Advances in network-basedmetabolic pathway analysis and gene expression data integration

Briefings in Bioinformatics

Volumn 16, Issue 2, 2015, Pages 1-15

  Rezola, Alberto ^a   Pey, Jon ^a   Tobalina, Luis ^a   Rubio, Ángel ^a   Beasley, John E ^b   Planes, Francisco J ^a,c  

^a Bionformatics Department CEIT   (United States)

^b BRUNEL UNIVERSITY   (United Kingdom)

^c Brunel University ^*  (Spain)

Author keywords

Constraint based modelling; Elementary fluxmodes; Gene expression data; Genome scale metabolic networks; Network based metabolic pathway analysis

Indexed keywords

ALGORITHM; BIOLOGICAL MODEL; BIOLOGY; COMPUTER PROGRAM; ESCHERICHIA COLI; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETICS; METABOLISM; STATISTICS AND NUMERICAL DATA;

ALGORITHMS; COMPUTATIONAL BIOLOGY; ESCHERICHIA COLI; GENE EXPRESSION; GENE EXPRESSION PROFILING; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; SOFTWARE;

EID: 84979838081     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbu009     Document Type: Article

References (102)

1. Nelson DL, Cox MM. Lehninger Principles of Biochemistry. New York: W. H. Freeman and Company, 2004.
2. Murray R, Bender D, Botham KM, et al. Harpers Illustrated Biochemistry. NY: McGraw-Hill Medical, 2012.
3. Kanehisa M, Goto S, Sato Y, et al. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012;40:D109-14.
4. Caspi R, Altman T, Dreher K, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2012;40:D742-53.
5. Vastrik I, D'Eustachio P, Schmidt E, et al. Reactome: a knowledge base of biologic pathways and processes. Genome Biol 2007;8:R39.
6. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: archive for functional genomics data sets-10 years on. NucleicAcids Res 2011;39:D1005-10.
7. Parkinson H, Sarkans U, Kolesnikov N, et al. ArrayExpress update-an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res 2011;39:D1002-4.
8. Ball CA, Awad IAB, Demeter J, et al. The Stanford Microarray Database accommodates additional microarray platforms and data formats. Nucleic Acids Res 2005;33:D580-2.
9. Curtis RK, Orešič M, Vidal-Puig A. Pathways to the analysis of microarray data. Trends Biotechnol 2005;23:429-35.
10. Llaneras F, PicóJ. Stoichiometric modelling of cell metabolism. J Biosci Bioeng 2008;105:1-11.
11. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat RevMicrobiol 2004;2:886-97.
12. Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 2012;10:291-305.
13. Rockafellar RT. Convex Analysis. NJ: Princeton University Press, 1996, 469.
14. Reed JL, Palsson BØ. Thirteen years of building constraintbased in silico models of Escherichia coli. J Bacteriol 2003;185:2692-9.
15. Schuster S, Hilgetag C. On elementary flux modes in biochemical reaction systems at steady state. J Biol Syst 1994;2:165-82.
16. Seressiotis A, Bailey JE. MPS: an algorithm and data base for metabolic pathway synthesis. Biotechnol Lett 1986;8:837-42.
17. Mavrovouniotis ML, Stephanopoulos G, Stephanopoulos G. Computer-aided synthesis of biochemical pathways. Biotechnol Bioeng 1990;36:1119-32.
18. Heinrich R, Rapoport TA. A linear steady-state treatment of enzymatic chains. EurJ Biochem 1974;42:97-105.
19. Clarke B. Stoichiometric network analysis. Cell Biochem Biophys 1988;12:237-53.
20. Schilling CH, Schuster S, Palsson BO, et al. Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog 1999;15:296-303.
21. Papin JA, Price ND, Wiback SJ, et al. Metabolic pathways in the post-genome era. Trends Biochem Sci 2003;28:250-58.
22. Schuster S, Fell DA, Dandekar T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotech 2000;18:326-32.
23. Liao JC, Hou SY, Chao YP. Pathway analysis, engineering, and physiological considerations for redirecting central metabolism. Biotechnol Bioeng 1996;52:129-40.
24. 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.
25. Pfeiffer T, Sanchez-Valdenebro I, Nuno J, et al. METATOOL: for studying metabolic networks. Bioinformatics 1999;15:251-7.
26. Wagner C, Urbanczik R. The geometry of the flux cone of a metabolic network. BiophysJ 2005;89:3837-45.
27. Gagneur J, Klamt S. Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinformatics 2004;5:175.
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;203:229-48.
29. Motzkin TS, Raiffa H, Thompson GL, et al. The double description method. Contributions to the theory of games. Annals of Mathematics Studies. New Jersey: Princeton University Press, 1953, 51-73.
30. Wagner C. Nullspace approach to determine the elementary modes of chemical reaction systems. J Phys Chem B 2004;108:2425-31.
31. Klamt S, Gagneur J, von Kamp A. Algorithmic approaches for computing elementary modes in large biochemical reaction networks. Syst Biol 2005;152:249-55.
32. von Kamp A, Schuster S. Metatool 5.0: fast and flexible elementary modes analysis. Bioinformatics 2006;22:1930-1.
33. Klamt S, Stelling J, Ginkel M, et al. FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics 2003;19:261-9.
34. Urbanczik R. SNA - a toolbox for the stoichiometric analysis of metabolic networks. BMC Bioinformatics 2006;7:129.
35. Urbanczik R, Wagner C. An improved algorithm for stoichiometric network analysis: theory and applications. Bioinformatics 2005;21:1203-10.
36. Bell SL, Palsson BØ. Expa: a program for calculating extreme pathways in biochemical reaction networks. Bioinformatics 2005;21:1739-40.
37. Terzer M, Stelling J. Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics 2008;24:2229-35.
38. Wright J, Wagner A. The systems biology research tool: evolvable open-source software. BMC Syst Biol 2008;2:55.
39. Terzer M. Large scale methods to enumerate extreme rays and elementary modes. PhD thesis, 2009. http://dx.doi. org/10.3929/ethz-a-005945733 (23 January 2014, date last accessed).
40. Klamt S, Stelling J. Combinatorial complexity of pathway analysis in metabolic networks. Mol Biol Rep 2002;29:233-6.
41. Acuña V, Chierichetti F, Lacroix V, et al. Modes and cuts in metabolic networks: complexity and algorithms. Biosystems 2009;95:51-60.
42. Acuña V, Marchetti-Spaccamela A, Sagot MF, et al. A note on the complexity of finding and enumerating elementary modes. Biosystems 2010;99:210-14.
43. 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;104:1777-82.
44. Thiele I, Swainston N, Fleming RMT, et al. A communitydriven global reconstruction of human metabolism. Nat Biotech 2013;31:419-25.
45. Kaleta C, de Figueiredo LF, Behre J, et al. EFMEvolver:computing elementary flux modes in genome-scale metabolic networks. LectNotes Inform 2009;179-89.
46. De Figueiredo LF, Podhorski A, Rubio A, et al. Computing the shortest elementary flux modes in genome-scale metabolic networks. Bioinformatics 2009;25:3158-65.
47. Meléndez-Hevia E, Waddell TG, Montero F. Optimization of metabolism: the evolution of metabolic pathways toward simplicity through the game of the pentose phosphate cycle. JTheor Biol 1994;166:201-20.
48. Pfeiffer T, Bonhoeffer S. Evolution of cross feeding in microbial populations. AmNat 2004;163:E126-35.
49. Rezola A, Pey J, de Figueiredo LF, et al. Selection of human tissue-specific elementary flux modes using gene expression data. Bioinformatics 2013;29:2009-16.
50. Rezola A, de Figueiredo LF, Brock M, et al. Exploring metabolic pathways in genome-scale networks via generating flux modes. Bioinformatics 2011;27:534-40.
51. Larhlimi A, Bockmayr A. A new constraint-based description of the steady-state flux cone of metabolic networks. DiscreteApplMath 2009;157:2257-66.
52. Rahman SA, Schomburg D. Observing local and global properties of metabolic pathways: ''load points'' and ''choke points'' in the metabolic networks. Bioinformatics 2006;22:1767-74.
53. Fatumo S, Plaimas K, Mallm JP, et al. Estimating novel potential drug targets of Plasmodiumfalciparum by analysing the metabolic network of knock-out strains in silico. Infect Genet Evol 2009;9:351-8.
54. Guimerà R, Sales-Pardo M, Amaral LAN. A network-based method for target selection in metabolic networks. Bioinformatics 2007;23:1616-22.
55. Lee DS, Park J, Kay KA, et al. The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad SciUSA 2008;105:9880-5.
56. Planes FJ, Beasley JE. Path finding approaches and metabolic pathways. DiscreteApplMath 2009;157:2244-56.
57. Croes D, Couche F, Wodak SJ, et al. Inferring meaningful pathways in weighted metabolic networks. JMolBiol 2006;356:222-36.
58. De Figueiredo LF, Schuster S, Kaleta C, et al. Can sugars be produced from fatty acids? A test case for pathway analysis tools. Bioinformatics 2009;25:152-8.
59. Faust K, Croes D, van Helden J. In response to ''Can sugars be produced from fatty acids? A test case for pathway analysis tools''. Bioinformatics 2009;25:3202-5.
60. Deville Y, Gilbert D, van Helden J, et al. An overview of data models for the analysis of biochemical pathways. Br Bioinform 2003;4:246-59.
61. Pey J, Prada J, Beasley JE, et al. Path finding methods accounting for stoichiometry in metabolic networks. Genome Biol 2011;12:R49.
62. Pey J, Tobalina L, de Cisneros JP, et al. A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype. BMCSystBiol 2013;7:62.
63. Pey J, Planes FJ, Beasley JE. Refining carbon flux paths using atomic trace data. Bioinformatics 2013, in press.
64. Pey J, Valgepea K, Rubio A, et al. Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways. BMC Syst Biol 2013;7:134.
65. Kaleta C, de Figueiredo LF, Schuster S. Can the whole be less than the sum of its parts? Pathway analysis in genomescale metabolic networks using elementary flux patterns. Genome Res 2009;19:1872-83.
66. Schuster S, Pfeiffer T, Moldenhauer F, et al. Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae. Bioinformatics 2002;18:351-61.
67. Liebermeister W, Baur U, Klipp E. Biochemical network models simplified by balanced truncation. FEBSJ 2005;272:4034-43.
68. Kaleta C, de Figueiredo LF, Werner S, et al. In Silico evidence for gluconeogenesis from fatty acids in humans. PLoS Comput Biol 2011;7:e1002116.
69. Oh MK, Liao JC. DNA microarray detection of metabolic responses to protein overproduction in Escherichia coli. Metab Eng 2000;2:201-9.
70. Smyth GK. Limma: linear models for microarray data. Bioinform Comput Biol Solution R Bioconductor 2005;397-420.
71. Efron B, Tibshirani R. Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol 2002;23:70-86.
72. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad SciUSA 2001;98:5116-21.
73. Leek JT, Monsen E, Dabney AR, et al. EDGE: extraction and analysis of differential gene expression. Bioinformatics 2006;22:507-8.
74. Weniger M, Engelmann J, Schultz J. Genome expression pathway analysis tool - analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context. BMC Bioinformatics 2007;8:179.
75. Shi W, de Graaf CA, Kinkel SA, et al. Estimating the proportion of microarray probes expressed in an RNA sample. Nucleic Acids Res 2010;38:2168-76.
76. McCall MN, Uppal K, Jaffee HA, et al. The gene expression barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res 2011;39:D1011-15.
77. Ghazalpour A, Doss S, Sheth SS, et al. Genomic analysis of metabolic pathway gene expression in mice. Genome Biol 2005;6:R59.
78. Konradi C, Eaton M, MacDonald ML, et al. Molecular evidence for mitochondrial dysfunction in bipolar disorder. Arch Gen Psychiatry 2004;61:300-8.
79. Naylor TL, Greshock J, Wang Y, et al. High resolution genomic analysis of sporadic breast cancer using arraybased comparative genomic hybridization. Breast Cancer Res 2005;7:R1186-98.
80. Li C, Li X, Miao Y, et al. SubpathwayMiner: a software package for flexible identification of pathways. Nucleic Acids Res 2009;37:e131.
81. Zien A, Küffner R, Zimmer R, et al. Analysis of gene expression data with pathway scores. Proc IntConf Intell Syst Mol Biol 2000;8:407-17.
82. Schwartz JM, Gaugain C, Nacher J, et al. Observing metabolic functions at the genome scale. Genome Biol 2007;8:R123.
83. Wessely F, Bartl M, Guthke R, et al. Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs. Mol Syst Biol 2011;7:515.
84. Schellenberger J, Park J, Conrad T, et al. BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 2010;11:213.
85. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009;37:1-13.
86. Drăghici S, Khatri P, Martins RP, et al. Global functional profiling of gene expression. Genomics 2003;81:98-104.
87. Beißbarth T, Speed TP. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 2004;20:1464-5.
88. Rivals I, Personnaz L, Taing L, et al. Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics 2007;23:401-7.
89. Khatri P, Bhavsar P, Bawa G, et al. Onto-Tools: an ensemble of web-accessible, ontology-based tools for the functional design and interpretation of high-throughput gene expression experiments. Nucleic Acids Res 2004;32:W449-56.
90. Doniger S, Salomonis N, Dahlquist K, et al. MAPPFinder:using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 2003;4:R7.
91. Pavlidis P, Lewis DP, Noble WS. Exploring gene expression data with class scores. Proc Pac Symp Biocomput 2001; 474-85.
92. Goeman JJ, van de Geer SA, de Kort F, et al. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 2004;20:474-85.
93. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102:15545-50.
94. Efron B, Tibshirani R. On testing the significance of sets of genes. Ann Appl Stat 2007;1:107-29.
95. Barry WT, Nobel AB, Wright FA. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 2005;21:1943-9.
96. Schwarz R, Musch P, von Kamp A, et al. YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities. BMC Bioinformatics 2005;6:135.
97. Antonov A, Dietmann S, Mewes H. KEGG spider:interpretation of genomics data in the context of the global gene metabolic network. Genome Biol 2008;9:R179.
98. Faust K, Dupont P, Callut J, et al. Pathway discovery in metabolic networks by subgraph extraction. Bioinformatics 2010;26:1211-18.
99. Alcaraz N, Kücük H, Weile J, et al. KeyPathwayMiner:detecting case-specific biological pathways using expression data. InternetMath 2011;7:299-313.
100. Baumbach J, Friedrich T, Kötzing T, et al. Efficient algorithms for extracting biological key pathways with global constraints. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference. New York, NY: ACM, 2012;169-76.
101. Barriot R, Poix J, Groppi A, et al. New strategy for the representation and the integration of biomolecular knowledge at a cellular scale. Nucleic Acids Res 2004;32:3581-9.
102. Rezola A, Pey J, Rubio A, et al. Interpreting highthroughput expression data of lung cancer subtypes in light of metabolic pathways. In: Proceedings of XXX Congreso Anual de la Sociedad Española de Ingeniera Biomédica (CASEIB 2012), San Sebastián (Spain), 2012.