Sampling with poling-based flux balance analysis: Optimal versus sub-optimal flux space analysis of Actinobacillus succinogenes

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Binns, Michael ^a,c   de Atauri, Pedro ^b   Vlysidis, Anestis ^a   Cascante, Marta ^b   Theodoropoulos, Constantinos ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF BARCELONA   (Spain)

^c Hanyang University   (South Korea)

Author keywords

Flux balance analysis; Flux sampling; Optimisation

Indexed keywords

ACTINOBACILLUS SUCCINOGENES; EXPERIMENTAL ERRORS; FLUX BALANCE ANALYSIS; FLUX DISTRIBUTIONS; OBJECTIVE FUNCTIONS; OPTIMISATIONS; SIMPLE MODIFICATIONS; SUBOPTIMAL SOLUTION;

ACTINOBACILLUS SUCCINOGENES;

CARBON DIOXIDE; GLYCEROL; SUCCINIC ACID;

ACTINOBACILLUS; ALGORITHM; BIOLOGICAL MODEL; GENETICS; GROWTH, DEVELOPMENT AND AGING; METABOLISM;

ACTINOBACILLUS; ALGORITHMS; CARBON DIOXIDE; GLYCEROL; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; SUCCINIC ACID;

EID: 84928716941     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0476-5     Document Type: Article

References (44)

1. Thiele I, Palsson BO. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5:93-121.
2. Binns M, Theodoropoulos C. An integrated knowledge-based approach for modeling biochemical reaction networks. Comput Chem Eng. 2011;35:3025-43.
3. Mu F, Unkefer CJ, Unkefer PJ, Hlavacek WS. Prediction of metabolic reactions based on atomic and molecular properties of small molecule compounds. Bioinformatics. 2011;27:1537-45.
4. Hatzimanikatis V, Li C, Ionita JA, Henry CS, Jankowski MD, Broadbelt LJ. Exploring the diversity of complex metabolic networks. Bioinformatics. 2005;21:1603-9.
5. Schellenberger J, Palsson BO. Use of randomized sampling for analysis of metabolic networks. J Biol Chem. 2009;284:5457-61.
6. Mahadevan R, Schilling CH. The effects of alternate optimal solutions in constraint-based genole-scale metabolic models. Metab Eng. 2003;5:264-76.
7. Soh KC, Miskovic L, Hatzimanikatis V. From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks. FEMS Yeast Res. 2012;12:129-43.
8. Bonarius HPJ, Schmid G, Tramper J. Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends Biotechnol. 1997;15:308-14.
9. Gudmundsson S, Thiele I. Computationally efficient flux variability analysis. BMC Bioinformatics. 2010;11:489.
10. Schuster S, Dandekar T, Fell DA. Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 1999;17:53-60.
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;18:326-32.
12. Schilling CH, Letscher D, Palsson BO. Theory for the systematic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol. 2000;203:229-48.
13. Lee S, Phalakornkule C, Domach MM, Grossmann IE. Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput Chem Eng. 2000;24:711-6.
14. Phalakornkule C, Lee S, Zhu T, Koepsel R, Ataai MM, Grossmann IE, et al. A MILP-based flux alternative generation and NMR experimental design strategy for metabolic engineering. Metab Eng. 2001;3:124-37.
15. Kelk SM, Olivier BG, Stougie L, Bruggeman FJ. Optimal flux spaces of genome-scale stoichiometric models are determined by a few sub-networks. Sci Rep. 2012;2:580.
16. Smith RL. Efficient monte carlo procedures for generating points uniformly distributed over bounded regions. Oper Res. 1984;32:1296-308.
17. Kaufman DE, Smith RL. Direction choice for accelerated convergence in hit-and-run sampling. Oper Res. 1998;46:84-95.
18. Wiback SJ, Famili I, Greenberg HJ, Palsson BO. Monte carlo sampling can be used to determine the size and shape of the steady-state flux space. J Theor Biol. 2004;228:437-47.
19. Lovasz L. Hit-and-run mixes fast. Math Program. 1999;86:443-61.
20. Price ND, Schellenberger J, Palsson BO. Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J. 2004;87:2172-86.
21. D'Huys PJ, Lule I, Vercammen D, Anne J, Van Impe JF, Bernaerts K. Genome-scale metabolic analysis of streptomyces lividans growing on a complex medium. J Biotechnol. 2012;161:1-13.
22. Smellie A, Teig SL, Towbin P. Poling - promoting conformational variation. J Comput Chem. 1995;16:171-87.
23. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7:457-511.
24. VanderWerf MJ, Guettler MV, Jain MK, Zeikus JG. Environmental and physiological factors affecting the succinate production ratio during carbohydrate fermentation by actinobacillus sp. 130z. Arch Microbiol. 1997;167:332-42.
25. Lin SKC, Du CY, Koutinas A, Wang RH, Webb C. Substrate and product inhibition kinetics in succinic acid production by actinobacillus succinogenes. Biochem Eng J. 2008;41:128-35.
26. Liu YP, Zheng P, Sun ZH, Ni Y, Dong JJ, Wei P. Strategies of ph control and glucose-fed batch fermentation for production of succinic acid by actinobacillus succinogenes cgmcc1593. J Chem Technol Biotechnol. 2008;83:722-9.
27. McKinlay JB, Shachar-Hill Y, Zeikus JG, Vieille C. Determining actinobacillus succinogenes metabolic pathways and fluxes by nmr and gc-ms analyses of c-13-labeled metabolic product isotopomers. Metab Eng. 2007;9:177-92.
28. McKinlay JB, Vieille C. (13)C-metabolic flux analysis of actinobacillus succinogenes fermentative metabolism at different nahco(3) and h(2) concentrations. Metab Eng. 2008;10:55-68.
29. Corona-Gonzalez RI, Bories A, Gonzalez-Alvarez V, Pelayo-Ortiz C. Kinetic study of succinic acid production by actinobacillus succinogenes zt-130. Process Biochem. 2008;43:1047-53.
30. Vlysidis A, Binns M, Webb C, Theodoropoulos C. Glycerol utilisation for the production of chemicals: conversion to succinic acid, a combined experimental and computational study. Biochem Eng J. 2011;58-59:1-11.
31. McKinlay JB, Laivenieks M, Schindler BD, McKinlay AA, Siddaramappa S, Challacombe JF, et al. A genomic perspective on the potential of actinobacillus succinogenes for industrial succinate production. BMC Genomics. 2010;11:680.
32. Goto S, Okuno Y, Hattori M, Nishioka T, Kanehisa M. Ligand: database of chemical compounds and reactions in biological pathways. Nucleic Acids Res. 2002;30:402-4.
33. Jankowski MD, Henry CS, Broadbelt LJ, Hatzimanikatis V. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys J. 2008;95:1487-99.
34. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox. Nat Protoc. 2007;2:727-38.
35. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, et al. Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox v2.0. Nat Protoc. 2011;6:1290-307.
36. Gurobi Optimization, Inc.: Gurobi optimizer reference manual. 2014, http://www.gurobi.com
37. Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, et al. 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.
38. Floudas CA, Gounaris CE. A review of recent advances in global optimization. J Glob Optim. 2009;45:3-38.
39. Binns M, de Visser SP, Theodoropoulos C. Modeling flexible pharmacophores with distance geometry, scoring and bound-stretching. J Chem Inf Model. 2012;52:577-88.
40. Wang L, Birol I, Hatzimanikatis V. Metabolic control analysis under uncertainty: framework development and case studies. Biophys J. 2004;87:3750-63.
41. Wang L, Hatzimanikatis V. Metabolic engineering under uncertainty. I: framework development. Metab Eng. 2006;8:133-41.
42. Wang L, Hatzimanikatis V. Metabolic engineering under uncertainty- II. Analysis of yeast metabolism. Metab Eng. 2006;8:142-59.
43. Binns M, Vlysidis A, Webb C, Theodoropoulos C, de Atauri P, Cascante M. Glycerol conversion to succinic acid using actinobacillus succinogenes: a metabolic network-based analysis. Comput Aided Chem Eng. 2011;29:1421-5.
44. Angeles-Martinez L, Binns M, Theodoropoulos C, de Atuari P, Cascante M. Thermodynamically constrained flux and control analysis of Escherichia coli. Comput Aided Chem Eng. 2012;30:1377-81.