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EcoSal Plus

Domain 3:

Metabolism

Introduction and Perspectives

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  • Author: Uwe Sauer1
  • Editor: Valley Stewart
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
  • Received 12 February 2007 Accepted 11 April 2007 Published 14 August 2007
  • Address correspondence to Uwe Sauer sauer@imsb.biol.ethz.ch
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  • Abstract:

    Classically, metabolism was investigated by studying molecular characteristics of enzymes and their regulators in isolation. This reductionistic approach successfully established mechanistic relationships with the immediate interacting neighbors and allowed reconstruction of network structures. Severely underdeveloped was the ability to make precise predictions about the integrated operation of pathways and networks that emerged from the typically nonlinear and complex interactions of proteins and metabolites. The burden of metabolic engineering is a consequence of this fact—one cannot yet predict with any certainty precisely what needs to be engineered to produce more complex phenotypes. What was and still is missing are concepts, methods, and algorithms to integrate data and information into a quantitatively coherent whole, as well as theoretical concepts to reliably predict the consequence of environmental stimuli or genetic interventions. This introduction and perspective to Domain 3, Metabolism and Metabolic Fluxes, starts with a brief overview of the panoply of global measurement technologies that herald the dawning of systems biology and whose impact on metabolic research is apparent throughout the Domain 3. In the middle section, applications to are used to illustrate general concepts and successes of computational methods that approach metabolism as a network of interacting elements, and thus have potential to fill the gap in quantitative data and information integration. The final section highlights prospective focus areas for future metabolic research, including functional genomics, eludication of evolutionary principles, and the integration of metabolism with regulatory networks.

  • Citation: Sauer U. 2007. Introduction and Perspectives, EcoSal Plus 2007; doi:10.1128/ecosal.3.1.1

Key Concept Ranking

Gene Expression and Regulation
0.41986033
Escherichia coli
0.33223078
Protein Transport
0.301407
0.41986033

References

1. Michal H. 2006. Roche Applied Science “Biochemical Pathways.” ExPASy. [Online.] http://www.expasy.ch/cgi-bin/search-biochem-index. [CrossRef]
2. Fraenkel DG. 1992. Genetics and intermediary metabolism. Annu Rev Genet 26:159–177.[PubMed] [CrossRef]
3. Bailey JE. 1991. Toward a science of metabolic engineering. Science 252:1668–1675.[PubMed] [CrossRef]
4. Nakamura CE, Whited GM. 2003. Metabolic engineering for the microbial production of 1,3-propanediol. Curr Opin Biotechnol 14:454–459.[PubMed] [CrossRef]
5. Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, Datsenko KA, Tomita M, Wanner BL, Mori H. 2006. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol Article no. 2006.0008. [Online.] doi:10.1038/msb4100050. http://www.nature.com/msb/journal/v2/n1/full/msb4100050.html. [CrossRef]
6. Kitagawa M, Ara T, Arifuzzaman M, Ioka-Nakamichi T, Inamoto E, Toyonaga H, Mori H. 2005. Complete set of ORF clones of Escherichia coli ASKA library (A Complete Set of E. coli K-12 ORF Archive): unique resources for biological research. DNA Res 12:291–299.[PubMed] [CrossRef]
7. Zaslaver A, Bren A, Ronen M, Itzkovitz S, Kikoin I, Shavit S, Liebermeister W, Surette MG, Alon U. 2006. A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat Methods 3:623–628.[PubMed] [CrossRef]
8. Joyce AR, Reed JL, White A, Edwards R, Osterman A, Baba T, Mori H, Lesely SA, Palsson BO, Agarwalla S. 2006. Experimental and computational assessment of conditionally essential genes in Escherichia coli. J Bacteriol 188:8259–8271.[PubMed] [CrossRef]
9. Zaslaver A, Mayo AE, Rosenberg R, Bashkin P, Sberro H, Tsalyuk M, Surette MG, Alon U. 2004. Just-in-time transcription program in metabolic pathways. Nat Genet 36:486–491.[PubMed] [CrossRef]
10. Barrett CL, Herring CD, Reed JL, Palsson BO. 2005. The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states. Proc Natl Acad Sci USA 102:19103–19108.[PubMed] [CrossRef]
11. Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP. 2003. Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci USA 100:15522–15527.[PubMed] [CrossRef]
12. Bertone P, Gerstein M, Snyder M. 2005. Applications of DNA tiling arrays to experimental genome annotation and regulatory pathway discovery. Chromosome Res 13:259–274.[PubMed] [CrossRef]
13. Bulyk ML. 2006. DNA microarray technologies for measuring protein-DNA interactions. Curr Opin Biotechnol 17:422–430.[PubMed] [CrossRef]
14. Herring CD, Raffaelle M, Allen TE, Kanin EI, Landick R, Ansari AZ, Palsson BO. 2005. Immobilization of Escherichia coli RNA polymerase and location of binding sites by use of chromatin immunoprecipitation and microarrays. J Bacteriol 187:6166–6174.[PubMed] [CrossRef]
15. Han M-J, Lee SY. 2006. The Escherichia coli proteome: past, present, and future prospects. Microbiol Mol Biol Rev 70:362–439.[PubMed] [CrossRef]
16. Kuster B, Schirle M, Mallick P, Aebersold R. 2005. Scoring proteomes with proteotypic peptide probes. Nat Rev Mol Cell Biol 6:577–583.[PubMed] [CrossRef]
17. Price ND, Reed JL, Palsson BO. 2004. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature Rev Microbiol 2:886–897. [CrossRef]
18. Arifuzzaman M, Maeda M, Itoh A, Nishikata K, Takita C, Saito R, Ara T, Nakahigashi K, Huang HC, Hirai A, Tsuzuki K, Nakamura S, Altaf-Ul-Amin M, Oshima T, Baba T, Yamamoto N, Kawamura T, Ioka-Nakamichi T, Kitagawa M, Tomita M, Kanaya S, Wada C, Mori H. 2006. Large-scale identification of protein-protein interaction of Escherichia coli K-12. Genome Res 16:686–691.[PubMed] [CrossRef]
19. Riley M, Serres MH. 2000. Interim report on genomics of Escherichia coli. Annu Rev Microbiol 54:341–411.[PubMed] [CrossRef]
20. Nielsen J, Oliver SG. 2005. The next wave in metabolome analysis. Trends Biotechnol 23:544–546.[PubMed] [CrossRef]
21. Hollywood K, Brison DR, Goodacre R. 2006. Metabolomics: current technologies and future trends. Proteomics 6:4716–4723.[PubMed] [CrossRef]
22. Kümmel A, Panke S, Heinemann M. 2006. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol Article no. 2006.0034. [Online.] http://www.nature.com/msb/journal/v2/n1/full/msb4100074.html. [CrossRef]
23. Sauer U. 2006. Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2:62. doi: 10.1038/msb4100109. [Online.] http://www.pubmedcentral.nih.gov. [CrossRef]
24. Wiechert W. 2001. 13C metabolic flux analysis. Metab Eng 3:195–206.[PubMed] [CrossRef]
25. Walsh K, Koshland DE, Jr. 1984. Determination of flux through the branch point of two metabolic cycles. J Biol Chem 259:9646–9654.[PubMed] [CrossRef]
26. Sanford K, Soucaille P, Whited G, Chotani G. 2002. Genomics to fluxomics and physiomics—pathway engineering. Curr Opin Microbiol 5:318–322.[PubMed] [CrossRef]
27. Sauer U, Eikmanns B. 2005. C3-carboxylation and C4-decarboxylation reactions: the anaplerotic node as a switchpoint for C-flux distribution. FEMS Microbiol Rev 29:765–794.[PubMed] [CrossRef]
28. Fischer E, Sauer U. 2003. A novel metabolic cycle catalyzes glucose oxidation and anaplerosis in hungry Escherichia coli. J Biol Chem 278:46446–46451.[PubMed] [CrossRef]
29. Sauer U, Canonaco F, Heri S, Perrenoud A, Fischer E. 2004. The soluble and membrane-bound transhydrogenases UdhA and PntAB have divergent functions in NADPH metabolism of Escherichia coli. J Biol Chem 279:6613–6619.[PubMed] [CrossRef]
30. Kitano H. 2002. Computational systems biology. Nature 420:206–210.[PubMed] [CrossRef]
31. Stelling J, Sauer U, Szallasi Z, Doyle FJ, III, Doyle J. 2004. Robustness of cellular functions. Cell 118:675–685. [PubMed] [CrossRef]
32. Stelling J. 2004. Mathematical models in microbial systems biology. Curr Opin Microbiol 7:513–518.[PubMed] [CrossRef]
33. Alon U. 2006. An Introduction to Systems Biology: Design Principles of Biological Circuits, vol. 10. CRC Press, London, United Kingdom. [CrossRef]
34. 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. [CrossRef]
35. Neidhardt FC, Ingraham JL, Schaechter M. 1990. Physiology of the Bacterial Cell: a Molecular Approach. Sinauer Associates, Inc., Sunderland, MA. [CrossRef]
36. Keseler IM, Collado-Vides J, Gama-Castro S, Ingraham J, Paley S, Paulsen IT, Peralta-Gil M, Karp PD. 2005. EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res 33:D334–D337.[PubMed] [CrossRef]
37. Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED. 2002. Metabolic network structure determines key aspects of functionality and regulation. Nature 420:190–193.[PubMed] [CrossRef]
38. Ibarra RU, Edwards JS, Palsson BO. 2002. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420:186–189.[PubMed] [CrossRef]
39. Fell DA. 1997. Understanding the Control of Metabolism. Portland Press, London, United Kingdom. [CrossRef]
40. Kao KC, Tran LM, Liao JC. 2005. A global regulatory role of gluconeogenic genes in Escherichia coli revealed by transcriptome network analysis. J Biol Chem 280:36079–36087.[PubMed] [CrossRef]
41. Çakir T, Patil KR, Önsan ZI, Ülgen KO, Kırdar B, Nielsen J. 2006. Integration of metabolome data with metabolic networks reveals reporter reactions. Mol Syst Biol Article no. 2:50. [Online.] doi:10.1038/msb4100085. http://www.nature.com/msb/journal/v2/n1/full/msb4100085.html. [CrossRef]
42. Kharchenko P, Chen L, Freund Y, Vitkup D, Church GM. 2006. Identifying metabolic enzymes with multiple types of association evidence. BMC Bioinformatics 7:177. [Online.] http://www.biomedcentral.com/1471-2105/7/177. [CrossRef]
43. Kuznetsova E, Proudfoot M, Sanders SA, Reinking J, Savchenko A, Arrowsmith CH, Edwards AM, Yakunin AF. 2005. Enzyme genomics: application of general enzymatic screens to discover new enzymes. FEMS Microbiol Rev 29:263–279.[PubMed] [CrossRef]
44. 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 103:17480–17484.[PubMed] [CrossRef]
45. Fischer E, Sauer U. 2005. Large-scale in vivo flux analysis shows rigidity and sub-optimal performance of Bacillus subtilis metabolism. Nat Genet 37:636–640.[PubMed] [CrossRef]
46. Elena SF, Lenski RE. 2003. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet 4:457–469.[PubMed] [CrossRef]
47. Fong SS, Palsson BO. 2004. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat Genet 36:1056–1058.[PubMed] [CrossRef]
48. Babu MM, Aravind L. 2006. Adaptive evolution by optimizing expression levels in different environments. Trends Microbiol 14:11–14.[PubMed] [CrossRef]
49. Fong SS, Nanchen A, Palsson BO, Sauer U. 2006. Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes. J Biol Chem 281:8024–8033.[PubMed] [CrossRef]
50. Herring CD, Raghunathan A, Honisch C, Patel T, Applebee MK, Joyce AR, Albert TJ, Blattner FR, van den Boom D, Cantor CR, Palsson BO. 2006. Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nat Genet 38:1406–1412.[PubMed] [CrossRef]
51. Vitkup D, Kharchenko P, Wagner A. 2006. Influence of metabolic network structure and function on enzyme evolution. Genome Biol 7:R39. doi:10.1186/gb-2006-7-5-r39. [Online.] http://genomebiology.com. [CrossRef]
52. Maharjan R, Seeto S, Notley-McRobb L, Ferenci T. 2006. Clonal radiation in a constant environment. Science 313:514–517.[PubMed] [CrossRef]
53. Elowitz MB, Levine AJ, Siggia ED, Swain PS. 2002. Stochastic gene expression in a single cell. Science 297:1183–1186.[PubMed] [CrossRef]
54. Dubnau D, Losick R. 2006. Bistability in bacteria. Mol Microbiol 61:564–572.[PubMed] [CrossRef]
55. Salgado H, Santos-Zavaleta A, Gama-Castro S, Peralta-Gil M, Penaloza-Spinola MI, Martinez-Antonio A, Karp PD, Collado-Vides J. 2006. The comprehensive updated regulatory network of Escherichia coli K-12. BMC Bioinformatics 7:5. doi: 10.1186/1471-2105-7-5. [Online.] http://www.biomedcentral.com. [CrossRef]
56. Rossell S, van der Weijden CC, Lindenbergh A, van Tuijl A, Francke C, Bakker BM, Westerhoff HV. 2006. Unraveling the complexity of flux regulation: a new method demonstrated for nutrient starvation in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 103:2166–2171.[PubMed] [CrossRef]
57. Caldara M, Charlier D, Cunin R. 2006. The arginine regulon of Escherichia coli: whole-system transcriptome analysis discovers new genes and provides an integrated view of arginine regulation. Microbiology 152:3343–3354.[PubMed] [CrossRef]
58. Koebmann BJ, Westerhoff HV, Snoep JL, Nilsson D, Jensen PR. 2002. The glycolytic flux in Escherichia coli is controlled by the intracellular demand for ATP. J Bacteriol 184:3909–3916.[PubMed] [CrossRef]
59. Fung E, Wong WW, Suen JK, Bulter T, Lee SG, Liao JC. 2005. A synthetic gene-metabolic oscillator. Nature 435:118–122.[PubMed] [CrossRef]
60. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball C, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim I, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M. 2001. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat Genet 29:365–371.[PubMed] [CrossRef]
61. Le Novere N, Finney A, Hucka M, Bhalla US, Campagne F, Collado-Vides J, Crampin EJ, Halstead M, Klipp E, Mendes P, Nielsen P, Sauro H, Shapiro B, Snoep JL, Spence HD, Wanner BL. 2005. Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23:1509–1515.[PubMed] [CrossRef]
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/content/journal/ecosalplus/10.1128/ecosal.3.1.1
2007-08-14
2017-10-20

Abstract:

Classically, metabolism was investigated by studying molecular characteristics of enzymes and their regulators in isolation. This reductionistic approach successfully established mechanistic relationships with the immediate interacting neighbors and allowed reconstruction of network structures. Severely underdeveloped was the ability to make precise predictions about the integrated operation of pathways and networks that emerged from the typically nonlinear and complex interactions of proteins and metabolites. The burden of metabolic engineering is a consequence of this fact—one cannot yet predict with any certainty precisely what needs to be engineered to produce more complex phenotypes. What was and still is missing are concepts, methods, and algorithms to integrate data and information into a quantitatively coherent whole, as well as theoretical concepts to reliably predict the consequence of environmental stimuli or genetic interventions. This introduction and perspective to Domain 3, Metabolism and Metabolic Fluxes, starts with a brief overview of the panoply of global measurement technologies that herald the dawning of systems biology and whose impact on metabolic research is apparent throughout the Domain 3. In the middle section, applications to are used to illustrate general concepts and successes of computational methods that approach metabolism as a network of interacting elements, and thus have potential to fill the gap in quantitative data and information integration. The final section highlights prospective focus areas for future metabolic research, including functional genomics, eludication of evolutionary principles, and the integration of metabolism with regulatory networks.

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Figure 1

Key measurable quantities are indicated for each level.

Citation: Sauer U. 2007. Introduction and Perspectives, EcoSal Plus 2007; doi:10.1128/ecosal.3.1.1
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Image of Figure 2
Figure 2

For each level, the information required for the less-detailed models is also mandatory.

Citation: Sauer U. 2007. Introduction and Perspectives, EcoSal Plus 2007; doi:10.1128/ecosal.3.1.1
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