Modeling Plant Metabolism: Advancements and Future Capabilities

  • Margaret N. Simons-Senftle
  • Debolina Sarkar
  • Costas D. MaranasEmail author


Genome-scale metabolic (GSM) models of plants have flourished over the last decade with advancements in both their scope and their applications. While the first plant models were mainly developed to represent a comprehensive set of all metabolic reactions that occur within a plant, organ- and tissue-specific models have been developed and recently these models have been linked to create whole-plant metabolic models. GSM models provide a promising path to predict the effect of genetic and environmental perturbations on metabolism. These models capture the interplay between carbon and nitrogen metabolism, which is important in designing genetic manipulations that improve nitrogen use efficiency (NUE). There is also potential to apply and adapt the diverse set of algorithms developed for microbial GSM models to plants. These algorithms have yielded numerous success stories in predicting the metabolic effects of genetic manipulations by identifying strategies for over-producing chemicals, and driving discovery. Furthering the development of plant GSM models and associated algorithmic tools is expected to have a large impact on predicting manipulations to improve plant traits such as NUE.


Plant genome-scale metabolic models Flux balance analysis Metabolic engineering Kinetic modeling Plant metabolism 


  1. Bao A, Zhao Z, Ding G, Shi L, Xu F, Cai H (2014) Accumulated expression level of cytosolic glutamine synthetase 1 gene (OsGS1;1 or OsGS1;2) alter plant development and the carbon-nitrogen metabolic status in rice. PLoS ONE 9(4):e95581. Scholar
  2. Orth J, Thiele I, Palsson B (2010) What is flux balance analysis? Nature Biotechnol 28:245–248CrossRefGoogle Scholar
  3. Chan SHJ, Cai J, Wang L, Simons-Senftle MN, Maranas CD (2017) Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models. Bioinformatics btx453Google Scholar
  4. Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5(4):264–276CrossRefPubMedGoogle Scholar
  5. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D, Weitz KK, Eils R, Konig R, Smith RD, Palsson BO (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6:390. Scholar
  6. Lakshmanan M, Koh G, Chung BK, Lee DY (2014) Software applications for flux balance analysis. Brief Bioinform 15(1):108–122. Scholar
  7. Dandekar T, Fieselmann A, Majeed S, Ahmed Z (2014) Software applications toward quantitative metabolic flux analysis and modeling. Brief Bioinform 15(1):91–107. Scholar
  8. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: constraints-based reconstruction and analysis for python. BMC Syst Biol 7, Artn 74.
  9. Arkin AP, Stevens RL, Cottingham RW, Maslov S, Henry CS, Dehal P, Ware D, Perez F, Harris NL, Canon S, Sneddon MW, Henderson ML, Riehl WJ, Gunter D, Murphy-Olson D, Chan S, Kamimura RT, Brettin TS, Meyer F, Chivian D, Weston DJ, Glass EM, Davison BH, Kumari S, Allen BH, Baumohl J, Best AA, Bowen B, Brenner SE, Bun CC, Chandonia J-M, Chia J-M, Colasanti R, Conrad N, Davis JJ, DeJongh M, Devoid S, Dietrich E, Drake MM, Dubchak I, Edirisinghe JN, Fang G, Faria JP, Frybarger PM, Gerlach W, Gerstein M, Gurtowski J, Haun HL, He F, Jain R, Joachimiak MP, Keegan KP, Kondo S, Kumar V, Land ML, Mills M, Novichkov P, Oh T, Olsen GJ, Olson B, Parrello B, Pasternak S, Pearson E, Poon SS, Price G, Ramakrishnan S, Ranjan P, Ronald PC, Schatz MC, Seaver SMD, Shukla M, Sutormin RA, Syed MH, Thomason J, Tintle NL, Wang D, Xia F, Yoo H, Yoo S (2016) The DOE systems biology knowledgebase (KBase). bioRxiv.
  10. Edwards JS, Palsson BO (1999) Systems properties of the Haemophilus influenzae Rd metabolic genotype. J Biol Chem 274(25):17410–17416CrossRefPubMedGoogle Scholar
  11. Shastri AA, Morgan JA (2005) Flux balance analysis of photoautotrophic metabolism. Biotechnol Prog 21(6):1617–1626. Scholar
  12. Grafahrend-Belau E, Schreiber F, Koschutzki 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. Scholar
  13. 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. Scholar
  14. de Oliveira Dal’Molin CG, Quek LE, Palfreyman RW, Brumbley SM, Nielsen LK (2010a) AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol 152(2):579–589. Scholar
  15. Saha R, Suthers PF, Maranas CD (2011) Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism. Plos One 6(7).
  16. Hay J, Schwender J (2011a) Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos. Plant J 67(3):526–541. Scholar
  17. Yuan H, Cheung CY, Poolman MG, Hilbers PA, van Riel NA (2016) A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism. Plant J 85(2):289–304. Scholar
  18. Poolman MG, Kundu S, Shaw R, Fell DA (2013) Responses to light intensity in a genome-scale model of rice metabolism. Plant Physiol 162(2):1060–1072. Scholar
  19. Lakshmanan M, Zhang Z, Mohanty B, Kwon JY, Choi HY, Nam HJ, Kim DI, Lee DY (2013) Elucidating rice cell metabolism under flooding and drought stresses using flux-based modeling and analysis. Plant Physiol 162(4):2140–2150. Scholar
  20. Simons M, Saha R, Amiour N, Kumar A, Guillard L, Clément G, Miquel M, Li Z, Mouille G, Lea PJ, Hirel B, Maranas CD (2014a) Assessing the metabolic impact of nitrogen availability using a compartmentalized maize leaf genome-scale model. Plant Physiol 166(3):1659–1674. Scholar
  21. Seaver SM, Bradbury LM, Frelin O, Zarecki R, Ruppin E, Hanson AD, Henry CS (2015) Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm. Front Plant Sci 6:142. Scholar
  22. de Oliveira Dal’Molin CG, Quek LE, Palfreyman RW, Brumbley SM, Nielsen LK (2010b) C4GEM, a genome-scale metabolic model to study C4 plant metabolism. Plant Physiol 154(4):1871–1885. Scholar
  23. de Oliveira Dal’Molin CG, Quek LE, Saa PA, Nielsen LK (2015) A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. Front Plant Sci 6:4. Scholar
  24. Grafahrend-Belau E, Junker A, Eschenroder A, Muller J, Schreiber F, Junker BH (2013) Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale. Plant Physiol 163(2):637–647. Scholar
  25. Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320. Scholar
  26. Fritz C, Palacios-Rojas N, Feil R, Stitt M (2006) Regulation of secondary metabolism by the carbon-nitrogen status in tobacco: nitrate inhibits large sectors of phenylpropanoid metabolism. Plant J 46(4):533–548. Scholar
  27. Kumar VS, Dasika MS, Maranas CD (2007) Optimization based automated curation of metabolic reconstructions. BMC Bioinf 8:212. Scholar
  28. Maranas CD, Zomorrodi AR (2016) Resolving network gaps and growth prediction inconsistencies in metabolic networks. In: Optimization methods in metabolic networks. Wiley, Inc, pp 119–135.
  29. Thiele I, Vlassis N, Fleming RM (2014) fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30(17):2529–2531. Scholar
  30. Tatsis EC, O’Connor SE (2016) New developments in engineering plant metabolic pathways. Curr Opin Biotechnol 42:126–132. Scholar
  31. Li CH, Henry CS, Jankowski MD, Ionita JA, Hatzimanikatis V, Broadbelt LJ (2004) Computational discovery of biochemical routes to specialty chemicals. Chem Eng Sci 59(22–23):5051–5060. Scholar
  32. Hatzimanikatis V, Li C, Ionita JA, Henry CS, Jankowski MD, Broadbelt LJ (2005) Exploring the diversity of complex metabolic networks. Bioinformatics 21(8):1603–1609. Scholar
  33. Jeffryes JG, Colastani RL, Elbadawi-Sidhu M, Kind T, Niehaus TD, Broadbelt LJ, Hanson AD, Fiehn O, Tyo KE, Henry CS (2015) MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J Cheminform 7:44. Scholar
  34. Price ND, Schellenberger J, Palsson BO (2004) Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J 87(4):2172–2186. Scholar
  35. Rothstein SJ, Bi YM, Coneva V, Han M, Good A (2014) The challenges of commercializing second-generation transgenic crop traits necessitate the development of international public sector research infrastructure. J Exp Bot 65(19):5673–5682. Scholar
  36. Beatty PH, Klein MS, Fischer JJ, Lewis IA, Muench DG, Good AG (2016) Understanding plant nitrogen metabolism through metabolomics and computational approaches. Plants (Basel) 5(4).
  37. Suthers PF, Zomorrodi A, Maranas CD (2009) Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol Syst Biol 5:301. Scholar
  38. Pratapa A, Balachandran S, Raman K (2015) Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks. Bioinformatics 31(20):3299–3305. Scholar
  39. Chowdhury R, Chowdhury A, Maranas CD (2015) Using gene essentiality and synthetic lethality information to correct yeast and CHO cell genome-scale models. Metabolites 5(4):536–570. Scholar
  40. Blazier AS, Papin JA (2012) Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 3:299. Scholar
  41. Hyduke DR, Lewis NE, Palsson BO (2013) Analysis of omics data with genome-scale models of metabolism. Mol BioSyst 9(2):167–174. Scholar
  42. Saha R, Chowdhury A, Maranas CD (2014) Recent advances in the reconstruction of metabolic models and integration of omics data. Curr Opin Biotechnol 29:39–45. Scholar
  43. Hay J, Schwender J (2011b) Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to (1)(3)C metabolic flux analysis. Plant J 67(3):513–525. Scholar
  44. Williams K, Percival F, Merino J, Mooney HA (1987) Estimation of tissue construction cost from heat of combustion and organic nitrogen-content. Plant, Cell Environ 10(9):725–734Google Scholar
  45. Leegood RC (2002) C4 photosynthesis: principles of CO2 concentration and prospects for its introduction into C3 plants. J Exp Bot 53(369):581–590. Scholar
  46. Ren QH, Paulsen IT (2005) Comparative analyses of fundamental differences in membrane transport capabilities in prokaryotes and eukaryotes. PLoS Comput Biol 1 (3):190–201, Artn e27.
  47. Linka N, Weber AP (2010) Intracellular metabolite transporters in plants. Mol Plant 3(1):21–53. Scholar
  48. Linka N, Theodoulou FL (2013) Metabolite transporters of the plant peroxisomal membrane: known and unknown. In: del Río LA (ed) Peroxisomes and their key role in cellular signaling and metabolism. Springer Netherlands, Dordrecht, pp 169–194.
  49. Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6:401. Scholar
  50. Ramos S, Schuldiner S, Kaback HR (1976) The electrochemical gradient of protons and its relationship to active transport in Escherichia coli membrane vesicles. Proc Natl Acad Sci U. S. A 73(6):1892–1896CrossRefPubMedPubMedCentralGoogle Scholar
  51. Sze H (1984) H+ -translocating ATPases of the plasma membrane and tonoplast of plant cells. Physiol Plant 61(4):683–691. Scholar
  52. Cheung CY, Williams TC, Poolman MG, Fell DA, Ratcliffe RG, Sweetlove LJ (2013) A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions. Plant J 75(6):1050–1061. Scholar
  53. Bogart E, Myers CR (2016) Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves. PLoS One 11(3):e0151722. Scholar
  54. Beatty PH, Klein MS, Fischer JJ, Lewis IA, Muench DG, Good AG (2016b) Understanding plant nitrogen metabolism through metabolomics and computational approaches. Plants 5(4):39. Scholar
  55. Yu LH, Wu J, Tang H, Yuan Y, Wang SM, Wang YP, Zhu QS, Li SG, Xiang CB (2016) Overexpression of Arabidopsis NLP7 improves plant growth under both nitrogen-limiting and -sufficient conditions by enhancing nitrogen and carbon assimilation. Sci Rep 6, Artn 27795.
  56. Burgard A, Pharkya P, Maranas C (2003) OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84:647–657CrossRefPubMedGoogle Scholar
  57. Patil KR, Rocha I, Forster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinform 6, Artn 308.
  58. Pharkya P, Maranas C (2006) An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab Eng 8:1–13CrossRefPubMedGoogle Scholar
  59. Pharkya P, Burgard AP, Maranas CD (2004) OptStrain: a computational framework for redesign of microbial production systems. Genome Res 14(11):2367–2376. Scholar
  60. Ranganathan S, Suthers P, Maranas C (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput Biol 6:e1000744CrossRefPubMedPubMedCentralGoogle Scholar
  61. Yang L, Cluett WR, Mahadevan R (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab Eng 13(3):272–281. Scholar
  62. Asadollahi MA, Maury J, Patil KR, Schalk M, Clark A, Nielsen J (2009) Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab Eng 11(6):328–334. Scholar
  63. Bro C, Regenberg B, Forster J, Nielsen J (2006) In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab Eng 8(2):102–111. Scholar
  64. Tee TW, Chowdhury A, Maranas CD, Shanks JV (2014) Systems metabolic engineering design: fatty acid production as an emerging case study. Biotechnol Bioeng 111(5):849–857. Scholar
  65. Nielsen J, Keasling JD (2016) Engineering cellular metabolism. Cell 164(6):1185–1197. Scholar
  66. Sweetlove LJ, Fernie AR (2013) The spatial organization of metabolism within the plant cell. Annu Rev Plant Biol 64(64):723–746. Scholar
  67. Sweetlove LJ, Nielsen J, Fernie AR (2017) Engineering central metabolism—a grand challenge for plant biologists. Plant J 90(4):749–763. Scholar
  68. Rasse DP, Tocquin P (2006) Leaf carbohydrate controls over Arabidopsis growth and response to elevated CO2: an experimentally based model. The New Phytol 172(3):500–513. Scholar
  69. White JW, Hoogenboom G, Hunt LA (2005) A structured procedure for assessing how crop models respond to temperature. Agron J 97(2):426–439CrossRefGoogle Scholar
  70. Kollas C, Kersebaum KC, Nendel C, Manevski K, Muller C, Palosuo T, Armas-Herrera CM, Beaudoin N, Bindi M, Charfeddine M, Conradt T, Constantin J, Eitzinger J, Ewert F, Ferrise R, Gaiser T, de Cortazar-Atauri IG, Giglio L, Hlavinka P, Hoffmann H, Hoffmann MP, Launay M, Manderscheid R, Mary B, Mirschel W, Moriondo M, Olesen JE, Ouml;zturk I, Pacholski JE, Ripoche-Wachter D, Roggero PP, Roncossek S, Rotter RP, Ruget F, Sharif B, Trnka M, Sharif B, Sharif B, Ventrella D, Waha K, Wegehenkel M, Weigel HJ, Wu LH (2015) Crop rotation modelling—a European model intercomparison. Eur J Agron 70:98–111. Scholar
  71. Osman J, Inglada J, Dejoux JF (2015) Assessment of a Markov logic model of crop rotations for early crop mapping. Comput Electron Agr 113:234–243. Scholar
  72. Liang H, Hu K, Batchelor WD, Qi Z, Li B (2016) An integrated soil-crop system model for water and nitrogen management in North China. Sci Rep 6:25755. Scholar
  73. Mishra A, Hansen JW, Dingkuhn M, Baron C, Traore SB, Ndiaye O, Ward MN (2008) Sorghum yield prediction from seasonal rainfall forecasts in Burkina Faso. Agr Forest Meteorol 148(11):1798–1814. Scholar
  74. Hansen JW (2005) Integrating seasonal climate prediction and agricultural models for insights into agricultural practice. Philos Trans R Soc B 360(1463):2037–2047. Scholar
  75. Kang YH, Khan S, Ma XY (2009) Climate change impacts on crop yield, crop water productivity and food security—a review. Prog Nat Sci-Mater 19(12):1665–1674. Scholar
  76. Benincasa P, Guiducci M, Tei F (2011) The nitrogen use efficiency: meaning and sources of variation-case studies on three vegetable crops in central Italy. Horttechnology 21(3):266–273Google Scholar
  77. Carpenter-Boggs L, Pikul JL, Vigil MF, Riedell WE (2000) Soil nitrogen mineralization influenced by crop rotation and nitrogen fertilization. Soil Sci Soc Am J 64(6):2038–2045CrossRefGoogle Scholar
  78. McAllister CH, Beatty PH, Good AG (2012) Engineering nitrogen use efficient crop plants: the current status. Plant Biotechnol J 10(9):1011–1025. Scholar
  79. Lerman JA, Hyduke DR, Latif H, Portnoy VA, Lewis NE, Orth JD, Schrimpe-Rutledge AC, Smith RD, Adkins JN, Zengler K, Palsson BO (2012) In silico method for modelling metabolism and gene product expression at genome scale. Nat Commun 3:929. Scholar
  80. O’Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BO (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693. Scholar
  81. Rügen M, Bockmayr A, Steuer R (2015) Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA. Sci Rep 5, Artn 15247.
  82. Matt P, Geiger M, Walch-Liu P, Engels C, Krapp A, Stitt M (2001) The immediate cause of the diurnal changes of nitrogen metabolism in leaves of nitrate-replete tobacco: a major imbalance between the rate of nitrate reduction and the rates of nitrate uptake and ammonium metabolism during the first part of the light period. Plant, Cell Environ 24(2):177–190. Scholar
  83. Delhon P, Gojon A, Tillard P, Passama L (1996) Diurnal regulation of NO3 uptake in soybean plants IV. Dependence on current photosynthesis and sugar availability to the roots. J Exp Bot 47(7):893–900. Scholar
  84. Hayes KR, Beatty M, Meng X, Simmons CR, Habben JE, Danilevskaya ON (2010) Maize global transcriptomics reveals pervasive leaf diurnal rhythms but rhythms in developing ears are largely limited to the core oscillator. PLoS ONE 5(9):e12887. Scholar
  85. Usadel B, Poree F, Nagel A, Lohse M, Czedik-Eysenberg A, Stitt M (2009) A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, Maize. Plant, Cell Environ 32(9):1211–1229. Scholar
  86. Ko DK, Rohozinski D, Song Q, Taylor SH, Juenger TE, Harmon FG, Chen ZJ (2016) Temporal shift of circadian-mediated gene expression and carbon fixation contributes to biomass heterosis in maize hybrids. PLoS Genet 12(7):e1006197. Scholar
  87. Riter LS, Jensen PK, Ballam JM, Urbanczyk-Wochniak E, Clough T, Vitek O, Sutton J, Athanas M, Lopez MF, MacIsaac S (2011) Evaluation of label-free quantitative proteomics in a plant matrix: a case study of the night-to-day transition in corn leaf. Anal Methods 3(12):2733–2739. Scholar
  88. Amiour N, Imbaud S, Clement G, Agier N, Zivy M, Valot B, Balliau T, Armengaud P, Quillere I, Canas R, Tercet-Laforgue T, Hirel B (2012) The use of metabolomics integrated with transcriptomic and proteomic studies for identifying key steps involved in the control of nitrogen metabolism in crops such as maize. J Exp Bot 63(14):5017–5033. Scholar
  89. Wang R, Okamoto M, Xing X, Crawford NM (2003) Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1,000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, iron, and sulfate metabolism. Plant Physiol 132(2):556–567. Scholar
  90. Opitz N, Paschold A, Marcon C, Malik WA, Lanz C, Piepho HP, Hochholdinger F (2014) Transcriptomic complexity in young maize primary roots in response to low water potentials. BMC Genom 15:741. Scholar
  91. Li PC, Cao W, Fang HM, Xu SH, Yin SY, Zhang YY, Lin DZ, Wang JN, Chen YF, Xu CW, Yang ZF (2017) Transcriptomic profiling of the maize (Zea mays L.) leaf response to abiotic stresses at the seedling stage. Front Plant Sci 8, Artn 290.
  92. Shao R, Xin L, Mao J, Li L, Kang G, Yang Q (2015) Physiological, ultrastructural and proteomic responses in the leaf of maize seedlings to polyethylene glycol-stimulated severe water deficiency. Int J Mol Sci 16(9):21606–21625. Scholar
  93. Wu L, Tian L, Wang S, Zhang J, Liu P, Tian Z, Zhang H, Liu H, Chen Y (2016) Comparative proteomic analysis of the response of maize (Zea mays L.) leaves to long photoperiod condition. Front Plant Sci 7:752. Scholar
  94. Simons M, Saha R, Guillard L, Clément G, Armengaud P, Cañas R, Maranas CD, Lea PJ, Hirel B (2014b) Nitrogen-use efficiency in maize (Zea mays L.): from ‘omics’ studies to metabolic modelling. J Exp Bot 65(19):5657–5671. Scholar
  95. Smallbone K, Simeonidis E, Swainston N, Mendes P (2010) Towards a genome-scale kinetic model of cellular metabolism. BMC Syst Biol 4:6. Scholar
  96. Jamshidi N, Palsson BO (2008) Formulating genome-scale kinetic models in the post-genome era. Mol Syst Biol 4:171. Scholar
  97. Khodayari A, Maranas CD (2016) A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Nat Commun 7:13806. Scholar
  98. Tan Y, Rivera JG, Contador CA, Asenjo JA, Liao JC (2011) Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux. Metab Eng 13(1):60–75. Scholar
  99. Cardenas J, Da Silva NA (2014) Metabolic engineering of Saccharomyces cerevisiae for the production of triacetic acid lactone. Metab Eng 25:194–203. Scholar
  100. Xu P, Ranganathan S, Fowler ZL, Maranas CD, Koffas MAG (2011) Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Metab Eng 13(5):578–587. Scholar
  101. Lin F, Chen Y, Levine R, Lee K, Yuan Y, Lin XN (2013) Improving fatty acid availability for bio-hydrocarbon production in Escherichia coli by metabolic engineering. PLoS ONE 8(10):e78595. Scholar
  102. Khodayari A, Chowdhury A, Maranas CD (2014) Succinate overproduction: a case study of computational strain design using a comprehensive escherichia coli kinetic model. Front Bioeng Biotechnol 2:76. Scholar
  103. Chatterjee A, Kundu S (2015) Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica. Sci Rep 5:14975CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Margaret N. Simons-Senftle
    • 1
  • Debolina Sarkar
    • 1
  • Costas D. Maranas
    • 1
    Email author
  1. 1.Department of Chemical EngineeringThe Pennsylvania State UniversityUniversity ParkUSA

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