone C, Oliver SG, Pál C, Papp B: An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat Genet 2011, 43(7):656-662.
• [46]Mo ML, Palsson BØ, Herrgård MJ: Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 2009, 3:37. BioMed Central Full Text
• [47]Larhlimi A, David L, Selbig J, Bockmayr A: F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks. BMC Bioinformatics 2012, 13:57. BioMed Central Full Text
• [48]Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novère N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003, 19(4):524-531.
• [49]In Silico Organisms Systems Biology Research Group [ http://gcrg.ucsd.edu/Downloads webcite]
• [50]Feist AM, Herrgård MJ, Thiele I, Reed JL, Palsson BØ: Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 2009, 7(2):129-43.
• [51]Oberhardt MA, Palsson BØ, Papin JA: Applications of genome-scale metabolic reconstructions. Mol Syst Biol 2009, 5:320.
• [52]Bornstein BJ, Keating SM, Jouraku A, Hucka M: LibSBML: an API library for SBML. Bioinformatics 2008, 24(6):880-881.
• [53]Makhorin A: GNU Linear Programming Kit (GLPK). [ http://www.gnu.org/software/glpk/ webcite]
• [54]IBM ILOG CPLEX [ https://www.ibm.com/developerworks/university/academicinitiative/ webcite]
• [55]Gelius-Dietrich G: cplexAPI. [ http://CRAN.R-project.org/package=cplexAPI webcite]
• [56]COIN OR Clp [ https://projects.coin-or.org/Clp webcite]
• [57]Gelius-Dietrich G: clpAPI. [ http://CRAN.R-project.org/package=clpAPI webcite]
• [58]lp_solve [ http://lpsolve.sourceforge.net/5.5/index.htm webcite]
• [59]Konis K: lpSolveAPI. [ http://CRAN.R-project.org/package=lpSolveAPI webcite]
• [60]Gurobi [ http://www.gurobi.com webcite]
• [61]Comprehensive R Archive Network (CRAN) [ http://cran.r-project.org webcite]