Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks

Bioinformatics

Volumn 28, Issue 18, 2012, Pages

  Larhlimi, Abdelhalim ^a,b   Basler, Georg ^b   Grimbs, Sergio ^a   Selbig, Joachim ^a,b   Nikoloski, Zoran ^b  

^a UNIVERSITY OF POTSDAM   (Germany)

^b MAX PLANCK INSTITUTE OF MOLECULAR PLANT PHYSIOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BACTERIUM; BIOLOGICAL MODEL; CITRIC ACID CYCLE; GLUCONEOGENESIS; GLYCOLYSIS; HUMAN; METABOLISM;

BACTERIA; CITRIC ACID CYCLE; GLUCONEOGENESIS; GLYCOLYSIS; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL;

EID: 84866447349     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts381     Document Type: Article

References (45)

1. Basler,G. et al. (2012) Optimizing metabolic pathways by screening for feasible synthetic reactions. Biosystems, 109, 186-191.
2. Becker,S.A. and Palsson,B.Ø. (2005) Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol., 5, 8.
3. Burgard,A.P. et al. (2003) OptKnock:Abilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng., 84, 647-657.
4. Burgard,A.P. et al. (2004) Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res., 14, 301-312.
5. de Figueiredo,L.F. et al. (2009) Can sugars be produced from fatty acids? A test case for pathway analysis tools. Bioinformatics, 25, 152-158.
6. Dobson,C.M. (2004) Chemical space and biology. Nature, 432, 824-828.
7. Duarte,N.C. et al. (2004) Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res., 14, 1298-1309.
8. Edwards,J.S. and Palsson,B.Ø. (2000) Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics, 1, 1.
9. Feist,A.M. et al. (2006) Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri. Mol. Syst. Biol., 2, 2006.0004.
10. Feist,A.M. 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.
11. Feist,A.M. et al. (2009) Reconstruction of biochemical networks in microorganisms. Nat. Rev. Microbiol., 7, 129-143.
12. Grafahrend-Belau,E. et al. (2009) Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism. Plant Physiol., 149, 585-598.
13. Hädicke,O. and Klamt,S. (2010) CASOP: a computational approach for strain optimization aiming at high productivity. J. Biotechnol., 147, 88-101.
14. Hanson,A.D. et al. (2010) Unknown proteins and 'orphan' enzymes: the missing half of the engineering parts list-and how to find it. Biochem. J., 425, 1-11.
15. Heinrich,R. and Schuster,S. (1996) The Regulation of Cellular Systems. Chapman and Hall, NY, USA.
16. Holmström,K. (1999) The TOMLAB Optimization Environment in Matlab. Adv. Model. Optim., 1, 47-69.
17. Jamshidi,N. and Palsson,B.Ø. (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst. Biol., 1, 26.
18. Kaleta,C. et al. (2011) In Silico evidence for gluconeogenesis from fatty acids in humans. PLoS Comput. Biol., 7, e1002116.
19. Kanehisa,M. and Goto,S. (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 28, 27-30.
20. Karp,P.D. et al. (2000) The EcoCyc and MetaCyc databases. Nucleic Acids Res., 28, 56-59.
21. Kauffman,K.J. et al. (2004) Advances in flux balance analysis. Curr. Opin. Biotechnol., 14, 491-496.
22. Klamt,S. and Stelling,J. (2003) Two approaches for metabolic pathway analysis? Trends Biotechnol., 21, 64-69.
23. Klamt,S. et al. (2009) Hypergraphs and cellular networks. PLoS Comput. Biol., 5, e1000385.
24. Kumar,A. et al. (2012) MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases. BMC Bioinformatics, 13, 6.
25. Lee,J.M. et al. (2006) Flux balance analysis in the era of metabolomics. Brief. Bioinformatics, 7, 140-150.
26. Lee,J.W. et al. (2011) Systems metabolic engineering for chemicals and materials. Trends Biotechnol., 29, 370-378.
27. Mahadevan,R. and Schilling,C.H. (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng., 5, 264-276.
28. Marashi,S.-A. and Bockmayr,A. (2011) Flux coupling analysis of metabolic networks is sensitive to missing reactions. BioSystems, 103, 57-66.
29. Müller,M.M. and Hausmann,R. (2011) Regulatory and metabolic network of rhamnolipid biosynthesis: traditional and advanced engineering towards biotechnological production. Appl. Microbiol. Biotechnol., 91, 251-264.
30. Oh,Y.-K. et al. (2007) Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J. Biol. Chem., 282, 28791-28799.
31. Orth,J.D. et al. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol., 7, 535.
32. Pharkya,P. and Maranas,C.D. (2006) An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab. Eng., 8, 1-13.
33. Pharkya,P. et al. (2004) OptStrain:Acomputational framework for redesign of microbial production systems. Genome Res., 14, 2367-2376.
34. Price,N.D. et al. (2003) Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol., 21, 162-169.
35. Ranganathan,S. et al. (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol., 6, e1000744.
36. Reed,J.L. et al. (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol., 4, R54.
37. Schilling,C.H. et al. (2000) Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J. Theor. Biol., 203, 229-248.
38. Schuetz,R. et al. (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol., 3, 119.
39. Schuster,S. et al. (2000) Ageneral definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat. Biotechnol., 18, 326-332.
40. Thiele,I. et al. (2005) Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J. Bacteriol., 187, 5818-5830.
41. Todorov,Y.R. et al. (1999) Determination of theoretical capacity of metal ion-doped LiMn2O4 as the positive electrode in Li-ion batteries. J. Power Sources, 77, 198-201.
42. Transportation Research Board. (1997) Highway capacity manual. Technical report, National Research Council, Washington, DC.
43. Varma,A. and Palsson,B.Ø. (1993) Metabolic capabilities of Escherichia coli: I. Synthesis of biosynthetic precursors and cofactors. J. Theor. Biol., 165, 477-502.
44. Varma,A. and Palsson,B.Ø. (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol., 60, 3724-3731.
45. Yang,L. et al. (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab. Eng., 13, 272-281.