Background Metabolic engineering aims to design microorganisms that will generate a product of interest at high yield. Thus, a variety of in silico modeling strategies has been applied successfully, including the concepts of elementary flux modes (EFMs) and constrained minimal cut sets (cMCSs). The EFMs (minimal, steady state pathways through the system) can be calculated given a metabolic model. cMCSs are sets of reaction deletions in such a network that will allow desired pathways to survive and disable undesired ones (e.g., those with low product secretion or low growth rates). Grouping the modes into desired and undesired categories had to be done manually until now. Results Although the optimal solution for a given set of pathways will always be found with the currently available tools, manual selection may lead to a sub-optimal solution with respect to a metabolic engineering target. A small change in the selection of modes can reduce the number of necessary deletions while only slightly reducing production. Based on our recently introduced formulation of cut set calculations using binary linear programming, we suggest an algorithm that does not require manual selection of the desired pathways. Conclusions We demonstrated the principle of our algorithm with the help of a small toy network and applied it to a model of E. coli using different design objectives. Furthermore we validated our method by reproducing previously obtained results without requiring manual grouping of modes.
References
[1]
Nielsen J, Larsson C, van Maris A, Pronk J (2013) Metabolic engineering of yeast for production of fuels and chemicals. Current Opinion in Biotechnology 24: 398–404. doi: 10.1016/j.copbio.2013.03.023
[2]
McCloskey D, Palsson BO, Feist AM (2013) Basic and applied uses of genome-scale metabolic network reconstructions of escherichia coli. Molecular systems biology 9: 661. doi: 10.1038/msb.2013.18
[3]
Gasser B, Prielhofer R, Marx H, Maurer M, Nocon J, et al. (2013) Pichia pastoris: protein production host and model organism for biomedical research. Future Microbiology 8: 191–208. doi: 10.2217/fmb.12.133
[4]
Otero JM, Cimini D, Patil KR, Poulsen SG, Olsson L, et al. (2013) Industrial systems biology of saccharomyces cerevisiae enables novel succinic acid cell factory. PLoS ONE 8: e54144. doi: 10.1371/journal.pone.0054144
[5]
Lee JW, Na D, Park JM, Lee J, Choi S, et al. (2012) Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nature Chemical Biology 8: 536–546. doi: 10.1038/nchembio.970
[6]
Gidijala L, Kiel JAKW, Douma RD, Seifar RM, van Gulik WM, et al. (2009) An engineered yeast efficiently secreting penicillin. PLoS ONE 4: e8317. doi: 10.1371/journal.pone.0008317
[7]
Fan Z, Wu W, Hildebrand A, Kasuga T, Zhang R, et al. (2012) A novel biochemical route for fuels and chemicals production from cellulosic biomass. PLoS ONE 7: e31693. doi: 10.1371/journal.pone.0031693
[8]
Burgard AP, Pharkya P, Maranas CD (2003) Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering 84: 647–657. doi: 10.1002/bit.10803
[9]
Tepper N, Shlomi T (2010) Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 26: 536–543. doi: 10.1093/bioinformatics/btp704
[10]
Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotypephenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology.
[11]
Schuster S, Fell DA, Dandekar T (2000) A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotech 18: 326–332.
[12]
Trinh C, Wlaschin A, Srienc F (2009) Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Applied Microbiology and Biotechnology 81: 813–826. doi: 10.1007/s00253-008-1770-1
[13]
Soons ZITA, Ferreira EC, Patil KR, Rocha I (2013) Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes. PLoS ONE 8: e61648. doi: 10.1371/journal.pone.0061648
[14]
Schuster S, Hilgetag C (1994) On elementary ux modes in biochemical reaction systems at steady state. Journal of Biological Systems 2: 165–182. doi: 10.1142/s0218339094000131
[15]
Schuster S, Dandekar T, Fell DA (1999) Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends in Biotechnology 17: 53–60. doi: 10.1016/s0167-7799(98)01290-6
[16]
Klamt S, Stelling J (2002) Combinatorial complexity of pathway analysis in metabolic networks. Molecular Biology Reports 29: 233–236. doi: 10.1023/a:1020390132244
[17]
Zanghellini J, Ruckerbauer DE, Hanscho M, Jungreuthmayer C (2013) Elementary flux modes in a nutshell: Properties, calculation and applications. Biotechnology Journal 8: 1009–1016. doi: 10.1002/biot.201200269
[18]
H？dicke O, Klamt S (2011) Computing complex metabolic intervention strategies using constrained minimal cut sets. Metabolic Engineering 13: 204–213. doi: 10.1016/j.ymben.2010.12.004
[19]
Bordel S, Nielsen J (2010) Identification of flux control in metabolic networks using non-equilibrium thermodynamics. Metabolic Engineering 12: 369–377. doi: 10.1016/j.ymben.2010.03.001
[20]
Trinh CT, Unrean P, Srienc F (2008) Minimal escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses. Applied and Environmental Microbiology 74: 3634–3643. doi: 10.1128/aem.02708-07
[21]
Unrean P, Trinh CT, Srienc F (2010) Rational design and construction of an efficient e. coli for production of diapolycopendioic acid. Metabolic Engineering 12: 112–122. doi: 10.1016/j.ymben.2009.11.002
[22]
H？dicke O, Klamt S (2010) CASOP: a computational approach for strain optimization aiming at high productivity. Journal of Biotechnology 147: 88–101. doi: 10.1016/j.jbiotec.2010.03.006
[23]
Feist AM, Zielinski DC, Orth JD, Schellenberger J, Herrgard MJ, et al. (2010) Model-driven evaluation of the production potential for growth-coupled products of escherichia coli. Metabolic Engineering 12: 173–186. doi: 10.1016/j.ymben.2009.10.003
Jungreuthmayer C, Nair G, Klamt S, Zanghellini J (2013) Comparison and improvement of algorithms for computing minimal cut sets. BMC Bioinformatics 14: 318. doi: 10.1186/1471-2105-14-318
[26]
Danna E, Fenelon M, Gu Z, Wunderling R (2007) Generating multiple solutions for mixed integer programming problems. In: Fischetti M, Williamson DP, editors, Integer Programming and Combinatorial Optimization, Berlin, Heidelberg: Springer Berlin Heidelberg, volume 4513: 280–294. doi: 10.1007/978-3-540-72792-7_22
