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Bringing Genomes to Life: The Use of Genome-Scale In Silico Models

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Introduction to Systems Biology

Abstract

Metabolic network reconstruction has become an established procedure that allows the integration of different data types and provides a framework to analyze and map high-throughput data, such as gene expression, metabolomics,and fluxomics data. In this chapter, we discuss how to reconstruct a metabolic network starting from a genome annotation. Further experimental data, such as biochemical and physiological data, are incorporated into the reconstruction, leading to a comprehensive, accurate representation of the reconstructed organism, cell, or organelle. Furthermore, we introduce the philosophy of constraint-based modeling, which can be used to investigate network properties and metabolic capabilities of the reconstructed system. Finally, we present two recent studies that combine in silico analysis of an Eschirichia coli metabolic reconstruction with experimental data. While the first study leads to novel insight into E. coli’s metabolic and regulatory networks, the second presents a computational approach to metabolic engineering.

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References

  1. Janssen P, Goldovsky L, Kunin V, et al. Genome coverage, literally speaking. The challenge of annotating 200 genomes with 4 million publications. EMBO Rep 2005;6(5):397–399.

    Article  PubMed  CAS  Google Scholar 

  2. Dansen TB, Wirtz KW, Wanders RJ, Pap EH. Peroxisomes in human fibroblasts have a basic pH. Nat Cell Biol 2000;2(1):51–53.

    Article  PubMed  CAS  Google Scholar 

  3. Nicolay K, Veenhuis M, Douma AC, et al. A 31P NMR study of the internal pH of yeast peroxisomes. Arch Microbiol 1987;147(1):37–41.

    Article  PubMed  CAS  Google Scholar 

  4. Karp PD, Riley M, Saier M, et al. The EcoCyc and MetaCyc databases. Nucleic Acids Res 2000;28(1):56–59.

    Article  PubMed  CAS  Google Scholar 

  5. (NC-IUBMB) NCotIUoBaMB. Enzyme Nomenclature. 6th ed. San Diego: Academic Press; 1992.

    Google Scholar 

  6. Palsson BO. Systems Biology: Properties of Reconstructed Networks. New York: Cambridge University Press; 2006.

    Google Scholar 

  7. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004;2(11):886–897.

    Article  PubMed  CAS  Google Scholar 

  8. Reed JL, Palsson BO. Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res 2004;14(9):1797–1805.

    Article  PubMed  CAS  Google Scholar 

  9. Famili I, Mahadevan R, Palsson BO. k-Cone analysis: determining all candidate values for kinetic parameters on a network scale. Biophys J 2005;88(3):1616–1625.

    Article  PubMed  CAS  Google Scholar 

  10. Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO. Integrating high-throughput and computational data elucidates bacterial networks. Nature 2004;429(6987):92–96.

    Article  PubMed  CAS  Google Scholar 

  11. Reed JL, Vo TD, Schilling CH, et al. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 2003;4(9):R54.1–R.12.

    Article  Google Scholar 

  12. Fong SS, Burgard AP, Herring CD, et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng 2005;91(5):643–648.

    Article  PubMed  CAS  Google Scholar 

  13. Thanbichler M, Viollier PH, Shapiro L. The structure and function of the bacterial chromosome. Curr Opin Genet Dev 2005;15(2):153–162.

    Article  PubMed  CAS  Google Scholar 

  14. Flores N, Flores S, Escalante A, et al. Adaptation for fast growth on glucose by differential expression of central carbon metabolism and gal regulon genes in an Escherichia coli strain lacking the phosphoenolpyruvate:carbohydrate phosphotransferase system. Metab Eng 2005;7(2):70–87.

    Article  PubMed  CAS  Google Scholar 

  15. Notley-McRobb L, Ferenci T. Adaptive mgl-regulatory mutations and genetic diversity evolving in glucose-limited Escherichia coli populations. Environ Microbiol 1999;1(1):33–43.

    Article  PubMed  CAS  Google Scholar 

  16. Raghunathan A, Palsson B. Scalable method to determine mutations that occur during adaptive evolution of Escherichia coli. Biotechnol Lett 2003;25:435–441.

    Article  PubMed  CAS  Google Scholar 

  17. Reed JL, Palsson BO. Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 2003;185(9):2692–2699.

    Article  PubMed  CAS  Google Scholar 

  18. Reed JL, Famili I, Thiele I, Palsson BO. Towards multidimensional genome annotation. Nat Rev Genet 2006;7(2):130–141.

    Article  PubMed  CAS  Google Scholar 

  19. Park SM, Schilling CH, Palsson BO. Compositions and methods for modeling Bacillus subtilis metabolism. USA. 2003. Available at http://www.freepatentsonline.com/20030224363.html.

    Google Scholar 

  20. Reed JL, Vo TD, Schilling CH, et al. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 2003;4(9):R54.

    Article  PubMed  Google Scholar 

  21. Edwards JS, Palsson BO. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci USA 2000;97(10):5528–5533.

    Article  PubMed  CAS  Google Scholar 

  22. Mahadevan R, Bond DR, Butler JE, et al. Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl Environ Microbiol 2006;72(2):1558–1568.

    Article  PubMed  CAS  Google Scholar 

  23. Schilling CH, Palsson BO. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J Theor Biol 2000;203(3):249–283.

    Article  PubMed  CAS  Google Scholar 

  24. Edwards JS, Palsson BO. Systems properties of the Haemophilus influenzae Rd metabolic genotype. J Biol Chem 1999;274(25):17410–17416.

    Article  PubMed  CAS  Google Scholar 

  25. Thiele I, Vo TD, Price ND, et al. An expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): An in silico genome-scale characterization of single and double deletion mutants. J Bacteriol 2005;187:5818–5830.

    Article  PubMed  CAS  Google Scholar 

  26. Schilling CH, Covert MW, Famili I, Church GM, Edwards JS, Palsson BO. Genome-scale metabolic model of Helicobacter pylori 26695. J Bacteriol 2002;184(16):4582–4593.

    Article  PubMed  CAS  Google Scholar 

  27. Oliveira AP, Nielsen J, Forster J. Modeling Lactococcus lactis using a genomescale flux model. BMC Microbiol 2005;5(1):39.

    Article  PubMed  Google Scholar 

  28. Hong SH, Kim JS, Lee SY, et al. The genome sequence of the capnophilic rumen bacterium Mannheimia succiniciproducens. Nat Biotechnol 2004;22(10):1275–1281.

    Article  PubMed  CAS  Google Scholar 

  29. Becker SA, Palsson BO. Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol 2005;5(1):8.

    Article  PubMed  Google Scholar 

  30. Borodina I, Krabben P, Nielsen J. Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism. Genome Res 2005;15(6):820–829.

    Article  PubMed  CAS  Google Scholar 

  31. Feist AM, Scholten JCM, Palsson BO, et al. Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri. 2006;2(1):msb4100046-E1-msb-E14.

    Google Scholar 

  32. Sheikh K, Forster J, Nielsen LK. Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus. Biotechnol Prog 2005;21(1):112–121.

    Article  PubMed  CAS  Google Scholar 

  33. Duarte NC, Herrgard MJ, Palsson BO. Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genomescale metabolic model. Genome Res 2004;14(7):1298–1309.

    Article  PubMed  CAS  Google Scholar 

  34. Forster J, Famili I, Fu P, et al. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 2003;13(2):244–253.

    Article  PubMed  CAS  Google Scholar 

  35. Majewski RA, Domach MM. Simple constrained optimization view of acetate overflow in E. coli. Biotechnol Bioeng 1990;35:732–738.

    Article  CAS  Google Scholar 

  36. Lee S, Phalakornkule C, Domach MM, et al. Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comp Chem Eng 2000;24:711–716.

    Article  CAS  Google Scholar 

  37. Vo TD, Greenberg HJ, Palsson BO. Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. J Biol Chem 2004;279(38):39532–39540.

    Article  PubMed  CAS  Google Scholar 

  38. Burgard AP, Pharkya P, Maranas CD. OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 2003;84(6):647–657.

    Article  PubMed  CAS  Google Scholar 

  39. Thiele I, Price ND, Vo TD, Palsson BO. Candidate metabolic network states in human mitochondria: Impact of diabetes, ischemia, and diet. J Biol Chem 2005;280(12):11683–11695.

    Article  PubMed  CAS  Google Scholar 

  40. Price ND, Thiele I, Palsson BO. Candidate states of Helicobacter pylori’s genome-scale metabolic network upon application of “loop law” thermodynamic constraints. Biophys J 2006;90(11):3919–3928

    Article  PubMed  CAS  Google Scholar 

  41. Price ND, Schellenberger J, Palsson BO. Uniform sampling of steady state flux spaces: means to design experiments and to interpret enzymopathies. Biophysl J 2004;87(4):2172–2186.

    Article  CAS  Google Scholar 

  42. Wiback SJ, Famili I, Greenberg HJ, et al. Monte Carlo sampling can be used to determine the size and shape of the steady state flux space. J Theor Biol 2004;228(4):437–447.

    Article  PubMed  Google Scholar 

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Thiele, I., Palsson, B.Ø. (2007). Bringing Genomes to Life: The Use of Genome-Scale In Silico Models. In: Choi, S. (eds) Introduction to Systems Biology. Humana Press. https://doi.org/10.1007/978-1-59745-531-2_2

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