Computer-Guided Metabolic Engineering

  • M. A. Valderrama-Gomez
  • S. G. Wagner
  • A. KremlingEmail author
Part of the Springer Protocols Handbooks book series (SPH)


Computational methods and tools are nowadays widely applied for rational Metabolic Engineering approaches. However, what is still missing are clear advices on the right order of the application of these tools. The availability of genomic information for a large number of cellular systems especially requires the use of computers to store, analyze, and process knowledge of single enzymes, metabolic pathways, and cellular networks. The trend of integrating measured quantities for the metabolome, the transcriptome, and the proteome into mathematical models, combined with methods for the rational design of cellular networks, has led to the research field Systems Metabolic Engineering, a field that extends and amplifies the classical field of Metabolic Engineering. This chapter describes mathematical and computational approaches on the cellular and the process levels. In the Material section, modeling approaches and methods for model analysis are introduced, and the current state of the art is reviewed. In the Method section, we propose a protocol for efficiently combining various approaches for the optimal production of desired biotechnological products.


Constraint-based modelling Dynamic flux balance analysis Flux balance analysis In silico strain optimization Metabolic Engineering Metabolic models Stoichiometric analysis Succinate production Systems Metabolic Engineering Theoretical yields 


  1. 1.
    Trentacoste EM, Shrestha RP, Smith SR et al (2013) Metabolic engineering of lipid catabolism increases microalgal lipid accumulation without compromising growth. Proc Natl Acad Sci U S A 110:19748–19753. doi: 10.1073/pnas.1309299110 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Liang M-H, Jiang J-G (2013) Advancing oleaginous microorganisms to produce lipid via metabolic engineering technology. Prog Lipid Res 52:395–408. doi: 10.1016/j.plipres.2013.05.002 CrossRefPubMedGoogle Scholar
  3. 3.
    Röling WFM, van Bodegom PM (2014) Toward quantitative understanding on microbial community structure and functioning: a modeling-centered approach using degradation of marine oil spills as example. Front Microbiol 5:125. doi: 10.3389/fmicb.2014.00125 PubMedPubMedCentralGoogle Scholar
  4. 4.
    Sierra-García IN, Correa Alvarez J, de Vasconcellos SP et al (2014) New hydrocarbon degradation pathways in the microbial metagenome from Brazilian petroleum reservoirs. PLoS One 9, e90087. doi: 10.1371/journal.pone.0090087 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305. doi: 10.1038/nrmicro2737 PubMedPubMedCentralGoogle Scholar
  6. 6.
    Lee JW, Kim TY, Jang Y-S et al (2011) Systems metabolic engineering for chemicals and materials. Trends Biotechnol 29:370–378. doi: 10.1016/j.tibtech.2011.04.001 CrossRefPubMedGoogle Scholar
  7. 7.
    Ogata H, Goto S, Sato K et al (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34. doi: 10.1093/nar/27.1.29 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Karp P (1996) EcoCyc: an encyclopedia of Escherichia coli genes and metabolism. Nucleic Acids Res 24:32–39. doi: 10.1093/nar/24.1.32 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Schomburg I, Hofmann O, Baensch C et al (2000) Enzyme data and metabolic information: BRENDA, a resource for research in biology, biochemistry, and medicine. Gene Funct Dis 1:109–118. doi: 10.1002/1438-826X(200010)1:3/4<109::AID-GNFD109>3.0.CO;2-O CrossRefGoogle Scholar
  10. 10.
    Pirt SJ (1965) The maintenance energy of bacteria in growing cultures. Proc R Soc Lond Ser B Biol Sci 163:224–231CrossRefGoogle Scholar
  11. 11.
    Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121. doi: 10.1038/nprot.2009.203 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Oberhardt MA, Palsson BØ, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320. doi: 10.1038/msb.2009.77 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Reed JL, Palsson BO (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 185:2692–2699. doi: 10.1128/JB.185.9.2692-2699.2003 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Feist AM, Palsson BØ (2008) The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotechnol 26:659–667. doi: 10.1038/nbt1401 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Reed JL, Vo TD, Schilling CH, Palsson BO (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4:R54. doi: 10.1186/gb-2003-4-9-r54 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    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. doi: 10.1038/msb4100155 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Orth JD, Conrad TM, Na J et al (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism--2011. Mol Syst Biol 7:535. doi: 10.1038/msb.2011.65 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Covert MW, Famili I, Palsson BO (2003) Identifying constraints that govern cell behavior: a key to converting conceptual to computational models in biology? Biotechnol Bioeng 84:763–772. doi: 10.1002/bit.10849 CrossRefPubMedGoogle Scholar
  19. 19.
    Hamilton JJ, Dwivedi V, Reed JL (2013) Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models. Biophys J 105:512–522. doi: 10.1016/j.bpj.2013.06.011 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Beg QK, Vazquez A, Ernst J et al (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci U S A 104:12663–12668. doi: 10.1073/pnas.0609845104 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Covert MW, Schilling CH, Palsson B (2001) Regulation of gene expression in flux balance models of metabolism. J Theor Biol 213:73–88. doi: 10.1006/jtbi.2001.2405 CrossRefPubMedGoogle Scholar
  22. 22.
    Edwards JS, Ibarra RU, Palsson BO (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19:125–130. doi: 10.1038/84379 CrossRefPubMedGoogle Scholar
  23. 23.
    Varma A, Palsson BO (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 60:3724–3731PubMedPubMedCentralGoogle Scholar
  24. 24.
    Pramanik J, Keasling JD (1997) Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. Biotechnol Bioeng 56:398–421. doi: 10.1002/(SICI)1097-0290(19971120)56:4<398::AID-BIT6>3.0.CO;2-J CrossRefPubMedGoogle Scholar
  25. 25.
    Kremling A (2013) Systems biology: mathematical modeling and model analysis. CRC/Taylor & Francis, Boca RatonGoogle Scholar
  26. 26.
    Hyduke DR, Lewis NE, Palsson BØ (2013) Analysis of omics data with genome-scale models of metabolism. Mol Biosyst 9:167–174. doi: 10.1039/c2mb25453k CrossRefPubMedGoogle Scholar
  27. 27.
    Kim MK, Lun DS (2014) Methods for integration of transcriptomic data in genome-scale metabolic models. Comput Struct Biotechnol J 11:59–65. doi: 10.1016/j.csbj.2014.08.009 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Carrera J, Estrela R, Luo J et al (2014) An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol Syst Biol 10:735–735. doi: 10.15252/msb.20145108 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Murphy TA, Young JD (2013) ETA: robust software for determination of cell specific rates from extracellular time courses. Biotechnol Bioeng 110:1748–1758. doi: 10.1002/bit.24836 CrossRefPubMedGoogle Scholar
  30. 30.
    Schellenberger J, Que R, Fleming RMT 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 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: COnstraints-based reconstruction and analysis for python. BMC Syst Biol 7:74. doi: 10.1186/1752-0509-7-74 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Klamt S, Saez-Rodriguez J, Gilles E (2007) Structural and functional analysis of cellular networks with Cell NetAnalyzer. BMC Syst Biol 1:2. doi: 10.1186/1752-0509-1-2 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Urbanczik R (2006) SNA – a toolbox for the stoichiometric analysis of metabolic networks. BMC Bioinformatics 7:129. doi: 10.1186/1471-2105-7-129 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Schwarz R, Musch P, von Kamp A et al (2005) YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities. BMC Bioinformatics 6:135. doi: 10.1186/1471-2105-6-135 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Thiele I, Fleming RMT, Que R et al (2012) Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage. PLoS One 7:e45635. doi: 10.1371/journal.pone.0045635 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    O’Brien EJ, Lerman JA, Chang RL et al (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693. doi: 10.1038/msb.2013.52 PubMedPubMedCentralGoogle Scholar
  37. 37.
    Liu JK, O’Brien EJ, Lerman JA et al (2014) Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale. BMC Syst Biol 8:110. doi: 10.1186/s12918-014-0110-6 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Kremling A (2007) Comment on mathematical models which describe transcription and calculate the relationship between mRNA and protein expression ratio. Biotechnol Bioeng 96:815–819. doi: 10.1002/bit.21065 CrossRefPubMedGoogle Scholar
  39. 39.
    Carta A (2014) Modelling, analysis and control for systems biology: application to bacterial growth models. Dissertation, University of Nice-Sophia AntipolisGoogle Scholar
  40. 40.
    Shen CR, Liao JC (2013) Synergy as design principle for metabolic engineering of 1-propanol production in Escherichia coli. Metab Eng 17:12–22. doi: 10.1016/j.ymben.2013.01.008 CrossRefPubMedGoogle Scholar
  41. 41.
    Sánchez AM, Bennett GN, San K-Y (2005) Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity. Metab Eng 7:229–239. doi: 10.1016/j.ymben.2005.03.001 CrossRefPubMedGoogle Scholar
  42. 42.
    Jantama K, Haupt MJ, Svoronos SA et al (2008) Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of C that produce succinate and malate. Biotechnol Bioeng 99:1140–1153. doi: 10.1002/bit.21694 CrossRefPubMedGoogle Scholar
  43. 43.
    Zhang X, Jantama K, Moore JC et al (2009) Metabolic evolution of energy-conserving pathways for succinate production in Escherichia coli. Proc Natl Acad Sci 106:20180–20185. doi: 10.1073/pnas.0905396106 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Yang L, Cluett WR, Mahadevan R (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab Eng 13:272–281. doi: 10.1016/j.ymben.2011.03.002 CrossRefPubMedGoogle Scholar
  45. 45.
    Lin H, Bennett GN, San K-Y (2005) Genetic reconstruction of the aerobic central metabolism in Escherichia coli for the absolute aerobic production of succinate. Biotechnol Bioeng 89:148–156. doi: 10.1002/bit.20298 CrossRefPubMedGoogle Scholar
  46. 46.
    Hoefel T, Faust G, Reinecke L et al (2012) Comparative reaction engineering studies for succinic acid production from sucrose by metabolically engineered Escherichia coli in fed-batch-operated stirred tank bioreactors. Biotechnol J 7:1277–1287. doi: 10.1002/biot.201200046 CrossRefPubMedGoogle Scholar
  47. 47.
    Sánchez AM, Bennett GN, San K-Y (2006) Batch culture characterization and metabolic flux analysis of succinate-producing Escherichia coli strains. Metab Eng 8:209–226. doi: 10.1016/j.ymben.2005.11.004 CrossRefPubMedGoogle Scholar
  48. 48.
    Wang W, Li Z, Xie J, Ye Q (2009) Production of succinate by a pflB ldhA double mutant of Escherichia coli overexpressing malate dehydrogenase. Bioprocess Biosyst Eng 32:737–745. doi: 10.1007/s00449-009-0298-9 CrossRefPubMedGoogle Scholar
  49. 49.
    Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84:647–657. doi: 10.1002/bit.10803 CrossRefPubMedGoogle Scholar
  50. 50.
    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 CrossRefPubMedGoogle Scholar
  51. 51.
    Patil KR, Rocha I, Förster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6:308. doi: 10.1186/1471-2105-6-308 CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Lun DS, Rockwell G, Guido NJ et al (2009) Large-scale identification of genetic design strategies using local search. Mol Syst Biol 5:296. doi: 10.1038/msb.2009.57 CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Ranganathan S, Suthers PF, Maranas CD (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput Biol 6, e1000744. doi: 10.1371/journal.pcbi.1000744 CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:119. doi: 10.1038/msb4100162 CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Schellenberger J, Park JO, Conrad TM, Palsson BØ (2010) BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11:213. doi: 10.1186/1471-2105-11-213 CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99:15112–15117. doi: 10.1073/pnas.232349399 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Mahadevan R, Edwards JS, Doyle FJ (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83:1331–1340. doi: 10.1016/S0006-3495(02)73903-9 CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Zhuang K, Ma E, Lovley DR, Mahadevan R (2012) The design of long-term effective uranium bioremediation strategy using a community metabolic model. Biotechnol Bioeng 109:2475–2483. doi: 10.1002/bit.24528 CrossRefPubMedGoogle Scholar
  59. 59.
    Zhuang K, Yang L, Cluett WR, Mahadevan R (2013) Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. BMC Biotechnol 13:8. doi: 10.1186/1472-6750-13-8 CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • M. A. Valderrama-Gomez
    • 1
  • S. G. Wagner
    • 1
  • A. Kremling
    • 1
    Email author
  1. 1.Fachgebiet für SystembiotechnologieTechnische Universität MünchenGarching bei MünchenGermany

Personalised recommendations