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Quantitative Biology

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Quant. Biol.    2014, Vol. 2 Issue (1) : 30-46    https://doi.org/10.1007/s40484-014-0027-5
REVIEW
The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network
Guodong Liu1,Antonio Marras1,Jens Nielsen1,2,*()
1. Novo Nordisk Foundation Center for Biosustainability, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg SE41296, Sweden
2. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, H?rsholm DK2970, Denmark
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Abstract

Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model. While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate transcriptional regulatory interactions to genome-scale metabolic models in a quantitative manner.
Keywords transcriptional regulatory network      metabolic model      Saccharomyces cerevisiae      integration     
Corresponding Author(s): Jens Nielsen   
Issue Date: 25 June 2014
 Cite this article:   
Guodong Liu,Antonio Marras,Jens Nielsen. The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network[J]. Quant. Biol., 2014, 2(1): 30-46.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-014-0027-5
https://academic.hep.com.cn/qb/EN/Y2014/V2/I1/30
Fig.1  Transcription factors involved in the regulation of carbon metabolism in S. cerevisiae. Data were collected from YEASTRACT [11] and SGD [12] databases. Different metabolic pathways are shown in different colors, except those for non-fermentable carbon source utilization (13 to 18) sharing dark grey. Abbreviations: G6P,?glucose?6-phosphate; GA3P, glyceraldehyde-3-phosphate; AcH, acetaldehyde; AcCoA, acetyl-CoA; OA, oxaloacetate
Fig.2  Strategies for the reconstruction of a functional TRN in S. cerevisiae
Fig.3  Examples of TRN reconstructions in S. cerevisiae. Abbreviations: PL, primary literature; DB, databases; HC, high-throughput ChIP-based experiment data; GE, gene expression profiling data; PF, phylogenetic footprinting. (A) data not reported; (B) total number of TFs and target genes; (C) data in October 2011
Fig.4  The constraint-based approach for metabolism modeling
Fig.5  A simple integrated metabolic model (A) and its representation with a Boolean formalism (B)
Fig.6  The reduction of the solution space of the metabolic network using regulatory constraints
1 Osterlund, T., Nookaew, I. and Nielsen, J. (2012) Fifteen years of large scale metabolic modeling of yeast: developments and impacts. Biotechnol. Adv., 30, 979–988
pmid: 21846501
2 Herrg?rd, M. J., Covert, M. W. and Palsson, B. O. (2004) Reconstruction of microbial transcriptional regulatory networks. Curr. Opin. Biotechnol., 15, 70–77
pmid: 15102470
3 Blais, A. and Dynlacht, B. D. (2005) Constructing transcriptional regulatory networks. Genes Dev., 19, 1499–1511
pmid: 15998805
4 Chua, G., Robinson, M. D., Morris, Q. and Hughes, T. R. (2004) Transcriptional networks: reverse-engineering gene regulation on a global scale. Curr. Opin. Microbiol., 7, 638–646
pmid: 15556037
5 Feist, A. M., Herrg?rd, M. J., Thiele, I., Reed, J. L. and Palsson, B. O. (2009) Reconstruction of biochemical networks in microorganisms. Nat. Rev. Microbiol., 7, 129–143
pmid: 19116616
6 Hughes, T. R. and de Boer, C. G. (2013) Mapping yeast transcriptional networks. Genetics, 195, 9–36
pmid: 24018767
7 Kim, T. M. and Park, P. J. (2011) Advances in analysis of transcriptional regulatory networks. Wiley Interdiscip. Rev. Syst. Biol. Med., 3, 21–35
pmid: 21069662
8 Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J. and Stolovitzky, G., and the DREAM5 Consortium. (2012) Wisdom of crowds for robust gene network inference. Nat. Methods, 9, 796–804
pmid: 22796662
9 Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., . (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298, 799–804
pmid: 12399584
10 Chubukov, V., Uhr, M., Le Chat, L., Kleijn, R. J., Jules, M., Link, H., Aymerich, S., Stelling, J. and Sauer, U. (2013) Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis. Mol. Syst. Biol., 9, 709
pmid: 24281055
11 Abdulrehman, D., Monteiro, P. T., Teixeira, M. C., Mira, N. P., Louren?o, A. B., dos Santos, S. C., Cabrito, T. R., Francisco, A. P., Madeira, S. C., Aires, R. S., . (2011) YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface. Nucleic Acids Res., 39, D136–D140
pmid: 20972212
12 Cherry, J. M., Hong, E. L., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E. T., Christie, K. R., Costanzo, M. C., Dwight, S. S., Engel, S. R., . (2012) Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res., 40, D700–D705
pmid: 22110037
13 Broach, J. R. (2012) Nutritional control of growth and development in yeast. Genetics, 192, 73–105
pmid: 22964838
14 Ljungdahl, P. O. and Daignan-Fornier, B. (2012) Regulation of amino acid, nucleotide, and phosphate metabolism in Saccharomyces cerevisiae. Genetics, 190, 885–929
pmid: 22419079
15 Daran-Lapujade, P., Rossell, S., van Gulik, W. M., Luttik, M. A., de Groot, M. J., Slijper, M., Heck, A. J., Daran, J. M., de Winde, J. H., Westerhoff, H. V., . (2007) The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proc. Natl. Acad. Sci. U.S.A., 104, 15753–15758
pmid: 17898166
16 Daran-Lapujade, P., Jansen, M. L., Daran, J. M., van Gulik, W., de Winde, J. H. and Pronk, J. T. (2004) Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study. J. Biol. Chem., 279, 9125–9138
pmid: 14630934
17 Fendt, S. M., Oliveira, A. P., Christen, S., Picotti, P., Dechant, R. C. and Sauer, U. (2010) Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast. Mol. Syst. Biol., 6, 432
pmid: 21119627
18 Moxley, J. F., Jewett, M. C., Antoniewicz, M. R., Villas-Boas, S. G., Alper, H., Wheeler, R. T., Tong, L., Hinnebusch, A. G., Ideker, T., Nielsen, J., . (2009) Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc. Natl. Acad. Sci. U.S.A., 106, 6477–6482
pmid: 19346491
19 ?sterlund, T., Nookaew, I., Bordel, S. and Nielsen, J. (2013) Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling. BMC Syst. Biol., 7, 36
pmid: 23631471
20 Bordel, S., Agren, R. and Nielsen, J. (2010) Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLOS Comput. Biol., 6, e1000859
pmid: 20657658
21 Klein, C. J., Rasmussen, J. J., R?nnow, B., Olsson, L. and Nielsen, J. (1999) Investigation of the impact of MIG1 and MIG2 on the physiology of Saccharomyces cerevisiae. J. Biotechnol., 68, 197–212
pmid: 10194857
22 Marczak, J. E. and Brandriss, M. C. (1989) Isolation of constitutive mutations affecting the proline utilization pathway in Saccharomyces cerevisiae and molecular analysis of the PUT3 transcriptional activator. Mol. Cell. Biol., 9, 4696–4705
pmid: 2689861
23 Hedges, D., Proft, M. and Entian, K. D. (1995) CAT8, a new zinc cluster-encoding gene necessary for derepression of gluconeogenic enzymes in the yeast Saccharomyces cerevisiae. Mol. Cell. Biol., 15, 1915–1922
pmid: 7891685
24 Lo, T. L., Qu, Y., Uwamahoro, N., Quenault, T., Beilharz, T. H. and Traven, A. (2012) The mRNA decay pathway regulates the expression of the Flo11 adhesin and biofilm formation in Saccharomyces cerevisiae. Genetics, 191, 1387–1391
pmid: 22595243
25 Hinnebusch, A. G. (1984) Evidence for translational regulation of the activator of general amino acid control in yeast. Proc. Natl. Acad. Sci. U.S.A., 81, 6442–6446
pmid: 6387704
26 Turner, G. C., Du, F. and Varshavsky, A. (2000) Peptides accelerate their uptake by activating a ubiquitin-dependent proteolytic pathway. Nature, 405, 579–583
pmid: 10850718
27 Andréasson, C. and Ljungdahl, P. O. (2002) Receptor-mediated endoproteolytic activation of two transcription factors in yeast. Genes Dev., 16, 3158–3172
pmid: 12502738
28 Cox, K. H., Tate, J. J. and Cooper, T. G. (2002) Cytoplasmic compartmentation of Gln3 during nitrogen catabolite repression and the mechanism of its nuclear localization during carbon starvation in Saccharomyces cerevisiae. J. Biol. Chem., 277, 37559–37566
pmid: 12140287
29 Sellick, C. A. and Reece, R. J. (2003) Modulation of transcription factor function by an amino acid: activation of Put3p by proline. EMBO J., 22, 5147–5153
pmid: 14517252
30 Pannala, V. R., Bhat, P. J., Bhartiya, S. and Venkatesh, K. V. (2010) Systems biology of GAL regulon in Saccharomyces cerevisiae.Wiley Interdiscip. Rev. Syst. Biol. Med., 2, 98–106
pmid: 20836013
31 Kornitzer, D., Raboy, B., Kulka, R. G. and Fink, G. R. (1994) Regulated degradation of the transcription factor Gcn4. EMBO J., 13, 6021–6030
pmid: 7813440
32 Soontorngun, N., Larochelle, M., Drouin, S., Robert, F. and Turcotte, B. (2007) Regulation of gluconeogenesis in Saccharomyces cerevisiae is mediated by activator and repressor functions of Rds2. Mol. Cell. Biol., 27, 7895–7905
pmid: 17875938
33 Martens, J. A., Wu, P. Y. and Winston, F. (2005) Regulation of an intergenic transcript controls adjacent gene transcription in Saccharomyces cerevisiae. Genes Dev., 19, 2695–2704
pmid: 16291644
34 Ambroziak, J. and Henry, S. A. (1994) INO2 and INO4 gene products, positive regulators of phospholipid biosynthesis in Saccharomyces cerevisiae, form a complex that binds to the INO1 promoter. J. Biol. Chem., 269, 15344–15349
pmid: 8195172
35 Blaiseau, P. L., Isnard, A. D., Surdin-Kerjan, Y. and Thomas, D. (1997) Met31p and Met32p, two related zinc finger proteins, are involved in transcriptional regulation of yeast sulfur amino acid metabolism. Mol. Cell. Biol., 17, 3640–3648
pmid: 9199298
36 Georis, I., Feller, A., Vierendeels, F. and Dubois, E. (2009) The yeast GATA factor Gat1 occupies a central position in nitrogen catabolite repression-sensitive gene activation. Mol. Cell. Biol., 29, 3803–3815
pmid: 19380492
37 Babu, M. M., Luscombe, N. M., Aravind, L., Gerstein, M. and Teichmann, S. A. (2004) Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol., 14, 283–291
pmid: 15193307
38 Lavoie, H., Hogues, H. and Whiteway, M. (2009) Rearrangements of the transcriptional regulatory networks of metabolic pathways in fungi. Curr. Opin. Microbiol., 12, 655–663
pmid: 19875326
39 Agren, R., Liu, L., Shoaie, S., Vongsangnak, W., Nookaew, I. and Nielsen, J. (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLOS Comput. Biol., 9, e1002980
pmid: 23555215
40 Geertz, M. and Maerkl, S. J. (2010) Experimental strategies for studying transcription factor-DNA binding specificities. Brief Funct. Genomics, 9, 362–373
pmid: 20864494
41 Xie, Z., Hu, S., Qian, J., Blackshaw, S. and Zhu, H. (2011) Systematic characterization of protein-DNA interactions. Cell. Mol. Life Sci., 68, 1657–1668
pmid: 21207099
42 Garner, M. M. and Revzin, A. (1981) A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the Escherichia coli lactose operon regulatory system. Nucleic Acids Res., 9, 3047–3060
pmid: 6269071
43 Galas, D. J. and Schmitz, A. (1978) DNAse footprinting: a simple method for the detection of protein-DNA binding specificity. Nucleic Acids Res., 5, 3157–3170
pmid: 212715
44 Ren, B., Robert, F., Wyrick, J. J., Aparicio, O., Jennings, E. G., Simon, I., Zeitlinger, J., Schreiber, J., Hannett, N., Kanin, E., . (2000) Genome-wide location and function of DNA binding proteins. Science, 290, 2306–2309
pmid: 11125145
45 Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J., Macisaac, K. D., Danford, T. W., Hannett, N. M., Tagne, J. B., Reynolds, D. B., Yoo, J., . (2004) Transcriptional regulatory code of a eukaryotic genome. Nature, 431, 99–104
pmid: 15343339
46 Venters, B. J., Wachi, S., Mavrich, T. N., Andersen, B. E., Jena, P., Sinnamon, A. J., Jain, P., Rolleri, N. S., Jiang, C., Hemeryck-Walsh, C., . (2011) A comprehensive genomic binding map of gene and chromatin regulatory proteins in Saccharomyces. Mol. Cell, 41, 480–492
pmid: 21329885
47 Park, P. J. (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet., 10, 669–680
pmid: 19736561
48 Rhee, H. S. and Pugh, B. F. (2011) Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell, 147, 1408–1419
pmid: 22153082
49 Lickwar, C. R., Mueller, F., Hanlon, S. E., McNally, J. G. and Lieb, J. D. (2012) Genome-wide protein-DNA binding dynamics suggest a molecular clutch for transcription factor function. Nature, 484, 251–255
pmid: 22498630
50 Mirzaei, H., Knijnenburg, T. A., Kim, B., Robinson, M., Picotti, P., Carter, G. W., Li, S., Dilworth, D. J., Eng, J. K., Aitchison, J. D., . (2013) Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins. Proc. Natl. Acad. Sci. U.S.A., 110, 3645–3650
pmid: 23388641
51 MacQuarrie, K. L., Fong, A. P., Morse, R. H. and Tapscott, S. J. (2011) Genome-wide transcription factor binding: beyond direct target regulation. Trends Genet., 27, 141–148
pmid: 21295369
52 Nookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M. and Nielsen, J. (2012) A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res., 40, 10084–10097
pmid: 22965124
53 Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., Véronneau, S., Dow, S., Lucau-Danila, A., Anderson, K., André, B., . (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature, 418, 387–391
pmid: 12140549
54 Hu, Z., Killion, P. J. and Iyer, V. R. (2007) Genetic reconstruction of a functional transcriptional regulatory network. Nat. Genet., 39, 683–687
pmid: 17417638
55 Reimand, J., Vaquerizas, J. M., Todd, A. E., Vilo, J. and Luscombe, N. M. (2010) Comprehensive reanalysis of transcription factor knockout expression data in Saccharomyces cerevisiae reveals many new targets. Nucleic Acids Res., 38, 4768–4777
pmid: 20385592
56 Chua, G., Morris, Q. D., Sopko, R., Robinson, M. D., Ryan, O., Chan, E. T., Frey, B. J., Andrews, B. J., Boone, C. and Hughes, T. R. (2006) Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl. Acad. Sci. U.S.A., 103, 12045–12050
pmid: 16880382
57 Tang, L., Liu, X. and Clarke, N. D. (2006) Inferring direct regulatory targets from expression and genome location analyses: a comparison of transcription factor deletion and overexpression. BMC Genomics, 7, 215
pmid: 16923194
58 Gitter, A., Siegfried, Z., Klutstein, M., Fornes, O., Oliva, B., Simon, I. and Bar-Joseph, Z. (2009) Backup in gene regulatory networks explains differences between binding and knockout results. Mol. Syst. Biol., 5, 276
pmid: 19536199
59 Reiss, D. J., Baliga, N. S. and Bonneau, R. (2006) Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks. BMC Bioinformatics, 7, 280
pmid: 16749936
60 Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D. and Friedman, N. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet., 34, 166–176
pmid: 12740579
61 Hibbs, M. A., Hess, D. C., Myers, C. L., Huttenhower, C., Li, K. and Troyanskaya, O. G. (2007) Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics, 23, 2692–2699
pmid: 17724061
62 Wu, M. and Chan, C. (2012) Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data. Brief Bioinformatics, 13, 150–161
pmid: 21622543
63 Beer, M. A. and Tavazoie, S. (2004) Predicting gene expression from sequence. Cell, 117, 185–198
pmid: 15084257
64 Badis, G., Chan, E. T., van Bakel, H., Pena-Castillo, L., Tillo, D., Tsui, K., Carlson, C. D., Gossett, A. J., Hasinoff, M. J., Warren, C. L., . (2008) A library of yeast transcription factor motifs reveals a widespread function for Rsc3 in targeting nucleosome exclusion at promoters. Mol. Cell, 32, 878–887
pmid: 19111667
65 Zhu, C., Byers, K. J., McCord, R. P., Shi, Z., Berger, M. F., Newburger, D. E., Saulrieta, K., Smith, Z., Shah, M. V., Radhakrishnan, M., . (2009) High-resolution DNA-binding specificity analysis of yeast transcription factors. Genome Res., 19, 556–566
pmid: 19158363
66 Fordyce, P. M., Gerber, D., Tran, D., Zheng, J., Li, H., DeRisi, J. L. and Quake, S. R. (2010) De novo identification and biophysical characterization of transcription-factor binding sites with microfluidic affinity analysis. Nat. Biotechnol., 28, 970–975
pmid: 20802496
67 Ho, S. W., Jona, G., Chen, C. T., Johnston, M. and Snyder, M. (2006) Linking DNA-binding proteins to their recognition sequences by using protein microarrays. Proc. Natl. Acad. Sci. U.S.A., 103, 9940–9945
pmid: 16785442
68 Roth, F. P., Hughes, J. D., Estep, P. W. and Church, G. M. (1998) Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol., 16, 939–945
pmid: 9788350
69 Hughes, J. D., Estep, P. W., Tavazoie, S. and Church, G. M. (2000) Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J. Mol. Biol., 296, 1205–1214
pmid: 10698627
70 Cliften, P., Sudarsanam, P., Desikan, A., Fulton, L., Fulton, B., Majors, J., Waterston, R., Cohen, B. A. and Johnston, M. (2003) Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science, 301, 71–76
pmid: 12775844
71 Kellis, M., Patterson, N., Endrizzi, M., Birren, B. and Lander, E. S. (2003) Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature, 423, 241–254
pmid: 12748633
72 Gordan, R., Murphy, K. F., McCord, R. P., Zhu, C., Vedenko, A. and Bulyk, M. L. (2011) Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights. Genome Biol., 12, R125
pmid: 22189060
73 de Boer, C. G. and Hughes, T. R. (2012) YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities. Nucleic Acids Res., 40, D169–D179
pmid: 22102575
74 Spivak, A. T. and Stormo, G. D. (2012) ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species. Nucleic Acids Res., 40, D162–D168
pmid: 22140105
75 Zhu, J. and Zhang, M. Q. (1999) SCPD: a promoter database of the yeast Saccharomyces cerevisiae. Bioinformatics, 15, 607–611
pmid: 10487868
76 Costanzo, M. C., Engel, S. R., Wong, E. D., Lloyd, P., Karra, K., Chan, E. T., Weng, S., Paskov, K. M., Roe, G. R., Binkley, G., . (2014) Saccharomyces genome database provides new regulation data. Nucleic Acids Res., 42, D717–D725
pmid: 24265222
77 Liu, X., Jessen, W. J., Sivaganesan, S., Aronow, B. J. and Medvedovic, M. (2007) Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. BMC Bioinformatics, 8, 283
pmid: 17683565
78 Bar-Joseph, Z., Gerber, G. K., Lee, T. I., Rinaldi, N. J., Yoo, J. Y., Robert, F., Gordon, D. B., Fraenkel, E., Jaakkola, T. S., Young, R. A., . (2003) Computational discovery of gene modules and regulatory networks. Nat. Biotechnol., 21, 1337–1342
pmid: 14555958
79 Gao, F., Foat, B. C. and Bussemaker, H. J. (2004) Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics, 5, 31
pmid: 15113405
80 Kato, M., Hata, N., Banerjee, N., Futcher, B. and Zhang, M. Q. (2004) Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biol., 5, R56
pmid: 15287978
81 Youn, A., Reiss, D. J. and Stuetzle, W. (2010) Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model. Bioinformatics, 26, 1879–1886
pmid: 20525821
82 Novershtern, N., Regev, A. and Friedman, N. (2011) Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinformatics, 27, i177–i185
pmid: 21685068
83 Haverty, P. M., Hansen, U. and Weng, Z. (2004) Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification. Nucleic Acids Res., 32, 179–188
pmid: 14704355
84 McCord, R. P., Berger, M. F., Philippakis, A. A. and Bulyk, M. L. (2007) Inferring condition-specific transcription factor function from DNA binding and gene expression data. Mol. Syst. Biol., 3, 100
pmid: 17437025
85 Xing, B. and van der Laan, M. J. (2005) A statistical method for constructing transcriptional regulatory networks using gene expression and sequence data. J. Comput. Biol., 12, 229–246
pmid: 15767778
86 Wang, W., Cherry, J. M., Botstein, D. and Li, H. (2002) A systematic approach to reconstructing transcription networks in Saccharomycescerevisiae. Proc. Natl. Acad. Sci. U.S.A., 99, 16893–16898
pmid: 12482955
87 Chang, L. W., Nagarajan, R., Magee, J. A., Milbrandt, J. and Stormo, G. D. (2006) A systematic model to predict transcriptional regulatory mechanisms based on overrepresentation of transcription factor binding profiles. Genome Res., 16, 405–413
pmid: 16449500
88 Qin, J., Li, M. J., Wang, P., Zhang, M. Q. and Wang, J. (2011) ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor. Nucleic Acids Res., 39, W430–436
pmid: 21586587
89 Chen, G., Jensen, S. T. and Stoeckert, C. J. Jr. (2007) Clustering of genes into regulons using integrated modeling-COGRIM. Genome Biol., 8, R4
pmid: 17204163
90 Lemmens, K., Dhollander, T., De Bie, T., Monsieurs, P., Engelen, K., Smets, B., Winderickx, J., De Moor, B. and Marchal, K. (2006) Inferring transcriptional modules from ChIP-chip, motif and microarray data. Genome Biol., 7, R37
pmid: 16677396
91 Workman, C. T., Mak, H. C., McCuine, S., Tagne, J. B., Agarwal, M., Ozier, O., Begley, T. J., Samson, L. D. and Ideker, T. (2006) A systems approach to mapping DNA damage response pathways. Science, 312, 1054–1059
pmid: 16709784
92 Herrg?rd, M. J., Covert, M. W. and Palsson, B. O. (2003) Reconciling gene expression data with known genome-scale regulatory network structures. Genome Res., 13, 2423–2434
pmid: 14559784
93 Machado, D., Costa, R. S., Rocha, M., Ferreira, E. C., Tidor, B. and Rocha, I. (2011) Modeling formalisms in Systems Biology. AMB Express, 1, 45
pmid: 22141422
94 Schlitt, T. and Brazma, A. (2007) Current approaches to gene regulatory network modelling. BMC Bioinformatics, 8, S9
pmid: 17903290
95 Karlebach, G. and Shamir, R. (2008) Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol., 9, 770–780
pmid: 18797474
96 de Jong, H. (2002) Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol., 9, 67–103
pmid: 11911796
97 Glass, L. and Kauffman, S. A. (1973) The logical analysis of continuous, non-linear biochemical control networks. J. Theor. Biol., 39, 103–129
pmid: 4741704
98 Kauffman, S., Peterson, C., Samuelsson, B. and Troein, C. (2003) Random Boolean network models and the yeast transcriptional network. Proc. Natl. Acad. Sci. U.S.A., 100, 14796–14799
pmid: 14657375
99 Gianchandani, E. P., Papin, J. A., Price, N. D., Joyce, A. R. and Palsson, B. O. (2006) Matrix formalism to describe functional states of transcriptional regulatory systems. PLOS Comput. Biol., 2, e101
pmid: 16895435
100 Yu, H., Luscombe, N. M., Qian, J. and Gerstein, M. (2003) Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet., 19, 422–427
pmid: 12902159
101 Luscombe, N. M., Babu, M. M., Yu, H., Snyder, M., Teichmann, S. A. and Gerstein, M. (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature, 431, 308–312
pmid: 15372033
102 Jothi, R., Balaji, S., Wuster, A., Grochow, J. A., Gsponer, J., Przytycka, T. M., Aravind, L. and Babu, M. M. (2009) Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture. Mol. Syst. Biol., 5, 294
pmid: 19690563
103 Herrg?rd, M. J., Lee, B. S., Portnoy, V. and Palsson, B. O. (2006) Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res., 16, 627–635
pmid: 16606697
104 Maslov, S. and Sneppen, K. (2002) Specificity and stability in topology of protein networks. Science, 296, 910–913
pmid: 11988575
105 Guelzim, N., Bottani, S., Bourgine, P. and Képès, F. (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nat. Genet., 31, 60–63
pmid: 11967534
106 Balaji, S., Iyer, L. M., Aravind, L. and Babu, M. M. (2006) Uncovering a hidden distributed architecture behind scale-free transcriptional regulatory networks. J. Mol. Biol., 360, 204–212
pmid: 16730024
107 Guzmán-Vargas, L. and Santillán, M. (2008) Comparative analysis of the transcription-factor gene regulatory networks of E. coli and S. cerevisiae. BMC Syst. Biol., 2, 13
pmid: 18237429
108 Yu, H. and Gerstein, M. (2006) Genomic analysis of the hierarchical structure of regulatory networks. Proc. Natl. Acad. Sci. U.S.A., 103, 14724–14731
pmid: 17003135
109 Lewis, N. E., Nagarajan, H. and Palsson, B. O. (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol., 10, 291–305
pmid: 22367118
110 Schellenberger, J., Que, R., Fleming, R. M., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., . (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc., 6, 1290–1307
pmid: 21886097
111 Thiele, I. and Palsson, B. O. (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc., 5, 93–121
pmid: 20057383
112 Chang, A., Scheer, M., Grote, A., Schomburg, I. and Schomburg, D. (2009) BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res., 37, D588–D592
pmid: 18984617
113 Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K. F., Itoh, M., Kawashima, S., Katayama, T., Araki, M. and Hirakawa, M. (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res., 34, D354–D357
pmid: 16381885
114 Herrg?rd, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., . (2008) A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat. Biotechnol., 26, 1155–1160
pmid: 18846089
115 Nookaew, I., Jewett, M. C., Meechai, A., Thammarongtham, C., Laoteng, K., Cheevadhanarak, S., Nielsen, J. and Bhumiratana, S. (2008) The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. BMC Syst. Biol., 2, 71
pmid: 18687109
116 Orth, J. D., Thiele, I. and Palsson, B. O. (2010) What is flux balance analysis? Nat. Biotechnol., 28, 245–248
pmid: 20212490
117 Price, N. D., Reed, J. L. and Palsson, B. O. (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol., 2, 886–897
pmid: 15494745
118 Feist, A. M. and Palsson, B. O. (2010) The biomass objective function. Curr. Opin. Microbiol., 13, 344–349
pmid: 20430689
119 Edwards, J. S. and Palsson, B. O. (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl. Acad. Sci. U.S.A., 97, 5528–5533
pmid: 10805808
120 Covert, M. W., Schilling, C. H. and Palsson, B. (2001) Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol., 213, 73–88
pmid: 11708855
121 Covert, M. W. and Palsson, B. O. (2002) Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J. Biol. Chem., 277, 28058–28064
pmid: 12006566
122 Lee, S., Phalakornkule, C., Domach, M. M. and Grossmann, I. E. (2000) Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput. Chem. Eng., 24, 711–716.
123 Mahadevan, R. and Schilling, C. H. (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng., 5, 264–276
pmid: 14642354
124 Snitkin, E. S. and Segrè, D. (2008) Optimality criteria for the prediction of metabolic fluxes in yeast mutants. Genome inform., 20, 123–134.
125 Llaneras, F. and Picó, J. (2008) Stoichiometric modelling of cell metabolism. J. Biosci. Bioeng., 105, 1–11
pmid: 18295713
126 Covert, M. W. and Palsson, B. O. (2003) Constraints-based models: regulation of gene expression reduces the steady-state solution space. J. Theor. Biol., 221, 309–325
pmid: 12642111
127 Varma, A. and Palsson, B. O. (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol., 60, 3724–3731
pmid: 7986045
128 Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J. and Palsson, B. O. (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429, 92–96
pmid: 15129285
129 Schuetz, R., Kuepfer, L. and Sauer, U. (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol., 3, 119
pmid: 17625511
130 Zamboni, N. (2011) 13C metabolic flux analysis in complex systems. Curr. Opin. Biotechnol., 22, 103–108
pmid: 20833526
131 Liu, Y. Y., Slotine, J. J. and Barabási, A. L. (2013) Observability of complex systems. Proc. Natl. Acad. Sci. U.S.A., 110, 2460–2465
pmid: 23359701
132 Smallbone, K., Simeonidis, E., Broomhead, D. S. and Kell, D. B. (2007) Something from nothing: bridging the gap between constraint-based and kinetic modelling. FEBS J., 274, 5576–5585
pmid: 17922843
133 Covert, M. W., Xiao, N., Chen, T. J. and Karr, J. R. (2008) Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics, 24, 2044–2050
pmid: 18621757
134 Lee, J. M., Gianchandani, E. P., Eddy, J. A. and Papin, J. A. (2008) Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLOS Comput. Biol., 4, e1000086
pmid: 18483615
135 Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B. Jr, Assad-Garcia, N., Glass, J. I. and Covert, M. W. (2012) A whole-cell computational model predicts phenotype from genotype. Cell, 150, 389–401
pmid: 22817898
136 Shlomi, T., Eisenberg, Y., Sharan, R. and Ruppin, E. (2007) A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol. Syst. Biol., 3, 101
pmid: 17437026
137 Kim, J. and Reed, J. L. (2010) OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol., 4, 53
pmid: 20426856
138 Barua, D., Kim, J. and Reed, J. L. (2010) An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLOS Comput. Biol., 6, e1000970
pmid: 21060853
139 Goelzer, A., Bekkal Brikci, F., Martin-Verstraete, I., Noirot, P., Bessières, P., Aymerich, S. and Fromion, V. (2008) Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis. BMC Syst. Biol., 2, 20
pmid: 18302748
140 Colijn, C., Brandes, A., Zucker, J., Lun, D. S., Weiner, B., Farhat, M. R., Cheng, T. Y., Moody, D. B., Murray, M. and Galagan, J. E. (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLOS Comput. Biol., 5, e1000489
pmid: 19714220
141 Becker, S. A. and Palsson, B. O. (2008) Context-specific metabolic networks are consistent with experiments. PLOS Comput. Biol., 4, e1000082
pmid: 18483554
142 Jensen, P. A., Lutz, K. A. and Papin, J. A. (2011) TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks. BMC Syst. Biol., 5, 147
pmid: 21943338
143 Blazier, A. S. and Papin, J. A. (2012) Integration of expression data in genome-scale metabolic network reconstructions. Front. Physiol., 3, 299
pmid: 22934050
144 Gygi, S. P., Rochon, Y., Franza, B. R. and Aebersold, R. (1999) Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol., 19, 1720–1730
pmid: 10022859
145 Chandrasekaran, S. and Price, N. D. (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. U.S.A., 107, 17845–17850
pmid: 20876091
146 Chandrasekaran, S. and Price, N. D. (2013) Metabolic constraint-based refinement of transcriptional regulatory networks. PLOS Comput. Biol., 9, e1003370
pmid: 24348226
147 Jensen, P. A. and Papin, J. A. (2011) Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics, 27, 541–547
pmid: 21172910
148 Thiele, I., Jamshidi, N., Fleming, R. M. and Palsson, B. O. (2009) Genome-scale reconstruction of Escherichia coli’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLOS Comput. Biol., 5, e1000312
pmid: 19282977
149 Thiele, I., Fleming, R. M., Que, R., Bordbar, A., Diep, D. and Palsson, B. O. (2012) Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage. PLoS ONE, 7, e45635
pmid: 23029152
150 O’Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R. and Palsson, B. O. (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol. Syst. Biol., 9, 693
pmid: 24084808
151 Lerman, J. A., Hyduke, D. R., Latif, H., Portnoy, V. A., Lewis, N. E., Orth, J. D., Schrimpe-Rutledge, A. C., Smith, R. D., Adkins, J. N., Zengler, K., . (2012) In silico method for modelling metabolism and gene product expression at genome scale. Nat. Commun., 3, 929
pmid: 22760628
152 Feizi, A., ?sterlund, T., Petranovic, D., Bordel, S. and Nielsen, J. (2013) Genome-scale modeling of the protein secretory machinery in yeast. PLoS ONE, 8, e63284
pmid: 23667601
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