Comparative study of computational methods to detect the correlated reaction sets in biochemical networks

Briefings in Bioinformatics

Volumn 12, Issue 2, 2011, Pages 132-150

  Xi, Yanping ^a   Chen, Yi Ping Phoebe ^b   Qian, Chen ^c   Wang, Fei ^a  

^a FUDAN UNIVERSITY   (China)

^b LA TROBE UNIVERSITY   (Australia)

^c NONE

Author keywords

Correlated reaction set; Enzyme subset; Hard coupled reaction set; Linear optimization; Monte Carlo sampling; Network based pathway analysis

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGY; COMPARATIVE STUDY; ESCHERICHIA COLI; METABOLISM; METHODOLOGY; MONTE CARLO METHOD; SIGNAL TRANSDUCTION;

ALGORITHMS; COMPUTATIONAL BIOLOGY; ESCHERICHIA COLI; METABOLIC NETWORKS AND PATHWAYS; MONTE CARLO METHOD; SIGNAL TRANSDUCTION;

ESCHERICHIA COLI;

EID: 79953141472     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbp068     Document Type: Article

References (64)

1. Papin JA, Hunter T, Palsson BO, et al. Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol 2005;6:99-111.
2. Barrett CL, Palsson BO, Segre D. Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach. PLoS Comput Biol 2006;2:e52.
3. Reed JL, Famili I, Thiele I, et al. Towards multidimensional genome annotation. Nat Rev Genet 2006;7:130-141.
4. Goelzer A, Brikci FB, Martin-Verstraete I, et al. Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis. BMCSyst Biol 2008;2:20.
5. Feist AM, Herrgard MJ, Thiele I, et al. Reconstruction of biochemical networks in microorganisms. Nat Rev Micro 2009;7:129-143.
6. Papin JA, Reed JL, Palsson BO. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem Sci 2004;29:641-647.
7. Mo ML, Jamshidi N, Palsson BO. A genome-scale, constraint-based approach to systems biology of human metabolism. Mol Biosyst 2007;3:598-603.
8. Burgard AP, Nikolaev EV, Schilling CH, et al. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 2004;14:301-12.
9. Lee JM, Gianchandani EP, Papin JA. Flux balance analysis in the era of metabolomics. Brief Bioinform 2006;7:140-150.
10. Raman K, Chandra N. Flux balance analysis of biological systems: applications and challenges. Brief Bioinform. doi:10.1093/bib/bbp011 [Epub ahead of print 15 March 2009].
11. Schuster S, Klamt S, Weckwerth W, et al. Use of network analysis of metabolic systems in bioengineering. Bioprocess Biosyst Eng 2002;24:363-372.
12. Papin JA, Price ND, Palsson BO. Extreme pathway lengths and reaction participation in genome-scale metabolic networks. Genome Res 2002;12:1889-1900.
13. Pfeiffer T, Sanchez-Valdenebro I, Nuno JC, et al. METATOOL: for studying metabolic networks. Bioinformatics 1999;15:251-257.
14. Schilling CH, Edwards JS, Letscher D, et al. Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol Bioeng 2000; 71:286-306.
15. 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. JTheor Biol 2004;228:437-447.
16. Price ND, Schellenberger J, Palsson BO. Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J 2004;87:2172-2186.
17. Kamp Av, Schuster S. Metatool 5.0: fast and flexible elementary modes analysis. Bioinformatics 2006;22: 1930-1931.
18. Jamshidi N, Palsson BO. Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMCSyst Biol 2007;1:26.
19. 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;14:1797-1805.
20. Pál C, Papp B, Lercher MJ. Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat Genet 2005;37:1372-1375.
21. Notebaart RA, Teusink B, Siezen RJ, etal. Co-regulation of metabolic genes is better explained by flux coupling than by network distance. PLoSComput Biol 2008;4:e26.
22. Duarte NC, Becker SA, Jamshidi N, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 2007; 104:1777-1782.
23. Jamshidi N, Palsson BO. Systems biology of SNPs. Mol Syst Biol 2006;2:38.
24. Schuster S, Fell DA, Dandekar T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol 2000;18:326-332.
25. Schuster S, Dandekar T, Fell DA. Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 1999;17:53-60.
26. Schuster S, Hilgetag C. On elementary flux modes in biochemical reaction systems at steady state. J Biol Syst 1994;2: 165-182.
27. Klamt S, Stelling J. Two approaches for metabolic pathway analysis? Trends Biotechnol 2003;21:64-69.
28. Schilling CH, Letscher D, Palsson BO. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. JTheor Biol 2000;203:229-248.
29. Schilling CH, Palsson BO. The underlying pathway structure of biochemical reaction networks. Proc Natl Acad Sci USA 1998;95:4193-4198.
30. Papin JA, Stelling J, Price ND, et al. Comparison of network-based pathway analysis methods. Trends Biotechnol 2004;22:400-405.
31. Papin JA, Palsson BO. Topological analysis of massbalanced signaling networks: a framework to obtain network properties including crosstalk. J Theor Biol 2004;227: 283-297.
32. Covert MW, Palsson BO. Constraints-based models: regulation of gene expression reduces the steady-state solution space. JTheor Biol 2003;221:309-325.
33. Llaneras F, Picó J. Stoichiometric modelling of cell metabolism. J Biosci Bioeng 2008;105:1-11.
34. Papin JA, Price ND, Wiback SJ, etal. Metabolic pathways in the post-genome era. Trends Biochem Sci 2003;28:250-258.
35. Palsson BO. Systems Biology: Properties of Reconstructed Networks. 1st edn. New York, NY, USA: Cambridge University Press, 2006.
36. Bell SL, Palsson BO. expa: a program for calculating extreme pathways in biochemical reaction networks. Bioinformatics 2005;21:1739-1740.
37. Wiback SJ, Palsson BO. Extreme pathway analysis of human red blood cell metabolism. Biophys J 2002;83: 808-818.
38. Price ND, Papin JA, Palsson BO. Determination of redundancy and systems properties of the metabolic network of Helicobacter pylori using genome-scale extreme pathway analysis. GenomeRes 2002;12:760-769.
39. Papin JA, Palsson BO. The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis. BiophysJ 2004;87:37-46.
40. Segura D, Mahadevan R, Juárez K, et al. Computational and experimental analysis of redundancy in the central metabolism of Geobacter sulfurreducens. PLoS Comput Biol 2008;4:e36.
41. Gianchandani EP, Papin JA, Price ND, et al. Matrix formalism to describe functional states of transcriptional regulatory systems. PLoSComput Biol 2006;2:e101.
42. Schellenberger J, Palsson BO. Use of randomized sampling for analysis of metabolic networks. J Biol Chem 2009;284: 5457-5461.
43. Becker SA, Feist AM, Mo ML, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protocol 2007;2:727-738.
44. Thiele I, Price ND, Vo TD, et al. Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J BiolChem 2005;280:11683-11695.
45. Xi Y, Chen YP, Cao M, et al. Analysis on relationship between extreme pathways and correlated reaction sets. BMC Bioinform 2009;10:S58.
46. Barrett CL, Herrgard MJ, Palsson BO. Decomposing complex reaction networks using random sampling, principal component analysis, and basis rotation. BMC Syst Biol 2009;3:30.
47. Patil KR, Rocha I, Forster J, et al. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinform 2005;6:308.
48. In Silico Organisms, E. coli, E. coli SBML, http://gcrg.ucsd.edu/In.Silico.Organisms/E_coli/E_coli_SBML (2 January 2010, date last accessed).
49. Reed JL, Palsson BO. Thirteen years of building constraintbased in silico models of Escherichia coli. J Bacteriol 2003; 185:2692-2699.
50. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat RevMicrobiol 2004;2:886.
51. Schuster S, Hofer T. Determining all extreme semi-positive conservation relations in chemical reaction systems: a test criterion for conservativity. J Chem Soc: FaradayTrans 1991; 87:2561-2566.
52. Urbanczik R, Wagner C. An improved algorithm for stoichiometric network analysis: theory and applications. Bioinformatics 2005;21:1203-1210.
53. Wiback SJ, Mahadevan R, Palsson B. Reconstructing metabolic flux vectors from extreme pathways: defining the (-spectrum. JTheor Biol 2003;224:313-324.
54. Price ND, Reed JL, Papin JA, et al. Analysis of metabolic capabilities using singular value decomposition of extreme pathway matrices. Biophys J 2003;84:794-804.
55. Sharon JW, Radhakrishnan M, Bernhard P. Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum. Biotechnol Bioeng 2004;86:317-331.
56. Llaneras F, Picó J. An interval approach for dealing with flux distributions and elementary modes activity patterns. J Theor Biol 2007;246:290-308.
57. Klamt S, Stelling J. Combinatorial complexity of pathway analysis in metabolic networks. Mol Biol Rep 2002;29: 233-236.
58. Yeung M, Thiele I, Palsson BO. Estimation of the number of extreme pathways for metabolic networks. BMC Bioinform 2007;8:363.
59. Kaufman DE, Smith RL. Direction choice for accelerated convergence in hit-and-run sampling. Oper Res 1998;46: 84-95.
60. 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:R54.
61. Duarte NC, Herrgard MJ, Palsson BO. Reconstruction and validation of Saccharomyces cerevisiae iND750: a fully compartmentalized genome-scale metabolic model. GenomeRes 2004;14:1298-1309.
62. Khachiyan LG. A polynomial algorithm in linear programming. Dok'-lady Akadcmlz Nauk SSSR 1979;244: 1093-1096 (in Russian) [English translation: Smqet Mathematics Doklady 1979;20:191-194].
63. Karmarkar N. A new polynomial-time algorithm for linear programming. Combinatorica 1984;4:373-395.
64. Renegar J. A polynomial-time algorithm, based on Newton's method, for linear programming. Math Program 1988;40:59-93.