Genome-scale bacterial transcriptional regulatory networks: Reconstruction and integrated analysis with metabolic models

Briefings in Bioinformatics

Volumn 15, Issue 4, 2014, Pages 592-611

  Faria, José P ^a   Overbeek, Ross ^b   Xia, Fangfang ^b   Rocha, Miguel ^a   Rocha, Isabel ^a   Henry, Christopher S ^b,c  

^a UNIVERSITY OF MINHO   (Portugal)

^b ARGONNE NATIONAL LABORATORY   (United States)

^c NORTHWESTERN UNIVERSITY   (United States)

Author keywords

De novo reverse engineering; Genome scale metabolic (GSM) model; Integrated metabolic and regulatory models; Transcriptional regulatory network (TRN)

Indexed keywords

BACTERIAL GENOME; BACTERIUM; BIOLOGICAL MODEL; CLASSIFICATION; GENE REGULATORY NETWORK; GENETIC DATABASE; GENETIC TRANSCRIPTION; GENETICS; METABOLISM; PHYLOGENY;

BACTERIA; DATABASES, GENETIC; GENE REGULATORY NETWORKS; GENOME, BACTERIAL; METABOLISM; MODELS, BIOLOGICAL; PHYLOGENY; TRANSCRIPTION, GENETIC;

EID: 85047688512     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbs071     Document Type: Article

References (191)

1. Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol 2010;26:721-44.
2. Reed JL, Famili I, Thiele I, et al. Towards multidimensional genome annotation. Nat Rev Genet 2006;7:130-41.
3. Covert MW, Schilling CH, Famili I, et al. Metabolic modeling of microbial strains in silico. Trends Biochem Sci 2001;26:179-86.
4. Henry CS, DeJongh M, Best AA, et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 2010;28:977-82.
5. Terzer M, Maynard ND, Covert MW, et al. Genome-scale metabolic networks. Wiley Interdiscip Rev Syst Biol Med 2009;1:285-97.
6. Ruppin E, Papin JA, de Figueiredo LF, et al. Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks. Curr Opin Biotechnol 2010;21:502-10.
7. Feist AM, Herrgard MJ, Thiele I, et al. Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 2009;7:129-43.
8. Struhl K. Fundamentally different logic of gene regulation in eukaryotes and prokaryotes. Cell 1999;98:1-4.
9. Nudler E, Mironov AS. The riboswitch control of bacterial metabolism. Trends Biochem Sci 2004;29:11-7.
10. Mironov AS, Gusarov I, Rafikov R, et al. Sensing small molecules by nascent RNA: a mechanism to control transcription in bacteria. Cell 2002;111:747-56.
11. Wagner EG, Simons RW. Antisense RNA control in bacteria, phages, and plasmids. Annu Rev Microbiol 1994;48:713-42.
12. Chen K, Rajewsky N. The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet 2007;8:93-103.
13. Covert MW, Schilling CH, Palsson B. Regulation of gene expression in flux balance models of metabolism. J Theor Biol 2001;213:73-88.
14. Covert MW, Knight EM, Reed JL, et al. Integrating high-throughput and computational data elucidates bacterial networks. Nature 2004;429:92-6.
15. Yoon H, McDermott JE, Porwollik S, et al. Coordinated regulation of virulence during systemic infection of Salmonella enterica serovar Typhimurium. PLoS Pathog 2009;5:e1000306.
16. Herrgard MJ, Lee BS, Portnoy V, et al. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res 2006;16:627-35.
17. Friedman N. Inferring cellular networks using probabilistic graphical models. Science 2004;303:799-805.
18. Gardner TS, di Bernardo D, Lorenz D, et al. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 2003;301:102-5.
19. Segal E, Shapira M, Regev A, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003;34:166-76.
20. Tegner J, Yeung MK, Hasty J, et al. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 2003;100:5944-9.
21. Yeung MK, Tegner J, Collins JJ. Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci USA 2002;99:6163-8.
22. Mwangi MM, Siggia ed. Genome wide identification of regulatory motifs in Bacillus subtilis. BMC Bioinformatics 2003;4:18.
23. Rodionov DA, Dubchak I, Arkin A, et al. Reconstruction of regulatory and metabolic pathways in metal-reducing delta-proteobacteria. Genome Biol 2004;5:R90.
24. Rodionov DA, Dubchak IL, Arkin AP, et al. Dissimilatory metabolism of nitrogen oxides in bacteria: comparative reconstruction of transcriptional networks. PLoS Comput Biol 2005;1:e55.
25. Babu MM, Lang B, Aravind L. Methods to reconstruct and compare transcriptional regulatory networks. Methods Mol Biol 2009;541:163-80.
26. Edwards JS, Covert M, Palsson B. Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 2002;4:133-40.
27. Shlomi T, Eisenberg Y, Sharan R, et al. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol Syst Biol 2007;3:101.
28. Covert MW, Palsson BO. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol 2003;221:309-25.
29. Yizhak K, Benyamini T, Liebermeister W, et al. Integrating quantitative proteomics and metabolomics with a genomescale metabolic network model. Bioinformatics 2010;26: i255-60.
30. van Berlo RJP, de Ridder D, Daran JM, et al. Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM Trans Comput Biol Bioinformatics 2011;8:206-16.
31. Young RA. Biomedical discovery with DNA arrays. Cell 2000;102:9-15.
32. Eisenstein M. Microarrays: quality control. Nature 2006;442:1067-70.
33. Edgar R. Challenge of choosing right level of microarray detail. Nature 2006;443:394.
34. Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001;29:365.
35. Editorial. Microarray standards at last. Nature 2002;419:323.
36. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002;30:207-10.
37. Brazma A, Parkinson H, Sarkans U, et al. Array Express-a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 2003;31:68-71.
38. Velculescu VE, Zhang L, Vogelstein B, et al. Serial analysis of gene expression. Science 1995;270:484-7.
39. Velculescu VE, Zhang L, Zhou W, et al. Characterization of the yeast transcriptome. Cell 1997;88:243-51.
40. Ren B, Robert F, Wyrick JJ, et al. Genome-wide location and function of DNA binding proteins. Science 2000;290:2306-9.
41. Iyer VR, Horak CE, Scafe CS, et al. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 2001;409:533-8.
42. Roh TY, Ngau WC, Cui K, et al. High-resolution genome-wide mapping of histone modifications. Nat Biotechnol 2004;22:1013-6.
43. Kim TH, Ren B. Genome-wide analysis of protein DNA interactions. Annu Rev Genomics Hum Genet 2006;7:81-102.
44. Johnson DS, Mortazavi A, Myers RM, et al. Genome-wide mapping of in vivo protein-DNA interactions. Science 2007;316:1497-502.
45. Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008;320:1344-9.
46. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008;5:621-8.
47. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10:57-63.
48. Herring CD, Raffaelle M, Allen TE, et al. Immobilization of Escherichiacoli RNA polymerase and location of binding sites by use of chromatin immunoprecipitation and microarrays. J Bacteriol 2005;187:6166-74.
49. Wade JT, Roa DC, Grainger DC, et al. Extensive functional overlap between sigma factors in Escherichia coli. Nat Struct Mol Biol 2006;13:806-14.
50. Grainger DC, Hurd D, Goldberg MD, et al. Association of nucleoid proteins with coding and non-coding segments of the Escherichia coli genome. Nucleic Acids Res 2006;34:4642-52.
51. Perkins TT, Kingsley RA, Fookes MC, et al. A strand-specific RNA-Seq analysis of the transcriptome of the typhoid bacillus Salmonella typhi. PLoS Genet 2009;5: e1000569.
52. Croucher NJ, Thomson NR. Studying bacterial transcriptomes using RNA-seq. Curr Opin Microbiol 2010;13:619-24.
53. Price MN, Deutschbauer AM, Kuehl JV, et al. Evidence-based annotation of transcripts and proteins in the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough. J Bacteriol 2011;193:5716-27.
54. Faith JJ, Driscoll ME, Fusaro VA, et al. Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res 2008;36:D866-70.
55. Sherlock G, Hernandez-Boussard T, Kasarskis A, et al. The Stanford Microarray Database. Nucleic Acids Res 2001;29:152-5.
56. Engelen K, Fu Q, Meysman P, et al. COLOMBOS: access port for cross-platform bacterial expression compendia. PLoS One 2011;6:e20938.
57. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 2009;37:D885-90.
58. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: archive for functional genomics data sets-10 years on. Nucleic Acids Res 2011;39:D1005-10.
59. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res 2007;35:D760-5.
60. Faith JJ, Hayete B, Thaden JT, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 2007;5:e8.
61. Overbeek R, Begley T, Butler RM, et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 2005;33:5691-702.
62. Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 2007;23:127-8.
63. Letunic I, Bork P. Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res 2011;39:W475-8.
64. Gama-Castro S, Salgado H, Peralta-Gil M, et al. Regulon DB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units). Nucleic Acids Res 2011;39: D98-105.
65. Keseler IM, Collado-Vides J, Santos-Zavaleta A, et al. EcoCyc: a comprehensive database of Escherichia coli biology. Nucleic Acids Res 2011;39:D583-90.
66. Sierro N, Makita Y, de Hoon M, et al. DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information. Nucleic Acids Res 2008;36:D93-6.
67. Robison K, McGuire AM, Church GM. A comprehensive library of DNA-binding site matrices for 55 proteins applied to the complete Escherichia coli K-12 genome. J Mol Biol 1998;284:241-54.
68. Jacques PE, Gervais AL, Cantin M, et al. MtbRegList, a database dedicated to the analysis of transcriptional regulation in Mycobacterium tuberculosis. Bioinformatics 2005;21:2563-5.
69. Pauling J, Rottger R, Tauch A, et al. CoryneRegNet 6.0- Updated database content, new analysis methods and novel features focusing on community demands. Nucleic Acids Res 2012;40:D610-4.
70. Wu J, Zhao F, Wang S, et al. cTFbase: a database for comparative genomics of transcription factors in cyanobacteria. BMC Genomics 2007;8:104.
71. Perez AG, Angarica VE, Vasconcelos AT, et al. Tractor_DB (version 2.0): a database of regulatory interactions in gamma-proteobacterial genomes. Nucleic Acids Res 2007;35: D132-6.
72. Krawczyk J, Kohl TA, Goesmann A, et al. From Corynebacterium glutamicum to Mycobacterium tuberculosis-towards transfers of gene regulatory networks and integrated data analyses with MycoRegNet. Nucleic Acids Res 2009;37:e97.
73. Pareja E, Pareja-Tobes P, Manrique M, et al. Extra Train: a database of extragenic regions and transcriptional information in prokaryotic organisms. BMC Microbiol 2006;6:29.
74. Wilson D, Charoensawan V, Kummerfeld SK, et al. DBD- taxonomically broad transcription factor predictions: new content and functionality. Nucleic Acids Res 2008;36: D88-92.
75. Kazakov AE, Cipriano MJ, Novichkov PS, et al. RegTransBase-a database of regulatory sequences and interactions in a wide range of prokaryotic genomes. Nucleic Acids Res 2007;35:D407-12.
76. Grote A, Klein J, Retter I, et al. PRODORIC (release 2009): a database and tool platform for the analysis of gene regulation in prokaryotes. Nucleic Acids Res 2009;37: D61-5.
77. Huang HY, Chang HY, Chou CH, et al. sRNAMap: genomic maps for small non-coding RNAs, their regulators and their targets in microbial genomes. Nucleic Acids Res 2009; 37:D150-4.
78. Okuda S, Yoshizawa AC. ODB: a database for operon organizations, 2011 update. Nucleic Acids Res 2011;39: D552-5.
79. Novichkov PS, Laikova ON, Novichkova ES, et al. RegPrecise: a database of curated genomic inferences of transcriptional regulatory interactions in prokaryotes. Nucleic Acids Res 2010;38:D111-8.
80. De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Microbiol 2010;8:717-29.
81. Bansal M, Belcastro V, Ambesi-Impiombato A, et al. How to infer gene networks from expression profiles. Mol Syst Biol 2007;3:78.
82. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 2008;9:770-80.
83. Madan Babu M, Teichmann SA, Aravind L. Evolutionary dynamics of prokaryotic transcriptional regulatory networks. J Mol Biol 2006;358:614-33.
84. Madan Babu M, Teichmann SA. Evolution of transcription factors and the gene regulatory network in Escherichia coli. Nucleic Acids Res 2003;31:1234-44.
85. Teichmann SA, Babu MM. Gene regulatory network growth by duplication. Nat Genet 2004;36:492-6.
86. Gelfand MS. Evolution of transcriptional regulatory networks in microbial genomes. Curr Opin Struct Biol 2006;16:420-9.
87. Lozada-Chavez I, Janga SC, Collado-Vides J. Bacterial regulatory networks are extremely flexible in evolution. Nucleic Acids Res 2006;34:3434-45.
88. Overbeek R, Fonstein M, D'Souza M, et al. The use of gene clusters to infer functional coupling. Proc Natl Acad Sci USA 1999;96:2896-901.
89. Pilpel Y, Sudarsanam P, Church GM. Identifying regulatory networks by combinatorial analysis of promoter elements. Nat Genet 2001;29:153-9.
90. Bar-Joseph Z, Gerber GK, Lee TI, et al. Computational discovery of gene modules and regulatory networks. Nat Biotechnol 2003;21:1337-42.
91. Alkema WB, Lenhard B, Wasserman WW. Regulog analysis: detection of conserved regulatory networks across bacteria: application to Staphylococcus aureus. Genome Res 2004;14:1362-73.
92. Rodionov DA. Comparative genomic reconstruction of transcriptional regulatory networks in bacteria. Chem Rev 2007;107:3467-97.
93. Tompa M, Li N, Bailey TL, et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 2005;23:137-44.
94. Roth FP, Hughes JD, Estep PW, et al. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol 1998;16:939-45.
95. Chen X, Guo LQ, Fan ZC, et al. W-AlignACE: an improved Gibbs sampling algorithm based on more accurate position weight matrices learned from sequence and gene expression/ChIP-chip data. Bioinformatics 2008;24:1121-8.
96. Chen X, Guo L, Fan Z, et al. Learning position weight matrices from sequence and expression data. Comput Syst Bioinformatics Conf 2007;6:249-60.
97. Tan K, Moreno-Hagelsieb G, Collado-Vides J, et al. A comparative genomics approach to prediction of new members of regulons. Genome Res 2001;11:566-84.
98. Ravcheev DA, Rakhmaninova AB, Mironov AA, et al. Comparative genomics analysis of nitrate and nitrite respiration in gamma proteobacteria. Mol Biol 2005;39:832-46.
99. Doroshchuk NA, Gelfand MS, Rodionov DA. Regulation of nitrogen metabolism in gram-positive bacteria. Mol Biol 2006;40:829-36.
100. Novichkov PS, Rodionov DA, Stavrovskaya ED, et al. RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach. Nucleic Acids Res 2010;38:W299-307.
101. Rodionov DA, Novichkov PS, Stavrovskaya ED, et al. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus. BMCGenomics 2011;12(Suppl. 1):S3.
102. Suvorova IA, Tutukina MN, Ravcheev DA, et al. Comparative genomic analysis of the hexuronate metabolism genes and their regulation in gammaproteobacteria. J Bacteriol 2011;193:3956-63.
103. Leyn SA, Li X, Zheng Q, et al. Control of proteobacterial central carbon metabolism by the HexR transcriptional regulator: a case study in Shewanella oneidensis. J Biol Chem 2011;286:35782-94.
104. Oberto J. FITBAR: a web tool for the robust prediction of prokaryotic regulons. BMC Bioinformatics 2010;11:554.
105. McCue L, Thompson W, Carmack C, et al. Phylogenetic footprinting of transcription factor binding sites in proteo-bacterial genomes. Nucleic Acids Res 2001;29:774-82.
106. Su Z, Olman V, Mao F, et al. Comparative genomics analysis of NtcA regulons in cyanobacteria: regulation of nitrogen assimilation and its coupling to photosynthesis. Nucleic Acids Res 2005;33:5156-71.
107. Brazhnik P, de la Fuente A, Mendes P. Gene networks: how to put the function in genomics. Trends Biotechnol 2002;20:467-72.
108. De Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 2002;9:67-103.
109. Stolovitzky G, Monroe D, Califano A. Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann NYAcad Sci 2007;1115:1-22.
110. Marbach D, Prill RJ, Schaffter T, et al. Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci USA 2010;107:6286-91.
111. Stolovitzky G, Prill RJ, Califano A. Lessons from the DREAM2 challenges. Ann NY Acad Sci 2009;1158:159-95.
112. Hache H, Lehrach H, Herwig R. Reverse engineering of gene regulatory networks: a comparative study. EURASIP J Bioinform Syst Biol 2009;617281.
113. Michoel T, De Smet R, Joshi A, et al. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Syst Biol 2009;3:49.
114. Elati M, Rouveirol C. Unsupervised Learning for Gene Regulation Network Inference from Expression Data: A Review. Algorithms in Computational Molecular Biology. John Wiley & Sons, Inc., 2011;955-78.
115. Cloots L, Marchal K. Network-based functional modeling of genomics, transcriptomics and metabolism in bacteria. Curr Opin Microbiol 2011;14:599-607.
116. Cantone I, Marucci L, Iorio F, et al. A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 2009;137:172-81.
117. Gustafsson M, Hornquist M, Lundstrom J, et al. Reverse engineering of gene networks with LASSO and nonlinear basis functions. Ann NYAcad Sci 2009;1158:265-75.
118. di Bernardo D, Thompson MJ, Gardner TS, et al. Chemogenomic profiling on a genomewide scale using reverse-engineered gene networks. Nat Biotechnol 2005;23:377-83.
119. Butte AJ, Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 2000;418-29.
120. Meyer PE, Kontos K, Lafitte F, et al. Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol 2007;79879.
121. Yu J, Smith VA, Wang PP, et al. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 2004;20:3594-603.
122. Friedman N, Linial M, Nachman I, et al. Using Bayesian networks to analyze expression data. J Comput Biol 2000;7:601-20.
123. Liang S, Fuhrman S, Somogyi R. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 1998;18-29.
124. Hache H, Wierling C, Lehrach H, et al. Reconstruction and validation of gene regulatory networks with neural networks. In: Proceedings of the 2nd Foundations of Systems Biology in Engineering Conference, FOSBE 2007. Stuttgart, pp. 319-24.
125. Grimaldi M, Jurman G, Visintainer R. Reverse engineering gene networks with ANN: variability in network inference algorithms. Arxiv preprint arXiv:1009.4824 2010.
126. Rice JJ, Tu Y, Stolovitzky G. Reconstructing biological networks using conditional correlation analysis. Bioinformatics 2005;21:765-73.
127. Butte AJ, Kohane IS. Unsupervised knowledge discovery in medical databases using relevance networks. J Am Med Inform Assoc 1999;711-5.
128. Ihmels J, Friedlander G, Bergmann S, et al. Revealing modular organization in the yeast transcriptional network. Nat Genet 2002;31:370-7.
129. Bonneau R. Learning biological networks: from modules to dynamics. Nat Chem Biol 2008;4:658-64.
130. Reiss DJ, Baliga NS, Bonneau R. Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks. BMC Bioinformatics 2006;7. (Suppl. 1):S7.
131. Margolin AA, Nemenman I, Basso K, et al. ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2006;7.
132. Segal E, Pe'er D, Regev A, et al. Learning module networks. J Mach Learn Res 2005;6:557-88.
133. Joshi A, De Smet R, Marchal K, et al. Module networks revisited: computational assessment and prioritization of model predictions. Bioinformatics 2009;25:490-6.
134. Mordelet F, Vert JP. SIRENE: supervised inference of regulatory networks. Bioinformatics 2008;24:i76-82.
135. Huynh-Thu VA, Irrthum A, Wehenkel L, et al. Inferring regulatory networks from expression data using tree-based methods. PLoS One 2010;5(9):e12776.
136. Bonneau R, Reiss DJ, Shannon P, et al. The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 2006;7:R36.
137. Lemmens K, De Bie T, Dhollander T, et al. DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli. Genome Biol 2009;10:R27.
138. Ernst J, Beg QK, Kay KA, et al. A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli. PLoS Comput Biol 2008;4:e1000044.
139. You ZH, Yin Z, Han K, et al. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. BMC Bioinformatics 2010;11:343.
140. Cerulo L, Elkan C, Ceccarelli M. Learning gene regulatory networks from only positive and unlabeled data. BMC Bioinformatics 2010;11:228.
141. Marbach D, Schaffter T, Mattiussi C, et al. Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol 2009;16:229-39.
142. Prill RJ, Marbach D, Saez-Rodriguez J, et al. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS One 2010;5:e9202.
143. Greenfield A, Madar A, Ostrer H, et al. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models. PLoS One 2010;5:e13397.
144. Watkinson J, Liang KC, Wang XD, et al. Inference of regulatory gene interactions from expression data using three-way mutual information 2009;1158:302-13.
145. Madar A, Greenfield A, Vanden-Eijnden E, et al. DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator. PLoS One 2010; 5(3):e9803.
146. Bonneau R, Reiss DJ, Shannon P, et al. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 2006;7(5):R36.
147. Yip KY, Alexander RP, Yan KK, et al. Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data. PLoS One 2010;5(1):e8121.
148. Zwir I, Huang H, Groisman EA. Analysis of differentially-regulated genes within a regulatory network by GPS genome navigation. Bioinformatics 2005;21:4073-83.
149. Basso K, Margolin AA, Stolovitzky G, et al. Reverse engineering of regulatory networks in human B cells. Nat Genet 2005;37:382-90.
150. Castro-Melchor M, Charaniya S, Karypis G, et al. Genome-wide inference of regulatory networks in Streptomycescoelicolor. BMC Genomics 2010;11:578.
151. Glass K, Ott E, Losert W, et al. Implications of functional similarity for gene regulatory interactions. J R Soc Interface 2012;9(72):1625-36.
152. Yoon H, Ansong C, McDermott JE, et al. Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella. BMC Syst Biol 2011;5:100.
153. Anastassiou D. Computational analysis of the synergy among multiple interacting genes. Mol Syst Biol 2007;3:83.
154. Geurts P, Irrthum A, Wehenkel L. Supervised learning with decision tree-based methods in computational and systems biology. Mol Biosyst 2009;5:1593-605.
155. Balaji S, Babu MM, Aravind L. Interplay between network structures, regulatory modes and sensing mechanisms of transcription factors in the transcriptional regulatory network of E-coil. J Mol Biol 2007;372:1108-22.
156. Schaffter T, Marbach D, Floreano D. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 2011;27:2263-70.
157. Narendra V, Lytkin NI, Aliferis CF, et al. A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks. Genomics 2011;97:7-18.
158. Varner JD. Large-scale prediction of phenotype: concept. Biotechnol Bioeng 2000;69:664-78.
159. Palsson B. The challenges of in silico biology. Nat Biotechnol 2000;18:1147-50.
160. Varma A, Palsson BO. Metabolic flux balancing-basic concepts, scientific and practical use. Biotechnology 1994;12:994-8.
161. Tomita M. Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol 2001;19:205-10.
162. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004;2:886-97.
163. 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.
164. Covert MW, Xiao N, Chen TJ, et al. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008;24:2044-50.
165. Lee JM, Gianchandani EP, Eddy JA, et al. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol 2008;4(5):e1000086.
166. Ovacik MA, Androulakis IP. On the potential for integrating gene expression and metabolic flux data. Curr Bioinformatics 2008;3:142-8.
167. Tenazinha N, Vinga S. A survey on methods for modeling and analyzing integrated biological networks. IEEE/ACM Trans Comput Biol Bioinformatics 2011;8:943-58.
168. Machado D, Costa RS, Rocha M, et al. Modeling formalisms in systems biology. AMB Express 2011;1:45.
169. Price ND, Papin JA, Schilling CH, et al. Genome-scale microbial in silico models: the constraints-based approach. Tends Biotechnol 2003;21:162-9.
170. Llaneras F, Pico J. Stoichiometric modelling of cell metabolism. J Biosci Bioeng 2008;105:1-11.
171. Price ND, Famili I, Beard DA, et al. Extreme pathways and Kirchhofs second law. Biophys J 2002;83:2879-82.
172. Kauffman KJ, Prakash P, Edwards JS. Advances in flux balance analysis. Curr Opin Biotechnol 2003;14:491-6.
173. Covert MW, Palsson BO. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J Biol Chem 2002;277:28058-64.
174. Hohmann S. Osmotic stress signaling and osmoadaptation in yeasts. Microbiol Mol Biol Rev 2002;66(2):300-72.
175. Chandrasekaran S, Price ND. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci USA 2010;107:17845-50.
176. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 2010;11:733-9.
177. Otero JM, Panagiotou G, Olsson L. Fueling industrial biotechnology growth with bioethanol. Adv Biochem Eng Biotechnol 2007;108:1-40.
178. Bro C, Regenberg B, Forster J, et al. In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab Eng 2006;8:102-11.
179. Vilaca P, Rocha I, Rocha M. A computational tool for the simulation and optimization of microbial strains accounting integrated metabolic/regulatory information. Biosystems 2011;103:435-41.
180. Kim J, Reed JL. OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst Biol 2010;4:53.
181. Banta S, Vemula M, Yokoyama T, et al. Contribution of gene expression to metabolic fluxes in hypermetabolic livers induced through burn injury and cecal ligation and puncture in rats. Biotechnol Bioeng 2007;97:118-37.
182. Schilling O, Frick O, Herzberg C, et al. Transcriptional and metabolic responses of Bacillus subtilis to the availability of organic acids: Transcription regulation is important but not sufficient to account for metabolic adaptation. Appl Environ Microbiol 2007;73:499-507.
183. Shlomi T, Cabili MN, Herrgard MJ, et al. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 2008;26:1003-10.
184. Willenbrock H, Ussery DW. Chromatin architecture and gene expression in Escherichia coli. Genome Biol 2004;5:252.
185. Casadesus J, Low D. Epigenetic gene regulation in the bacterial world. Microbiol Mol Biol Rev 2006;70(3):830-56.
186. Rodionov DA, Gelfand MS, Todd JD, et al. Computational reconstruction of iron- and manganese-responsive transcriptional networks in alpha-proteobacteria. PLoS Comput Biol 2006;2:1568-85.
187. Moreno-Campuzano S, Janga SC, Perez-Rueda E. Identification and analysis of DNA-binding transcription factors in Bacillus subtilis and other Firmicutes-a genomic approach. BMC Genomics 2006;7:147.
188. Price MN, Dehal PS, Arkin AP. Horizontal gene transfer and the evolution of transcriptional regulation in Escherichia coli. Genome Biol 2008;9:R4.
189. Balleza E, Lopez-Bojorquez LN, Martinez-Antonio A, et al. Regulation by transcription factors in bacteria: beyond description. FEMS Microbiol Rev 2009;33:133-51.
190. Bornholdt S. Boolean network models of cellular regulation: prospects and limitations. J R Soc Intetf 2008;5: S85-94.
191. Karr JR, Sanghvi JC, Macklin DN, et al. A whole-cell computational model predicts phenotype from genotype. Cell 2012;150:389-401.