ToMI-FBA: A genome-scale metabolic flux based algorithm to select optimum hosts and media formulations for expressing pathways of interest

Hadi Nazem-Bokaee; Ryan S. Senger; Hadi Nazem-Bokaee; Ryan S. Senger

doi:10.3934/bioeng.2015.4.335

AIMS Bioengineering

2015, Volume 2, Issue 4: 335-374. doi: 10.3934/bioeng.2015.4.335

Previous Article Next Article

Research article Special Issues

ToMI-FBA: A genome-scale metabolic flux based algorithm to select optimum hosts and media formulations for expressing pathways of interest

Hadi Nazem-Bokaee ,
Ryan S. Senger ^,

Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA

Received: 23 June 2015 Accepted: 21 September 2015 Published: 29 September 2015

The Total Membrane Influx constrained Flux Balance Analysis (ToMI-FBA) algorithm was developed in this research as a new tool to help researchers decide which microbial host and medium formulation are optimal for expressing a new metabolic pathway. ToMI-FBA relies on genome-scale metabolic flux modeling and a novel in silico cell membrane influx constraint that specifies the flux of atoms (not molecules) into the cell through all possible membrane transporters. The ToMI constraint is constructed through the addition of an extra row and column to the stoichiometric matrix of a genome-scale metabolic flux model. In this research, the mathematical formulation of the ToMI constraint is given along with four case studies that demonstrate its usefulness. In Case Study 1, ToMI-FBA returned an optimal culture medium formulation for the production of isobutanol from Bacillus subtilis. Significant levels of L-valine were recommended to optimize production, and this result has been observed experimentally. In Case Study 2, it is demonstrated how the carbon to nitrogen uptake ratio can be specified as an additional ToMI-FBA constraint. This was investigated for maximizing medium chain length polyhydroxyalkanoates (mcl-PHA) production from Pseudomonas putida KT2440. In Case Study 3, ToMI-FBA revealed a strategy of adding cellobiose as a means to increase ethanol selectivity during the stationary growth phase of Clostridium acetobutylicum ATCC 824. This strategy was also validated experimentally. Finally, in Case Study 4, B. subtilis was identified as a superior host to Escherichia coli, Saccharomyces cerevisiae, and Synechocystis PCC6803 for the production of artemisinate.
- genome-scale model,
- microbial cell factory,
- flux balance analysis,
- de novo metabolic pathway
Citation: Hadi Nazem-Bokaee, Ryan S. Senger. ToMI-FBA: A genome-scale metabolic flux based algorithm to select optimum hosts and media formulations for expressing pathways of interest[J]. AIMS Bioengineering, 2015, 2(4): 335-374. doi: 10.3934/bioeng.2015.4.335

Related Papers:

Abstract

The Total Membrane Influx constrained Flux Balance Analysis (ToMI-FBA) algorithm was developed in this research as a new tool to help researchers decide which microbial host and medium formulation are optimal for expressing a new metabolic pathway. ToMI-FBA relies on genome-scale metabolic flux modeling and a novel in silico cell membrane influx constraint that specifies the flux of atoms (not molecules) into the cell through all possible membrane transporters. The ToMI constraint is constructed through the addition of an extra row and column to the stoichiometric matrix of a genome-scale metabolic flux model. In this research, the mathematical formulation of the ToMI constraint is given along with four case studies that demonstrate its usefulness. In Case Study 1, ToMI-FBA returned an optimal culture medium formulation for the production of isobutanol from Bacillus subtilis. Significant levels of L-valine were recommended to optimize production, and this result has been observed experimentally. In Case Study 2, it is demonstrated how the carbon to nitrogen uptake ratio can be specified as an additional ToMI-FBA constraint. This was investigated for maximizing medium chain length polyhydroxyalkanoates (mcl-PHA) production from Pseudomonas putida KT2440. In Case Study 3, ToMI-FBA revealed a strategy of adding cellobiose as a means to increase ethanol selectivity during the stationary growth phase of Clostridium acetobutylicum ATCC 824. This strategy was also validated experimentally. Finally, in Case Study 4, B. subtilis was identified as a superior host to Escherichia coli, Saccharomyces cerevisiae, and Synechocystis PCC6803 for the production of artemisinate.

References

[1]	Hanai T, Atsumi S, Liao JC (2007) Engineered synthetic pathway for isopropanol production in Escherichia coli. Appl Environ Microbiol 73: 7814-7818.
[2]	Atsumi S, Cann AF, Connor MR, et al. (2008) Metabolic engineering of Escherichia coli for 1-butanol production. Metabolic Eng 10: 305-311. doi: 10.1016/j.ymben.2007.08.003
[3]	Connor MR, Liao JC (2008) Engineering of an Escherichia coli strain for the production of 3-methyl-1-butanol. Appl Environ Microbiol 74: 5769-5775. doi: 10.1128/AEM.00468-08
[4]	McKenna R, Nielsen DR (2011) Styrene biosynthesis from glucose by engineered E. coli. Metabolic Eng 13: 544-554. doi: 10.1016/j.ymben.2011.06.005
[5]	McKenna R, Pugh S, Thompson B, et al. (2013) Microbial production of the aromatic building-blocks (S)-styrene oxide and (R)-1,2-phenylethanediol from renewable resources. Biotechnol J 8: 1465-1475.
[6]	Moon TS, Dueber JE, Shiue E, et al. (2010) Use of modular, synthetic scaffolds for improved production of glucaric acid in engineered E. coli. Metabolic Eng 12: 298-305. doi: 10.1016/j.ymben.2010.01.003
[7]	Tseng HC, Martin CH, Nielsen DR, et al. (2009) Metabolic engineering of Escherichia coli for enhanced production of (R)- and (S)-3-hydroxybutyrate. Appl Environ Microbiol 75: 3137-3145. doi: 10.1128/AEM.02667-08
[8]	Ajikumar PK, Xiao WH, Tyo KE, et al. (2010) Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science 330: 70-74. doi: 10.1126/science.1191652
[9]	Engels B, Dahm P, Jennewein S (2008) Metabolic engineering of taxadiene biosynthesis in yeast as a first step towards Taxol (Paclitaxel) production. Metabolic Eng 10: 201-206. doi: 10.1016/j.ymben.2008.03.001
[10]	Scaife MA, Burja AM, Wright PC (2009) Characterization of cyanobacterial beta-carotene ketolase and hydroxylase genes in Escherichia coli, and their application for astaxanthin biosynthesis. Biotechnol Bioeng 103: 944-955. doi: 10.1002/bit.22330
[11]	Lemuth K, Steuer K, Albermann C (2011) Engineering of a plasmid-free Escherichia coli strain for improved in vivo biosynthesis of astaxanthin. Microb Cell Fact10: 29.
[12]	Tsuruta H, Paddon CJ, Eng D, et al. (2009) High-level production of amorpha-4,11-diene, a precursor of the antimalarial agent artemisinin, in Escherichia coli. PloS One 4: e4489. doi: 10.1371/journal.pone.0004489
[13]	Becker J, Wittmann C (2012) Bio-based production of chemicals, materials and fuels -Corynebacterium glutamicum as versatile cell factory. Curr Opin Biotechnol 23: 631-640.
[14]	Lindberg P, Park S, Melis A (2010) Engineering a platform for photosynthetic isoprene production in cyanobacteria, using Synechocystis as the model organism. Metabolic Eng 12: 70-79. doi: 10.1016/j.ymben.2009.10.001
[15]	Hatzimanikatis V, Li C, Ionita JA, et al. (2005) Exploring the diversity of complex metabolic networks. Bioinformatics 21: 1603-1609. doi: 10.1093/bioinformatics/bti213
[16]	Finley SD, Broadbelt LJ, Hatzimanikatis V (2009) Computational framework for predictive biodegradation. Biotechnol Bioeng 104: 1086-1097. doi: 10.1002/bit.22489
[17]	Henry CS, Broadbelt LJ, Hatzimanikatis V (2010) Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3-hydroxypropanoate. Biotechnol Bioeng 106: 462-473.
[18]	Fisher AK, Freedman BG, Bevan DR, et al. (2014) A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories. Comput Struct Biotechnol J 11: 91-99. doi: 10.1016/j.csbj.2014.08.010
[19]	Lee SK, Chou H, Ham TS, et al. (2008) Metabolic engineering of microorganisms for biofuels production: from bugs to synthetic biology to fuels. Curr Opin Biotechnol 19: 556-563.
[20]	McEwen JT, Atsumi S (2012) Alternative biofuel production in non-natural hosts. Curr Opin Biotechnol 23: 744-750.
[21]	Jang YS, Park JM, Choi S, et al. (2012) Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches. Biotechnol Adv 30: 989-1000. doi: 10.1016/j.biotechadv.2011.08.015
[22]	Atsumi S, Hanai T, Liao JC (2008) Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451: 86-89. doi: 10.1038/nature06450
[23]	Atsumi S, Higashide W, Liao JC (2009) Direct photosynthetic recycling of carbon dioxide to isobutyraldehyde. Nat Biotechnol 27: 1177-1180. doi: 10.1038/nbt.1586
[24]	Smith KM, Cho KM, Liao JC (2010) Engineering Corynebacterium glutamicum for isobutanol production. Appl Microbiol Biotechnol 87: 1045-1055. doi: 10.1007/s00253-010-2522-6
[25]	Jia X, Li S, Xie S, et al. (2011) Engineering a metabolic pathway for isobutanol biosynthesis in Bacillus subtilis. Appl Biochem Biotechnol 168: 1-9.
[26]	Chen X, Nielsen KF, Borodina I, et al. (2011) Increased isobutanol production in Saccharomyces cerevisiae by overexpression of genes in valine metabolism. Biotechnol Biofuels 4: 21. doi: 10.1186/1754-6834-4-21
[27]	Higashide W, Li Y, Yang Y, et al. (2011) Metabolic engineering of Clostridium cellulolyticum for production of isobutanol from cellulose. Appl Environ Microbiol 77: 2727-2733.
[28]	Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28: 245-248. doi: 10.1038/nbt.1614
[29]	Varma A, Palsson BO (1994) Metabolic flux balancing - basic concepts, scientific and practical use. Bio-Technol 12: 994-998. doi: 10.1038/nbt1094-994
[30]	Reed JL, Palsson BO (2004) Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res 14: 1797-1805.
[31]	Henry CS, DeJongh M, Best AA, et al. (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28: 977-982. doi: 10.1038/nbt.1672
[32]	Devoid S, Overbeek R, DeJongh M, et al. (2013) Automated genome annotation and metabolic model reconstruction in the SEED and Model SEED. Methods Mol Biol 985: 17-45. doi: 10.1007/978-1-62703-299-5_2
[33]	DeJongh M, Formsma K, Boillot P, et al. (2007) Toward the automated generation of genome-scale metabolic networks in the SEED. BMC Bioinformatics 8: 139. doi: 10.1186/1471-2105-8-139
[34]	Zakrzewski P, Medema MH, Gevorgyan A, et al. (2012) MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models. PloS One 7: e51511. doi: 10.1371/journal.pone.0051511
[35]	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: 524-531. doi: 10.1093/bioinformatics/btg015
[36]	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: 1290-1307. doi: 10.1038/nprot.2011.308
[37]	McAnulty MJ, Yen JY, Freedman BG, et al. (2012) Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico. BMC Syst Biol 6: 42. doi: 10.1186/1752-0509-6-42
[38]	Yen JY, Nazem-Bokaee H, Freedman BG, et al. (2013) Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints. Biotechnol J 8: 581-594.
[39]	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: 579-589. doi: 10.1104/pp.109.148817
[40]	Duarte NC, Herrgard MJ, Palsson BO (2004) Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res 14: 1298-1309. doi: 10.1101/gr.2250904
[41]	Nogales J, Gudmundsson S, Knight EM, et al. (2012) Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. Proc Natl Acad Sci U S A 109: 2678-2683. doi: 10.1073/pnas.1117907109
[42]	Feist AM, Henry CS, Reed JL, et al. (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3: 121.
[43]	Jones SW, Paredes CJ, Tracy B, et al. (2008) The transcriptional program underlying the physiology of clostridial sporulation. Genome Biol 9: R114. doi: 10.1186/gb-2008-9-7-r114
[44]	Lewis NE, Hixson KK, Conrad TM, et al. (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6: 390.
[45]	Keating SM, Bornstein BJ, Finney A, et al. (2006) SBMLToolbox: an SBML toolbox for MATLAB users. Bioinformatics 22: 1275-1277. doi: 10.1093/bioinformatics/btl111
[46]	Henry CS, Zinner JF, Cohoon MP, et al. (2009) iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations. Genome Biol 10: R69. doi: 10.1186/gb-2009-10-6-r69
[47]	Ro DK, Paradise EM, Ouellet M, et al. (2006) Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440: 940-943. doi: 10.1038/nature04640
[48]	Senger RS, Papoutsakis ET (2008) Genome-scale model for Clostridium acetobutylicum: Part II. Development of specific proton flux states and numerically determined sub-systems. Biotechnol Bioeng 101: 1053-1071.
[49]	Baez A, Cho KM, Liao JC (2011) High-flux isobutanol production using engineered Escherichia coli: a bioreactor study with in situ product removal. Appl Microbiol Biotechnol 90: 1681-1690. doi: 10.1007/s00253-011-3173-y
[50]	Blombach B, Riester T, Wieschalka S, et al. (2011) Corynebacterium glutamicum tailored for efficient isobutanol production. Appl Environ Microbiol 77: 3300-3310. doi: 10.1128/AEM.02972-10
[51]	Li S, Wen J, Jia X (2011) Engineering Bacillus subtilis for isobutanol production by heterologous Ehrlich pathway construction and the biosynthetic 2-ketoisovalerate precursor pathway overexpression. Appl Microbiol Biotechnol 91: 577-589. doi: 10.1007/s00253-011-3280-9
[52]	Follonier S, Panke S, Zinn M (2011) A reduction in growth rate of Pseudomonas putida KT2442 counteracts productivity advances in medium-chain-length polyhydroxyalkanoate production from gluconate. Microb Cell Fact 10: 25.
[53]	Tracy BP, Jones SW, Fast AG, et al. (2012) Clostridia: the importance of their exceptional substrate and metabolite diversity for biofuel and biorefinery applications. Curr Opin Biotechnol 23: 364-381. doi: 10.1016/j.copbio.2011.10.008
[54]	Senger RS, Papoutsakis ET (2008) Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis. Biotechnol Bioeng 101: 1036-1052.
[55]	Martin VJ, Pitera DJ, Withers ST, et al. (2003) Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat Biotechnol 21: 796-802. doi: 10.1038/nbt833

Reader Comments

your name:*

Email:*
© 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)