Multi -omics and metabolic modelling pipelines: Challenges and tools for systems microbiology

Microbiological Research

Volumn 171, Issue , 2015, Pages 52-64

  Fondi, Marco ^a   Liò, Pietro ^b  

^a UNIVERSITY OF FLORENCE   (Italy)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

omics integration; Computational biology; Metabolic modelling; Multi omics; Systems microbiology

Indexed keywords

DATA INTEGRATION; LARGE DATASET; METABOLISM; MICROBIOLOGY; PIPELINES;

AMOUNT OF INFORMATION; BACTERIAL METABOLISM; BIOLOGICAL INFORMATION; COMPUTATIONAL BIOLOGY; LARGE-SCALE DATASETS; METABOLIC MODELLING; MULTI -OMICS; SPATIAL ORGANIZATION;

LARGE SCALE SYSTEMS;

BACTERIUM; BIOTECHNOLOGY; CELL ORGANELLE; COMPUTER SIMULATION; DATA SET; METABOLISM; MICROBIOLOGY; MOLECULAR ANALYSIS; NUMERICAL MODEL;

BACTERIA (MICROORGANISMS);

EID: 84930045803     PISSN: 09445013     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.micres.2015.01.003     Document Type: Review

References (143)

1. Angiuoli S.V., Gussman A., Klimke W., Cochrane G., Field D., Garrity G., et al. Toward an online repository of standard operating procedures (SOPs) for (meta)genomic annotation. Omics 2008, 12(2):137-141.
2. Ansong C., Schrimpe-Rutledge A.C., Mitchell H.D., Chauhan S., Jones M.B., Kim Y.M., et al. A multi-omic systems approach to elucidating Yersinia virulence mechanisms. Mol Biosyst 2013, 9(1):44-54.
3. Ansong C., Deatherage B.L., Hyduke D., Schmidt B., McDermott J.E., Jones M.B., et al. Studying Salmonellae and Yersiniae host-pathogen interactions using integrated omics and modeling. Curr Top Microbiol Immunol 2013, 363:21-41.
4. Arakawa K., Tomita M. Merging multiple omics datasets in silico: statistical analyses and data interpretation. Methods Mol Biol 2013, 985:459-470.
5. Aziz R.K., Bartels D., Best A.A., DeJongh M., Disz T., Edwards R.A., et al. The RAST server: rapid annotations using subsystems technology. BMC Genomics 2008, 9:75.
6. Bardoscia M.M., Samal A. Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks 2014.
7. Battke F., Symons S., Nieselt K. Mayday - integrative analytics for expression data. BMC Bioinform 2010, 11:121.
8. Beg Q.K., Vazquez A., Ernst J., de Menezes M.A., Bar-Joseph Z., Barabasi A.L., et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci U S A 2007, 104(31):12663-12668.
9. Beloqui A., de Maria P.D., Golyshin P.N., Ferrer M. Recent trends in industrial microbiology. Curr Opin Microbiol 2008, 11(3):240-248.
10. Berger A., Dohnt K., Tielen P., Jahn D., Becker J., Wittmann C. Robustness and plasticity of metabolic pathway flux among uropathogenic isolates of Pseudomonas aeruginosa. PLOS ONE 2014, 9(4):e88368.
11. Berghoff B.A., Konzer A., Mank N.N., Looso M., Rische T., Forstner K.U., et al. Integrative omics-approach discovers dynamic and regulatory features of bacterial stress responses. PLoS Genet 2013, 9(6):e1003576.
12. Blazier A.S., Papin J.A. Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 2012, 3:299.
13. Bochner B.R., Gadzinski P., Panomitros E. Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res 2001, 11(7):1246-1255.
14. Buijs J., Franklin G.C. SPR-MS in functional proteomics. Brief Funct Genomic Proteomic 2005, 4(1):39-47.
15. Burton J.N., Liachko I., Dunham M.J., Shendure J. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. G3 2014.
16. Cakir T., Patil K.R., Onsan Z., Ulgen K.O., Kirdar B., Nielsen J. Integration of metabolome data with metabolic networks reveals reporter reactions. Mol Syst Biol 2006, 2:50.
17. Camilo E., Bovolenta L.A., Acencio M.L., Rybarczyk-Filho J.L., Castro M.A., Moreira J.C., et al. GALANT: a Cytoscape plugin for visualizing data as functional landscapes projected onto biological networks. Bioinformatics 2013, 29(19):2505-2506.
18. Carlo Berzuini, Luisa Bernardinelli Causality: statistical perspectives and applications 2012, John Wiley & Sons, Ltd.
19. Casadesus J., Low D. Epigenetic gene regulation in the bacterial world. Microbiol Mol Biol Rev 2006, 70(3):830-856.
20. Chandrasekaran S., Price N.D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 2010, 107(41):17845-17850.
21. Chang C., Li L., Zhang C., Wu S., Guo K., Zi J., et al. Systematic analyses of the transcriptome, translatome, and proteome provide a global view and potential strategy for the C-HPP. J Proteome Res 2014, 13(1):38-49. 10.1021/pr4009018.
22. Chen X., Alonso A.P., Allen D.K., Reed J.L., Shachar-Hill Y. Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli. Metab Eng 2011, 13(1):38-48.
23. Colijn C., Brandes A., Zucker J., Lun D.S., Weiner B., Farhat M.R., et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009, 5(8):e1000489.
24. Copeland W.B., Bartley B.A., Chandran D., Galdzicki M., Kim K.H., Sleight S.C., et al. Computational tools for metabolic engineering. Metab Eng 2012, 14(3):270-280.
25. Deatherage Kaiser B.L., Li J., Sanford J.A., Kim Y.M., Kronewitter S.R., Jones M.B., et al. A multi-omic view of host-pathogen-commensal interplay in mediated intestinal infection. PLOS ONE 2013, 8(6):e67155.
26. Depuydt S., Trenkamp S., Fernie A.R., Elftieh S., Renou J.P., Vuylsteke M., et al. An integrated genomics approach to define niche establishment by Rhodococcus fascians. Plant Physiol 2009, 149(3):1366-1386.
27. Dimmer E.C., Huntley R.P., Alam-Faruque Y., Sawford T., O'Donovan C., Martin M.J., et al. The UniProt-GO annotation database in 2011. Nucleic Acids Res 2012, 40:D565-D570. [Database issue].
28. Doerfler H., Lyon D., Nagele T., Sun X., Fragner L., Hadacek F., et al. Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism. Metabolomics: Off J Metabolomic Soc 2013, 9(3):564-574.
29. Durot M., Bourguignon P.Y., Schachter V. Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 2009, 33(1):164-190.
30. Durot M., Le Fevre F., de Berardinis V., Kreimeyer A., Vallenet D., Combe C., et al. Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data. BMC Syst Biol 2008, 2:85.
31. Durre P. Biobutanol: an attractive biofuel. Biotechnol J 2007, 2(12):1525-1534.
32. Eddy S.R. A model of the statistical power of comparative genome sequence analysis. PLoS Biol 2005, 3(1):e10.
33. Endesfelder U., Finan K., Holden S.J., Cook P.R., Kapanidis A.N., Heilemann M. Multiscale spatial organization of RNA polymerase in Escherichia coli. Biophys J 2013, 105(1):172-181.
34. Enright A.J., Iliopoulos I., Kyrpides N.C., Ouzounis C.A. Protein interaction maps for complete genomes based on gene fusion events. Nature 1999, 402(6757):86-90.
35. Faith J.J., Hayete B., Thaden J.T., Mogno I., Wierzbowski J., Cottarel G., et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 2007, 5(1):e8.
36. Fang K., Zhao H., Sun C., Lam C.M., Chang S., Zhang K., et al. Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction. BMC Syst Biol 2011, 5:83.
37. Feng X., Zhao H. Investigating xylose metabolism in recombinant Saccharomyces cerevisiae via 13C metabolic flux analysis. Microb Cell Fact 2013, 12(1):114.
38. Fondi M., Maida I., Perrin E., Mellera A., Mocali S., Parrilli E., et al. Genome-scale metabolic reconstruction and constraint-based modelling of the antarctic bacterium Pseudoalteromonas haloplanktis TAC125. Environ Microbiol 2014.
39. Forbes A., Griffiths P. Methodological strategies for the identification and synthesis of 'evidence' to support decision-making in relation to complex healthcare systems and practices. Nurs Inq 2002, 9(3):141-155.
40. Friedlander T.M., Tlusty A.E., Alon T. Evolution of bow-tie architectures in biology 2014.
41. Fu F., Cheng V.W., Wu Y., Tang Y., Weiner J.H., Li L. Comparative proteomic and metabolomic analysis of Staphylococcus warneri SG1 cultured in the presence and absence of butanol. J Proteome Res 2013.
42. Garcia-Alcalde F., Garcia-Lopez F., Dopazo J., Conesa A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 2011, 27(1):137-139.
43. Garcia-Ochoa F., Santos V.E., Casas J.A., Gomez E. Xanthan gum: production, recovery, and properties. Biotechnol Adv 2000, 18(7):549-579.
44. Gehlenborg N., O'Donoghue S.I., Baliga N.S., Goesmann A., Hibbs M.A., Kitano H., et al. Visualization of omics data for systems biology. Nat Methods 2010, 7(Suppl. 3):S56-S68.
45. George H.A., Johnson J.L., Moore W.E., Holdeman L.V., Chen J.S. Acetone, isopropanol, and butanol production by Clostridium beijerinckii (syn. Clostridium butylicum) and Clostridium aurantibutyricum. Appl Environ Microbiol 1983, 45(3):1160-1163.
46. Geuten K., Massingham T., Darius P., Smets E., Goldman N. Experimental design criteria in phylogenetics: where to add taxa. Syst Biol 2007, 56(4):609-622.
47. Guell O., Sagues F., Serrano M.A. Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis. PLoS Comput Biol 2014, 10(5):e1003637.
48. Gygi S.P., Rochon Y., Franza B.R., Aebersold R. Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 1999, 19(3):1720-1730.
49. Hain T., Ghai R., Billion A., Kuenne C.T., Steinweg C., Izar B., et al. Comparative genomics and transcriptomics of lineages I, II, and III strains of Listeria monocytogenes. BMC Genomics 2012, 13:144.
50. Hastings J., de Matos P., Dekker A., Ennis M., Harsha B., Kale N., et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 2013, 41:D456-D463. [Database issue].
51. Hermida L., Poussin C., Stadler M.B., Gubian S., Sewer A., Gaidatzis D., et al. Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data. BMC Genomics 2013, 14:514.
52. Hitchen P.G., Dell A. Bacterial glycoproteomics. Microbiology 2006, 152(Pt 6):1575-1580.
53. Huang S., Chen L., Te R., Qiao J., Wang J., Zhang W. Complementary iTRAQ proteomics and RNA-seq transcriptomics reveal multiple levels of regulation in response to nitrogen starvation in Synechocystis sp. PCC 6803. Mol Biosyst 2013, 9(10):2565-2574.
54. Ishii N., Nakahigashi K., Baba T., Robert M., Soga T., Kanai A., et al. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 2007, 316(5824):593-597.
55. Jamshidi N., Palsson B.O. Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys J 2010, 98(2):175-185.
56. Jayapal K.P., Philp R.J., Kok Y.J., Yap M.G., Sherman D.H., Griffin T.J., et al. Uncovering genes with divergent mRNA-protein dynamics in Streptomyces coelicolor. PLoS ONE 2008, 3(5):e2097.
57. Jensen P.A., Papin J.A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 2011, 27(4):541-547.
58. Karaosmanoglu K., Sayar N.A., Aksan Kurnaz I., Sariyar Akbulut B. Assessment of berberine as a multi-target antimicrobial: a multi -omics study for drug discovery and repositioning. OMICS 2014, 18(1):42-53. 10.1089/omi.2013.0100.
59. Karnovsky A., Weymouth T., Hull T., Tarcea V.G., Scardoni G., Laudanna C., et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 2012, 28(3):373-380.
60. Karpievitch Y.V., Taverner T., Adkins J.N., Callister S.J., Anderson G.A., Smith R.D., et al. Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition. Bioinformatics 2009, 25(19):2573-2580.
61. Kim J., Reed J.L. RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations. Genome Biol 2012, 13(9):R78.
62. Klukas C., Schreiber F. Integration of -omics data and networks for biomedical research with vanted. J Integr Bioinform 2010, 7(2):112.
63. Kromer J.O., Sorgenfrei O., Klopprogge K., Heinzle E., Wittmann C. In-depth profiling of lysine-producing Corynebacterium glutamicum by combined analysis of the transcriptome, metabolome, and fluxome. J Bacteriol 2004, 186(6):1769-1784.
64. Kumar V.S., Maranas C.D. GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput Biol 2009, 5(3):e1000308.
65. Kuo T.C., Tian T.F., Tseng Y.J. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol 2013, 7:64.
66. Kurz V., Nelson E.M., Perry N., Timp W., Timp G. Epigenetic memory emerging from integrated transcription bursts. Biophys J 2013, 105(6):1526-1532.
67. Le Cao K.A., Gonzalez I., Dejean S. integrOmics: an R package to unravel relationships between two omics datasets. Bioinformatics 2009, 25(21):2855-2856.
68. Lee S.J., Lee D.Y., Kim T.Y., Kim B.H., Lee J., Lee S.Y. Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 2005, 71(12):7880-7887.
69. Li Q., Cheng T., Wang Y., Bryant S.H. PubChem as a public resource for drug discovery. Drug Discov Today 2010, 15(23-24):1052-1057.
70. Lieberman-Aiden E., van Berkum N.L., Williams L., Imakaev M., Ragoczy T., Telling A., et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 2009, 326(5950):289-293.
71. Lin J., Xie Z., Zhu H., Qian J. Understanding protein phosphorylation on a systems level. Brief Funct Genomics 2010, 9(1):32-42.
72. Lin K., Kools H., de Groot P.J., Gavai A.K., Basnet R.K., Cheng F., et al. MADMAX - management and analysis database for multiple omics experiments. J Integr Bioinform 2011, 8(2):160.
73. Ma Q., Zhou J., Zhang W., Meng X., Sun J., Yuan Y.J. Integrated proteomic and metabolomic analysis of an artificial microbial community for two-step production of vitamin C. PLoS ONE 2011, 6(10):e26108.
74. Machado D., Herrgard M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 2014, 10(4):e1003580.
75. Maier T., Schmidt A., Guell M., Kuhner S., Gavin A.C., Aebersold R., et al. Quantification of mRNA and protein and integration with protein turnover in a bacterium. Mol Syst Biol 2011, 7:511.
76. Maier T., Marcos J., Wodke J.A., Paetzold B., Liebeke M., Gutierrez-Gallego R., et al. Large-scale metabolome analysis and quantitative integration with genomics and proteomics data in Mycoplasma pneumoniae. Mol Biosyst 2013, 9(7):1743-1755.
77. Markus Kalisch MM, Diego Colombo, Marloes H. Maathuis, Peter Bühlmann Causal Inference Using Graphical Models with the R Package pcalg. Journal of statistical software 2012; 47(11).
78. Martin A., Ochagavia M.E., Rabasa L.C., Miranda J., Fernandez-de-Cossio J., Bringas R. BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinform 2010, 11:91.
79. McAuliffe J.D., Jordan M.I., Pachter L. Subtree power analysis and species selection for comparative genomics. Proc Natl Acad Sci U S A 2005, 102(22):7900-7905.
80. Meng C., Kuster B., Culhane A.C., Moghaddas Gholami A. A multivariate approach to the integration of multi-omics datasets. BMC Bioinform 2014, 15:162.
81. Merelli I., Lio P., Milanesi L. NuChart: an R package to study gene spatial neighbourhoods with multi-omics annotations. PLOS ONE 2013, 8(9):e75146.
82. Monk J.M., Charusanti P., Aziz R.K., Lerman J.A., Premyodhin N., Orth J.D., et al. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc Natl Acad Sci U S A 2013, 110(50):20338-20343.
83. Moriya Y., Itoh M., Okuda S., Yoshizawa A.C., Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 2007, 35:W182-W185. [Web Server issue].
84. Moura M., Broadbelt L., Tyo K. Computational tools for guided discovery and engineering of metabolic pathways. Methods Mol Biol 2013, 985:123-147.
85. Nagy H., Sornay-Rendu E., Boutroy S., Vilayphiou N., Szulc P., Chapurlat R. Impaired trabecular and cortical microarchitecture in daughters of women with osteoporotic fracture: the MODAM study. Osteoporosis Int: Journal Established as Result of Cooperation Between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA 2013, 24(6):1881-1889.
86. Navid A., Almaas E. Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach. BMC Syst Biol 2012, 6:150.
87. Neto E.C., Keller M.P., Attie A.D., Yandell B.S. Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Ann Appl Stat 2010, 4(1):320-339.
88. Neuweger H., Persicke M., Albaum S.P., Bekel T., Dondrup M., Huser A.T., et al. Visualizing post genomics data-sets on customized pathway maps by ProMeTra-aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example. BMC Syst Biol 2009, 3:82.
89. Nie L., Wu G., Zhang W. Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations. Biochem Biophys Res Commun 2006, 339(2):603-610.
90. Oberhardt M.A., Chavali A.K., Papin J.A. Flux balance analysis: interrogating genome-scale metabolic networks. Methods Mol Biol 2009, 500:61-80.
91. Oberhardt M.A., Puchalka J., Martins dos Santos V.A., Papin J.A. Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. PLoS Comput Biol 2011, 7(3):e1001116.
92. Oh Y.K., Palsson B.O., Park S.M., Schilling C.H., Mahadevan R. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem 2007, 282(39):28791-28799.
93. Pacheco M.P., Sauter T. Fast reconstruction of compact context-specific metabolic networks via integration of microarray data 2014.
94. Paley S.M., Karp P.D. The pathway tools cellular overview diagram and omics viewer. Nucleic Acids Res 2006, 34(13):3771-3778.
95. Palsson B., Zengler K. The challenges of integrating multi-omic data sets. Nat Chem Biol 2010, 6(11):787-789.
96. Papin J.A., Price N.D., Wiback S.J., Fell D.A., Palsson B.O. Metabolic pathways in the post-genome era. Trends Biochem Sci 2003, 28(5):250-258.
97. Pardi F., Goldman N. Species choice for comparative genomics: being greedy works. PLoS Genet 2005, 1(6):e71.
98. Park T., Yi S.G., Kang S.H., Lee S., Lee Y.S., Simon R. Evaluation of normalization methods for microarray data. BMC Bioinform 2003, 4:33.
99. Perco P., Muhlberger I., Mayer G., Oberbauer R., Lukas A., Mayer B. Linking transcriptomic and proteomic data on the level of protein interaction networks. Electrophoresis 2010, 31(11):1780-1789.
100. Poblete-Castro I., Escapa I.F., Jager C., Puchalka J., Lam C.M., Schomburg D., et al. The metabolic response of P. putida KT2442 producing high levels of polyhydroxyalkanoate under single- and multiple-nutrient-limited growth: highlights from a multi-level omics approach. Microb Fact 2012, 11:34.
101. Polpitiya A.D., Qian W.J., Jaitly N., Petyuk V.A., Adkins J.N., Camp D.G., et al. DAnTE: a statistical tool for quantitative analysis of omics data. Bioinformatics 2008, 24(13):1556-1558.
102. Prax M., Bertram R. Metabolic aspects of bacterial persisters. Front Cell Infect Microbiol 2014, 4:148.
103. Quek L.E., Wittmann C., Nielsen L.K., Kromer J.O. OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis. Microb Cell Fact 2009, 8:25.
104. Rao V.S., Srinivas K., Sujini G.N., Kumar G.N. Protein-protein interaction detection: methods and analysis. Int J Proteomics 2014, 2014:147648.
105. Sana T.R., Fischer S., Wohlgemuth G., Katrekar A., Jung K.H., Ronald P.C., et al. Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae. Metabolomics: Off J Metabolomics Soc 2010, 6(3):451-465.
106. Sass S., Buettner F., Mueller N.S., Theis F.J. A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Res 2013.
107. Sass S., Buettner F., Mueller N.S., Theis F.J. A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Res 2013, 41(21):9622-9633.
108. Schaub J., Clemens C., Kaufmann H., Schulz T.W. Advancing biopharmaceutical process development by system-level data analysis and integration of omics data. Adv Biochem Eng Biotechnol 2012, 127:133-163.
109. Schermelleh L., Heintzmann R., Leonhardt H. A guide to super-resolution fluorescence microscopy. J Cell Biol 2010, 190(2):165-175.
110. Schmidt B.J., Ebrahim A., Metz T.O., Adkins J.N., Palsson B.O., Hyduke D.R. GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics 2013, 29(22):2900-2908.
111. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014, 30(14):2068-2069.
112. Sengupta T., Bhushan M., Wangikar P.P. Metabolic modeling for multi-objective optimization of ethanol production in a Synechocystis mutant. Photosynth Res 2013, 118(1-2):155-165.
113. Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13(11):2498-2504.
114. Shavit Y., Lio P. CytoHiC: a cytoscape plugin for visual comparison of Hi-C networks. Bioinformatics 2013, 29(9):1206-1207.
115. Shell S.S., Prestwich E.G., Baek S.H., Shah R.R., Sassetti C.M., Dedon P.C., et al. DNA methylation impacts gene expression and ensures hypoxic survival of Mycobacterium tuberculosis. PLoS Pathog 2013, 9(7):e1003419.
116. Shi Y., Tyson G.W., Eppley J.M., DeLong E.F. Integrated metatranscriptomic and metagenomic analyses of stratified microbial assemblages in the open ocean. ISME J 2011, 5(6):999-1013.
117. Shi Z., Wang J., Zhang B. NetGestalt: integrating multidimensional omics data over biological networks. Nat Methods 2013, 10(7):597-598.
118. Shlomi T., Cabili M.N., Herrgard M.J., Palsson B.O., Ruppin E. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 2008, 26(9):1003-1010.
119. Stadler C., Rexhepaj E., Singan V.R., Murphy R.F., Pepperkok R., Uhlen M., et al. Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells. Nat Methods 2013, 10(4):315-323.
120. Sun H., Wang H., Zhu R., Tang K., Gong Q., Cui J., et al. iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis. Bioinformatics 2014, 30(5):737-739. 10.1093/bioinformatics/btt576.
121. Tatusov R.L., Fedorova N.D., Jackson J.D., Jacobs A.R., Kiryutin B., Koonin E.V., et al. The COG database: an updated version includes eukaryotes. BMC Bioinform 2003, 4:41.
122. Thiele I., Fleming R.M., Que R., Bordbar A., Diep D., Palsson B.O. Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage. PLoS ONE 2012, 7(9):e45635.
123. Thomas A., Rahmanian S., Bordbar A., Palsson B.O., Jamshidi N. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance. Sci Rep 2014, 4:3925.
124. Tomar N., De R.K. Comparing methods for metabolic network analysis and an application to metabolic engineering. Gene 2013, 521(1):1-14.
125. Tong W., Chen Z., Cao Z., Wang Q., Zhang J., Bai X., et al. Robustness analysis of a constraint-based metabolic model links cell growth and proteomics of Thermoanaerobacter tengcongensis under temperature perturbation. Mol Biosyst 2013, 9(4):713-722.
126. Topfer N., Jozefczuk S., Nikoloski Z. Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli. BMC Syst Biol 2012, 6:148.
127. Vanee N., Brooks J.P., Spicer V., Shamshurin D., Krokhin O., Wilkins J.A., et al. Proteomics-based metabolic modeling and characterization of the cellulolytic bacterium Thermobifida fusca. BMC Syst Biol 2014, 8:86.
128. Varma A., Palsson B.O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 1994, 60(10):3724-3731.
129. Vogel C., Marcotte E.M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 2012, 13(4):227-232.
130. Vogelsang J., Steinhauer C., Forthmann C., Stein I.H., Person-Skegro B., Cordes T., et al. Make them blink: probes for super-resolution microscopy. ChemPhysChem 2010, 11(12):2475-2490.
131. Walther D., Strassburg K., Durek P., Kopka J. Metabolic pathway relationships revealed by an integrative analysis of the transcriptional and metabolic temperature stress-response dynamics in yeast. Omics: J Integr Biol 2010, 14(3):261-274.
132. Wiench B., Chen Y.R., Paulsen M., Hamm R., Schroder S., Yang N.S., et al. Integration of different -omics technologies identifies inhibition of the IGF1R-Akt-mTOR signaling cascade involved in the cytotoxic effect of shikonin against leukemia cells. Evid Based Complem Altern Med: eCAM 2013, 2013:818709.
133. Winter G., Kromer J.O. Fluxomics - connecting 'omics analysis and phenotypes. Environ Microbiol 2013, 15(7):1901-1916.
134. Wrzodek C., Eichner J., Buchel F., Zell A. InCroMAP: integrated analysis of cross-platform microarray and pathway data. Bioinformatics 2013, 29(4):506-508.
135. Xia T., Hemert J.V., Dickerson J.A. OmicsAnalyzer: a Cytoscape plug-in suite for modeling omics data. Bioinformatics 2010, 26(23):2995-2996.
136. Yang S., Tschaplinski T.J., Engle N.L., Carroll S.L., Martin S.L., Davison B.H., et al. Transcriptomic and metabolomic profiling of Zymomonas mobilis during aerobic and anaerobic fermentations. BMC Genom 2009, 10:34.
137. Yang Y.H., Dudoit S., Luu P., Lin D.M., Peng V., Ngai J., et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002, 30(4):e15.
138. Yizhak K., Benyamini T., Liebermeister W., Ruppin E., Shlomi T. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 2010, 26(12):i255-i260.
139. 2 levels, using integrated metabolome and transcriptome analysis. Appl Environ Microbiol 2008, 74(9):2787-2796.
140. Zhang F., Drabier R. IPAD: the integrated pathway analysis database for systematic enrichment analysis. BMC Bioinform 2012, 13(Suppl 15):S7.
141. Zhang W., Li F., Nie L. Integrating multiple 'omics' analysis for microbial biology: application and methodologies. Microbiology 2010, 156(Pt 2):287-301.
142. Zhu H., Ren X., Wang J., Song Z., Shi M., Qiao J., et al. Integrated OMICS guided engineering of biofuel butanol-tolerance in photosynthetic Synechocystis sp. PCC 6803. Biotechnol. Biofuels 2013, 6(1):106.
143. Zou W., Liu L., Zhang J., Yang H., Zhou M., Hua Q., et al. Reconstruction and analysis of a genome-scale metabolic model of the vitamin C producing industrial strain Ketogulonicigenium vulgare WSH-001. J Biotechnol 2012, 161(1):42-48.