Abstract
Flux variability analysis enables comprehensive exploration of alternate optimal routes in a metabolic network. This method is especially useful with models such as bna572 for the developing oilseed rape embryo which is highly compartmentalized. Here, we describe a protocol for carrying out flux variability analysis on reactions and network projections of bna572 using well-established software (CellNetAnalyzer and COBRA) for constraint-based analysis of stoichiometric network reconstructions.
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References
Palsson B (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, Cambridge
Ogata H, Goto S, Sato K et al (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27(1):29–34
Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol 132(2):453–460
Wang X, Wang H, Wang J et al (2011) The genome of the mesopolyploid crop species Brassica rapa. Nat Genet 43(10):1035–1039
The Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408(6814):796–815
Troncoso-Ponce MA, Kilaru A, Cao X et al (2011) Comparative deep transcriptional profiling of four developing oilseeds. Plant J 68(6):1014–1027
Williams TC, Poolman MG, Howden AJ et al (2010) A genome-scale metabolic model accurately predicts fluxes in central carbon metabolism under stress conditions. Plant Physiol 154(1):311–323
Poolman MG, Miguet L, Sweetlove LJ, Fell DA (2009) A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol 151(3):1570–1581
Pilalis E, Chatziioannou A, Thomasset B, Kolisis F (2011) An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism. Biotechnol Bioeng 108(7):1673–1682
Hay J, Schwender J (2011) Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos. Plant J 67(3):526–541
Hay J, Schwender J (2011) Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to 13C metabolic flux analysis. Plant J 67(3):513–525
Mintz-Oron S, Meir S, Malitsky S et al (2012) Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc Natl Acad Sci U S A 109(1):339–344
de Oliveira Dal’Molin CG, Quek LE, Palfreyman RW et al (2010) AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol 152(2):579–589
Schwender J (2008) Metabolic flux analysis as a tool in metabolic engineering of plants. Curr Opin Biotechnol 19(2):131–137
Saha R, Suthers PF, Maranas CD (2011) Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism. PLoS One 6(7):e21784
Grafahrend-Belau E, Schreiber F, Koschützki D, Junker BH (2009) Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism. Plant Physiol 149(1):585–598
Schwender J, Hay JO (2012) Predictive modeling of biomass component tradeoffs in Brassica napus developing oilseeds based on in silico manipulation of storage metabolism. Plant Physiol 160(3):1218–1236
Klamt S, Stelling J (2002) Combinatorial complexity of pathway analysis in metabolic networks. Mol Biol Rep 29(1–2):233–236
Boyle NR, Shastri AA, Morgan JA (2009) Network stoichiometry. In: Schwender J (ed) Plant metabolic networks. Springer, New York, pp 211–243
Sweetlove LJ, Ratcliffe RG (2011) Flux-balance modeling of plant metabolism. Front Plant Sci 2:38
Edwards JS, Ibarra RU, Palsson BO (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19(2):125–130
Edwards JS, Palsson BO (2000) Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1:1
Edwards JS, Palsson BO (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci U S A 97(10):5528–5533
Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5(4):264–276
Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:119
Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with Cell NetAnalyzer. BMC Syst Biol 1:2
Schellenberger J, Que R, Fleming RM et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6(9):1290–1307
Becker SA, Feist AM, Mo ML et al (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2(3):727–738
Burgard AP, Pharkya P, Maranas CD (2003) OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647–657
Sègre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99(23):15112–15117
Baud S, Dubreucq B, Miquel M et al (2008) Storage reserve accumulation in Arabidopsis: metabolic and developmental control of seed filling. Arabidopsis Book 6:e0113
http://www.gnu.org/software/glpk/ GLPK (GNU Linear Programming Kit)
Schwender J (2011) Experimental flux measurements on a network scale. Front Plant Sci 2:63
Price ND, Thiele I, Palsson BO (2006) Candidate states of Helicobacter pylori’s genome-scale metabolic network upon application of “loop law” thermodynamic constraints. Biophys J 90(11):3919–3928
Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531
Acknowledgments
We would like to thank Griffin St. Clair and Dr. Zhijie Sun for helpful discussions. We gratefully acknowledge funding from the US Department of Energy (Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences, Field Work Proposal BO-133).
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Hay, J.O., Schwender, J. (2014). Flux Variability Analysis: Application to Developing Oilseed Rape Embryos Using Toolboxes for Constraint-Based Modeling. In: Dieuaide-Noubhani, M., Alonso, A. (eds) Plant Metabolic Flux Analysis. Methods in Molecular Biology, vol 1090. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-688-7_18
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DOI: https://doi.org/10.1007/978-1-62703-688-7_18
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