Modeling Mycobacterium tuberculosis H37Rv In Silico



Network reconstructions and constraint-based modeling have been shown to be effective methods for understanding complex processes, such as metabolism. These reconstructions are in fact biologically structured knowledge-bases that can be queried through computations, and thus have become valuable tools for Systems Biology. Strengths of this approach include flexibility in incorporating “incomplete” data measurements, the ability to incorporate different types of data (high-throughput as well as physiological), simultaneously, as well as the ability to make predictions with minimal reliance on parameter and curve fitting. Thus, this approach aims to move away from fitting data to describe experimental results using the current understanding of metabolism in order to interpret the data, make predictions, and to identify the gaps and bridges in knowledge.

The critical components for creating genome-scale reconstructions of metabolism include a sequenced and annotated genome, reaction stoichiometry for the annotated enzymes, and a bibliome for the organism (combined primary and secondary literature sources). Network reconstructions of the devastating pathogen Mycobacterium tuberculosis have been developed and have enabled the ability to query functional capabilities using constraint-based modeling approaches. Since these networks are then structured in terms of “gene–protein–reaction” associations, these knowledge-bases can serve as biologically structured databases onto which various high-throughput data types can be directly mapped on.

This chapter will focus on the model reconstruction process, methods that have been employed for analysis, and predictive applications of modeling the pathogen H37Rv strain of tuberculosis. Employing the existing analysis methods and available datasets there have already been a large number of applications for modeling constraint-based modeling of H37Rv. The reconstruction process is a time and resource intensive procedure and to date high quality reconstructions have not been possible without manual curation. The benefit of having a detailed and quality ­controlled reconstruction procedure is to help determine a high quality model that will provide more meaningful predictions from simulations. Applications of M. tuberculosis models have included the prediction of growth rates, assessment of different growth media, prediction of gene knockouts, identification of new drug targets, identification of alternative drug targets for existing drugs, and modeling the interaction macrophages during different infectious states.

Historically, technological advancements have driven biological discovery and have thus been a limiting factor in the development of methods to modify and alter biology, e.g., antibiotics. However, in the past decade with various high-throughput technologies (e.g., transcriptomics, proteomics, metabolomics, etc.) are being employed more frequently, thus there is a growing burden and need for means to integrate, interpret, and ideally make predictions for these datasets. Given the successes to date, with further development of new methods in conjunction with deeper experimental probing of tuberculosis in vitro and in vivo, constraint-based modeling will likely become even more important in the finding new targets and treatments for tuberculosis.


Metabolic Network Flux Balance Analysis Potential Drug Target Network Reconstruction Human Alveolar Macrophage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28(3):245–248PubMedCrossRefGoogle Scholar
  2. 2.
    Palsson BO (2006) Systems biology: determining the capabilities of reconstructed networks. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  3. 3.
    Raman K, Rajagopalan P, Chandra N (2005) Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs. PLoS Comput Biol 1(5):e46PubMedCrossRefGoogle Scholar
  4. 4.
    Beste DJ, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek AM, McFadden J (2007) GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol 8(5):R89PubMedCrossRefGoogle Scholar
  5. 5.
    Jamshidi N, Palsson BO (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 1:26PubMedCrossRefGoogle Scholar
  6. 6.
    Rom W, Garay S (2004) Tuberculosis, 2nd edn. Lippincott Williams and Wilkins, PhiladelphiaGoogle Scholar
  7. 7.
    Thiele I, Palsson BO (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5(1):93–121PubMedCrossRefGoogle Scholar
  8. 8.
    Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28(9):977–982PubMedCrossRefGoogle Scholar
  9. 9.
    Kanehisa M (2002) The KEGG database. Novartis Found Symp 247:91–101, discussion 101–103, 119–128, 244–152PubMedCrossRefGoogle Scholar
  10. 10.
    Lew JM, Kapopoulou A, Jones LM, Cole ST (2011) TubercuList – 10 years after. Tuberculosis (Edinb) 91(1):1–7CrossRefGoogle Scholar
  11. 11.
    Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crecy-Lagard V, Diaz N, Disz T, Edwards R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, Pusch GD, Rodionov DA, Ruckert C, Steiner J, Stevens R, Thiele I, Vassieva O, Ye Y, Zagnitko O, Vonstein V (2005) The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33(17):5691–5702PubMedCrossRefGoogle Scholar
  12. 12.
    Jamshidi N, Palsson BO (2008) Formulating genome-scale kinetic models in the post-genome era. Mol Syst Biol 4:171PubMedCrossRefGoogle Scholar
  13. 13.
    Beard DA, Liang SD, Qian H (2002) Energy balance for analysis of complex metabolic ­networks. Biophys J 83(1):79–86PubMedCrossRefGoogle Scholar
  14. 14.
    Kummel A, Panke S, Heinemann M (2006) Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7:512PubMedCrossRefGoogle Scholar
  15. 15.
    Famili I, Mahadevan R, Palsson BO (2005) k-Cone analysis: determining all candidate values for kinetic parameters on a network scale. Biophys J 88(3):1616–1625PubMedCrossRefGoogle Scholar
  16. 16.
    Holzhutter HG (2004) The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur J biochem/FEBS 271(14):2905–2922CrossRefGoogle Scholar
  17. 17.
    Jamshidi N, Palsson BO (2010) Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys J 98(2):175–185PubMedCrossRefGoogle Scholar
  18. 18.
    Feist AM, Palsson BO (2010) The biomass objective function. Curr Opin Microbiol 13(3):344–349PubMedCrossRefGoogle Scholar
  19. 19.
    Schellenberger J, Lewis NE, Palsson BO (2011) Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys J 100(3):544–553PubMedCrossRefGoogle Scholar
  20. 20.
    Papin JA, Reed JL, Palsson BO (2004) Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem Sci 29(12):641–647PubMedCrossRefGoogle Scholar
  21. 21.
    Price ND, Reed JL, Palsson BO (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2(11):886–897PubMedCrossRefGoogle Scholar
  22. 22.
    Schellenberger J, Palsson BO (2009) Use of randomized sampling for analysis of metabolic networks. J Biol Chem 284(9):5457–5461PubMedCrossRefGoogle Scholar
  23. 23.
    Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci USA 107(41):17845–17850PubMedCrossRefGoogle Scholar
  24. 24.
    Heinrich H, Schuster S (1996) The regulation of cellular systems. Springer, BerlinCrossRefGoogle Scholar
  25. 25.
    Jamshidi N, Palsson BO (2006) Systems biology of SNPs. Mol Syst Biol 2:38PubMedCrossRefGoogle Scholar
  26. 26.
    Burgard AP, Nikolaev EV, Schilling CH, Maranas CD (2004) Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 14(2):301–312PubMedCrossRefGoogle Scholar
  27. 27.
    Mdluli K, Spigelman M (2006) Novel targets for tuberculosis drug discovery. Curr Opin Pharmacol 6(5):459–467PubMedCrossRefGoogle Scholar
  28. 28.
    Kasper DL, Braunwald E, Fauci AS, Hauser SL, Longo DL, Jameson JL (2005) Harrison’s principles of internal medicine, 16th edition, New York, McGraw-HillGoogle Scholar
  29. 29.
    Youmans AS, Youmans GP (1968) Ribonucleic acid, deoxyribonucleic acid, and protein content of cells of different ages of Mycobacterium tuberculosis and the relationship to immunogenicity. J Bacteriol 95(2):272–279PubMedGoogle Scholar
  30. 30.
    Becker SA, Palsson BO (2008) Context-specific metabolic networks are consistent with ­experiments. PLoS Comput Biol 4(5):e1000082PubMedCrossRefGoogle Scholar
  31. 31.
    Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6:401PubMedCrossRefGoogle Scholar
  32. 32.
    Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R, Konig R, Palsson BO (2010) Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat Biotechnol 28(12):1279–1285PubMedCrossRefGoogle Scholar
  33. 33.
    de Souza GA, Wiker HG (2011) A proteomic view of mycobacteria. Proteomics 11(15):3118–3127PubMedCrossRefGoogle Scholar
  34. 34.
    Ehebauer MT, Wilmanns M (2011) The progress made in determining the Mycobacterium tuberculosis structural proteome. Proteomics 11(15):3128–3133PubMedCrossRefGoogle Scholar
  35. 35.
    Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, Bui OT, Knight EM, Fong SS, Palsson BO (2006) Systems approach to refining genome annotation. Proc Natl Acad Sci USA 103(46):17480–17484PubMedCrossRefGoogle Scholar
  36. 36.
    Satish Kumar V, Dasika MS, Maranas CD (2007) Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8:212PubMedCrossRefGoogle Scholar
  37. 37.
    Bordbar A, Lewis NE, Schellenberger J, Palsson BO, Jamshidi N (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6:422PubMedCrossRefGoogle Scholar
  38. 38.
    Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BO (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 104(6):1777–1782PubMedCrossRefGoogle Scholar
  39. 39.
    Kazeros A, Harvey BG, Carolan BJ, Vanni H, Krause A, Crystal RG (2008) Overexpression of apoptotic cell removal receptor MERTK in alveolar macrophages of cigarette smokers. Am J Respir Cell Mol Biol 39(6):747–757PubMedCrossRefGoogle Scholar
  40. 40.
    Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, Rashmi BP, Shanker K, Padma N, Niranjan V, Harsha HC, Talreja N, Vrushabendra BM, Ramya MA, Yatish AJ, Joy M, Shivashankar HN, Kavitha MP, Menezes M, Choudhury DR, Ghosh N, Saravana R, Chandran S, Mohan S, Jonnalagadda CK, Prasad CK, Kumar-Sinha C, Deshpande KS, Pandey A (2004) Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res 32(Database issue):D497–D501PubMedCrossRefGoogle Scholar
  41. 41.
    Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S, Wernerus H, Bjorling L, Ponten F (2011) Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 28(12):1248–1250CrossRefGoogle Scholar
  42. 42.
    Honer zu Bentrup K, Russell DG (2001) Mycobacterial persistence: adaptation to a changing environment. Trends Microbiol 9(12):597–605PubMedCrossRefGoogle Scholar
  43. 43.
    McKinney JD, Honer zu Bentrup K, Munoz-Elias EJ, Miczak A, Chen B, Chan WT, Swenson D, Sacchettini JC, Jacobs WR Jr, Russell DG (2000) Persistence of Mycobacterium ­tuberculosis in macrophages and mice requires the glyoxylate shunt enzyme isocitrate lyase. Nature 406(6797):735–738PubMedCrossRefGoogle Scholar
  44. 44.
    Schnappinger D, Ehrt S, Voskuil MI, Liu Y, Mangan JA, Monahan IM, Dolganov G, Efron B, Butcher PD, Nathan C, Schoolnik GK (2003) Transcriptional adaptation of Mycobacterium tuberculosis within macrophages: insights into the phagosomal environment. J Exp Med 198(5):693–704PubMedCrossRefGoogle Scholar
  45. 45.
    Sassetti CM, Boyd DH, Rubin EJ (2003) Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol 48(1):77–84PubMedCrossRefGoogle Scholar
  46. 46.
    Sassetti CM, Rubin EJ (2003) Genetic requirements for mycobacterial survival during infection. Proc Natl Acad Sci USA 100(22):12989–12994PubMedCrossRefGoogle Scholar
  47. 47.
    Thuong NT, Dunstan SJ, Chau TT, Thorsson V, Simmons CP, Quyen NT, Thwaites GE, Thi Ngoc Lan N, Hibberd M, Teo YY, Seielstad M, Aderem A, Farrar JJ, Hawn TR (2008) Identification of tuberculosis susceptibility genes with human macrophage gene expression profiles. PLoS Pathog 4(12):e1000229PubMedCrossRefGoogle Scholar
  48. 48.
    Hirayama Y, Yoshimura M, Ozeki Y, Sugawara I, Udagawa T, Mizuno S, Itano N, Kimata K, Tamaru A, Ogura H, Kobayashi K, Matsumoto S (2009) Mycobacteria exploit host hyaluronan for efficient extracellular replication. PLoS Pathog 5(10):e1000643PubMedCrossRefGoogle Scholar
  49. 49.
    Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320PubMedCrossRefGoogle Scholar
  50. 50.
    Beste DJ, Espasa M, Bonde B, Kierzek AM, Stewart GR, McFadden J (2009) The genetic requirements for fast and slow growth in mycobacteria. PLoS One 4(4):e5349PubMedCrossRefGoogle Scholar
  51. 51.
    Bonde BK, Beste DJ, Laing E, Kierzek AM, McFadden J (2011) Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis. PLoS Comput Biol 7(6):e1002060PubMedCrossRefGoogle Scholar
  52. 52.
    Ip K, Colijn C, Lun DS (2011) Analysis of complex metabolic behavior through pathway decomposition. BMC Syst Biol 5:91PubMedCrossRefGoogle Scholar
  53. 53.
    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 Biotechnol 17(2):53–60PubMedCrossRefGoogle Scholar
  54. 54.
    Beste DJ, Bonde B, Hawkins N, Ward JL, Beale MH, Noack S, Noh K, Kruger NJ, Ratcliffe RG, McFadden J (2011) 13C metabolic flux analysis identifies an unusual route for pyruvate dissimilation in mycobacteria which requires isocitrate lyase and carbon dioxide fixation. PLoS Pathog 7(7):e1002091PubMedCrossRefGoogle Scholar
  55. 55.
    Raman K, Yeturu K, Chandra N (2008) targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2:109PubMedCrossRefGoogle Scholar
  56. 56.
    Raman K, Vashisht R, Chandra N (2009) Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis. Mol Biosyst 5(12):1740–1751PubMedCrossRefGoogle Scholar
  57. 57.
    Kim TY, Kim HU, Lee SY (2010) Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks. Metab Eng 12(2):105–111PubMedCrossRefGoogle Scholar
  58. 58.
    Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 5(8):e1000489PubMedCrossRefGoogle Scholar
  59. 59.
    Fang X, Wallqvist A, Reifman J (2010) Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis. BMC Syst Biol 4:160PubMedCrossRefGoogle Scholar
  60. 60.
    Fang X, Wallqvist A, Reifman J (2009) A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis. BMC Syst Biol 3:92PubMedCrossRefGoogle Scholar
  61. 61.
    Driscoll MD, McLean KJ, Levy C, Mast N, Pikuleva IA, Lafite P, Rigby SE, Leys D, Munro AW (2010) Structural and biochemical characterization of Mycobacterium tuberculosis CYP142: evidence for multiple cholesterol 27-hydroxylase activities in a human pathogen. J Biol Chem 285(49):38270–38282PubMedCrossRefGoogle Scholar
  62. 62.
    Elamin AA, Stehr M, Spallek R, Rohde M, Singh M (2011) The Mycobacterium tuberculosis Ag85A is a novel diacylglycerol acyltransferase involved in lipid body formation. Mol Microbiol 81(6):1577–1592PubMedCrossRefGoogle Scholar
  63. 63.
    Hatzios SK, Bertozzi CR (2011) The regulation of sulfur metabolism in Mycobacterium tuberculosis. PLoS Pathog 7(7):e1002036PubMedCrossRefGoogle Scholar
  64. 64.
    Ouellet H, Guan S, Johnston JB, Chow ED, Kells PM, Burlingame AL, Cox JS, Podust LM, de Montellano PR (2010) Mycobacterium tuberculosis CYP125A1, a steroid C27 monooxygenase that detoxifies intracellularly generated cholest-4-en-3-one. Mol Microbiol 77(3):730–742PubMedCrossRefGoogle Scholar
  65. 65.
    Li F, Thiele I, Jamshidi N, Palsson BO (2009) Identification of potential pathway mediation targets in Toll-like receptor signaling. PLoS Comput Biol 5(2):e1000292PubMedCrossRefGoogle Scholar
  66. 66.
    Thiele I, Jamshidi N, Fleming RM, Palsson BO (2009) Genome-scale reconstruction of Escherichia coli’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput Biol 5(3):e1000312PubMedCrossRefGoogle Scholar
  67. 67.
    Thiele I, Palsson BO (2010) Reconstruction annotation jamborees: a community approach to systems biology. Mol Syst Biol 6:361PubMedCrossRefGoogle Scholar
  68. 68.
    The Institute for Genomic Research.
  69. 69.
    Porcelli AM, Ghelli A, Zanna C, Pinton P, Rizzuto R, Rugolo M (2005) pH difference across the outer mitochondrial membrane measured with a green fluorescent protein mutant. Biochem Biophys Res Commun 326(4):799–804PubMedCrossRefGoogle Scholar
  70. 70.
    Navarro A (2004) Mitochondrial enzyme activities as biochemical markers of aging. Mol Aspects Med 25(1–2):37–48PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Neema Jamshidi
    • 1
  • Aarash Bordbar
    • 1
  • Bernhard Palsson
    • 1
  1. 1.Department of BioengineeringUniversity of California, San DiegoLa JollaUSA

Personalised recommendations