Reconstruction of microbial transcriptional regulatory networks

Current Opinion in Biotechnology

Volumn 15, Issue 1, 2004, Pages 70-77

  Herrgård, Markus J ^a   Covert, Markus W ^a   Palsson, Bernhard Ø ^a  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAY REGION BINDING SITE; DATA BASE; GENE CONSTRUCT; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENOME ANALYSIS; LACTOSE OPERON; MATHEMATICAL MODEL; MOLECULAR INTERACTION; PRIORITY JOURNAL; PROKARYOTE; PROMOTER REGION; REVIEW; SACCHAROMYCES CEREVISIAE; TRANSCRIPTION REGULATION;

PROKARYOTA; SACCHAROMYCES CEREVISIAE;

EID: 1242328740     PISSN: 09581669     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.copbio.2003.11.002     Document Type: Review

References (60)

1. Covert M.W., Schilling C.H., Famili I., Edwards J.S., Goryanin I.I., Selkov E., Palsson B.O. Metabolic modeling of microbial strains in silico. Trends. Biochem. Sci. 26:2001;179-186.
2. Ibarra R.U., Edwards J.S., Palsson B.O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature. 420:2002;186-189.
3. Burgard A.P., Maranas C.D. An optimization based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol. Bioeng. 84:2003;647-657.
4. Wyrick J.J., Young R.A. Deciphering gene expression regulatory networks. Curr. Opin. Genet. Dev. 12:2002;130-136.
5. Stormo G.D., Tan K. Mining genome databases to identify and understand new gene regulatory systems. Curr. Opin. Microbiol. 5:2002;149-153.
6. Ptashne M, Gann A: Genes and Signals. Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press; 2002.
7. Buchler N.E., Gerland U., Hwa T. On schemes of combinatorial transcription logic. Proc. Natl. Acad. Sci. USA. 100:2003;5136-5141.
8. Struhl K. Fundamentally different logic of gene regulation in eukaryotes and prokaryotes. Cell. 98:1999;1-4.
9. Cases I., de Lorenzo V., Ouzounis C.A. Transcription regulation and environmental adaptation in bacteria. Trends Microbiol. 11:2003;248-253.
10. Perez-Rueda E., Collado-Vides J. The repertoire of DNA-binding transcriptional regulators in Escherichia coli K-12. Nucleic Acids Res. 28:2000;1838-1847.
11. Shen-Orr S.S., Milo R., Mangan S., Alon U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31:2002;64-68 The authors reconstruct the transcriptional regulatory network in E. coli based on the RegulonDB database as well as the primary literature. They develop algorithms to systematically discover commonly occurring network structural motifs in the reconstructed network. Three such motifs were found and the role of these motifs in network function was further elucidated.
12. Csank C., Costanzo M.C., Hirschman J., Hodges P., Kranz J.E., Mangan M., O'Neill K., Robertson L.S., Skrzypek M.S., Brooks J.et al. Three yeast proteome databases: YPD, PombePD, and CalPD (MycoPathPD). Methods Enzymol. 350:2002;347-373.
13. ••]. The authors also develop methods to automatically reconstruct regulatory networks based on location analysis and gene expression data and apply these methods to the cell-cycle regulatory network. Furthermore, the authors find that different cellular functions are highly interconnected at the level of transcriptional regulation.
14. Madan Babu M., Teichmann S.A. Evolution of transcription factors and the gene regulatory network in Escherichia coli. Nucleic Acids Res. 31:2003;1234-1244.
15. Wray G.A., Hahn M.W., Abouheif E., Balhoff J.P., Pizer M., Rockman M.V., Romano L.A. The evolution of transcriptional regulation in eukaryotes. Mol. Biol. Evol. 20:2003;1377-1419.
16. ••].
17. Kellis M., Patterson N., Endrizzi M., Birren B., Lander E.S. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature. 423:2003;241-254 These two papers describe the sequencing of multiple yeast species closely related to S. cerevisiae and the application of comparative sequence analysis to finding cis regulatory sites in genomes. Putative functions for these motifs were also suggested based on the utilization of GWLA and gene expression data. These studies demonstrate the utility of multiple closely related genome sequences for identifying conserved non-coding regions of the genome and indicate that comparative genomics approaches may be feasible for regulatory network reconstruction.
18. Qin Z.S., McCue L.A., Thompson W., Mayerhofer L., Lawrence C.E., Liu J.S. Identification of co-regulated genes through Bayesian clustering of predicted regulatory binding sites. Nat. Biotechnol. 21:2003;435-439.
19. Chiang D.Y., Moses A.M., Kellis M., Lander E.S., Eisen M.B. Phylogenetically and spatially conserved word pairs associated with gene-expression changes in yeasts. Genome Biol. 4:2003;R43.
20. Wolf D.M., Arkin A.P. Motifs, modules and games in bacteria. Curr. Opin. Microbiol. 6:2003;125-134.
21. Conant G.C., Wagner A. Convergent evolution of gene circuits. Nat. Genet. 34:2003;264-266 The authors evaluate the role of network motif duplication in the evolution of regulatory networks by studying reconstructed regulatory networks in E. coli and yeast. They find that separate instances of a particular network motif in the networks have not evolved by duplication of the genes participating in the motif, pointing towards convergent evolution as the origin of these motifs. The results indicate that the network motifs have an optimal design to perform their function.
22. Kanehisa M., Goto S., Kawashima S., Nakaya A. The KEGG databases at GenomeNet. Nucleic Acids Res. 30:2002;42-46.
23. Schomburg I., Chang A., Schomburg D. BRENDA, enzyme data and metabolic information. Nucleic Acids Res. 30:2002;47-49.
24. Overbeek R., Larsen N., Walunas T., D'Souza M., Pusch G., Selkov E. Jr., Liolios K., Joukov V., Kaznadzey D., Anderson I.et al. The ERGO genome analysis and discovery system. Nucleic Acids Res. 31:2003;164-171.
25. Salgado H., Santos-Zavaleta A., Gama-Castro S., Millán- Zárate D., Díaz-Peredo E., Sánchez-Solano F., Pérez-Rueda E., Bonavides-Martínez C., Collado-Vides J. RegulonDB (version 3.2): transcriptional regulation and operon organization in Escherichia coli K-12. Nucleic Acids Res. 29:2001;72-74.
26. Zhu J., Zhang M.Q. SCPD: a promoter database of the yeast Saccharomyces cerevisiae. Bioinformatics. 15:1999;607-611.
27. Matys V., Fricke E., Geffers R., Gossling E., Haubrock M., Hehl R., Hornischer K., Karas D., Kel A.E., Kel-Margoulis O.V.et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31:2003;374-378.
28. Covert M.W., Palsson B.O. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J. Biol. Chem. 277:2002;28058-28064 This article describes the reconstruction of a transcriptionally regulated model of E. coli core metabolism and its application to predicting and interpreting gene deletion phenotypes as well as dynamic metabolic shifts. The integrated model was found to improve the predictive capability of constraint-based models of biochemical networks. The modeling approach presented in this paper presents a scalable framework to build genome-scale constraint-based models of integrated regulatory/metabolic network function.
29. Stitt M., Fernie A.R. From measurements of metabolites to metabolomics: an 'on the fly' perspective illustrated by recent studies of carbon-nitrogen interactions. Curr. Opin. Biotechnol. 14:2003;136-144.
30. Sanford K., Soucaille P., Whited G., Chotani G. Genomics to fluxomics and physiomics - pathway engineering. Curr. Opin. Microbiol. 5:2002;318-322.
31. Holloway A.J., van Laar R.K., Tothill R.W., Bowtell D.D. Options available - from start to finish - for obtaining data from DNA microarrays II. Nat Genet. 32(Suppl):2002;481-489.
32. Quackenbush J. Microarray data normalization and transformation. Nat. Genet. 32(Suppl):2002;496-501.
33. Churchill G.A. Fundamentals of experimental design for cDNA microarrays. Nat. Genet. 32(Suppl):2002;490-495.
34. Ihmels J., Friedlander G., Bergmann S., Sarig O., Ziv Y., Barkai N. Revealing modular organization in the yeast transcriptional network. Nat. Genet. 31:2002;370-377.
35. •].
36. Wang W., Cherry J.M., Botstein D., Li H. A systematic approach to reconstructing transcription networks in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA. 99:2002;16893-16898 These two articles introduce statistical methods for reconstructing regulatory network modules (defined as genes co-regulated by one or more TFs) based on gene expression and promoter sequence data. Both approaches also allow identification of the conditions under which particular TFs regulate the activity of particular genes. This represents an important advance over previous approaches for data-driven network reconstruction, which typically assumed that the same set of TFs would regulate each gene under all conditions.
37. Hughes T.R., Marton M.J., Jones A.R., Roberts C.J., Stoughton R., Armour C.D., Bennett H.A., Coffey E., Dai H., He Y.D.et al. Functional discovery via a compendium of expression profiles. Cell. 102:2000;109-126.
38. Ideker T., Thorsson V., Ranish J.A., Christmas R., Buhler J., Eng J.K., Bumgarner R., Goodlett D.R., Aebersold R., Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 292:2001;929-934.
39. Oshima T., Aiba H., Masuda Y., Kanaya S., Sugiura M., Wanner B.L., Mori H., Mizuno T. Transcriptome analysis of all two-component regulatory system mutants of Escherichia coli K-12. Mol. Microbiol. 46:2002;281-291.
40. Ren B., Robert F., Wyrick J.J., Aparicio O., Jennings E.G., Simon I., Zeitlinger J., Schreiber J., Hannett N., Kanin E.et al. Genome-wide location and function of DNA binding proteins. Science. 290:2000;2306-2309.
41. Iyer V.R., Horak C.E., Scafe C.S., Botstein D., Snyder M., Brown P.O. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature. 409:2001;533-538.
42. Laub M.T., Chen S.L., Shapiro L., McAdams H.H. Genes directly controlled by CtrA, a master regulator of the Caulobacter cell cycle. Proc. Natl. Acad. Sci. USA. 99:2002;4632-4637.
43. Zeitlinger J., Simon I., Harbison C.T., Hannett N.M., Volkert T.L., Fink G.R., Young R.A. Program-specific distribution of a transcription factor dependent on partner transcription factor and MAPK signaling. Cell. 113:2003;395-404
44. Hartemink AJ, Gifford DK, Jaakkola TS, Young RA: Combining location and expression data for principled discovery of genetic regulatory network models. Pac Symp Biocomput 2002:437-449.
45. Liu X.S., Brutlag D.L., Liu J.S. An algorithm for finding protein DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat. Biotechnol. 20:2002;835-839.
46. Herrgard M.J., Covert M.W., Palsson B.O. Reconciling gene expression data with genome-scale regulatory network structures. Genome Res. 13:2003;2423-2434 These papers describe the first comprehensive efforts to evaluate the agreement between known regulatory network structures reconstructed based on database and literature information and large-scale gene expression data sets. The results can be used to evaluate both the completeness of the reconstructed network structures and the utility of the publicly available expression data collections for expanding these networks.
47. •].
48. •].
49. de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9:2002;67-103.
50. Bolouri H., Davidson E.H. Modeling transcriptional regulatory networks. Bioessays. 24:2002;1118-1129.
51. Guelzim N., Bottani S., Bourgine P., Kepes F. Topological and causal structure of the yeast transcriptional regulatory network. Nat. Genet. 31:2002;60-63.
52. Ronen M., Rosenberg R., Shraiman B.I., Alon U. Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc. Natl. Acad. Sci. USA. 99:2002;10555-10560.
53. Gardner T.S., di Bernardo D., Lorenz D., Collins J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science. 301:2003;102-105 The authors present a scalable method to identify linear models of regulatory networks from gene expression (quantitative PCR) data and an application to the SOS regulatory network in E. coli. There are some differences between the network structure identified from data and the known SOS network structure, most likely because of the relatively high noise level in the experiments. Despite differences in the network structure, the model was found to have good predictive power for predicting the primary target of a drug action based on gene expression data.
54. Tyson J.J., Chen K.C., Novak B. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell. Biol. 15:2003;221-231.
55. Rao C.V., Wolf D.M., Arkin A.P. Control, exploitation and tolerance of intracellular noise. Nature. 420:2002;231-237.
56. Hasty J., McMillen D., Collins J.J. Engineered gene circuits. Nature. 420:2002;224-230.
57. Davidson E.H., Rast J.P., Oliveri P., Ransick A., Calestani C., Yuh C.H., Minokawa T., Amore G., Hinman V., Arenas-Mena C.et al. A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo. Dev. Biol. 246:2002;162-190.
58. Pe'er D., Regev A., Elidan G., Friedman N. Inferring subnetworks from perturbed expression profiles. Bioinformatics. 17:2001;S215-S224.
59. Covert M.W., Schilling C.H., Palsson B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213:2001;73-88.
60. ••] and to a new dataset for 14 TFs in rapamycin-treated cells. Application of the proposed method to these datasets reveals a number of unexpected connections between different regulatory subnetworks.