The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

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.

Graphical abstract

Keywords

transcriptional regulatory network / metabolic model / Saccharomyces cerevisiae / integration

References

[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.

[2] Herrgård, M. J., Covert, M. W. and Palsson, B. O. (2004) Reconstruction of microbial transcriptional regulatory networks. Curr. Opin. Biotechnol., 15, 70–77.

[3] Blais, A. and Dynlacht, B. D. (2005) Constructing transcriptional regulatory networks. Genes Dev., 19, 1499–1511.

[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.

[5] Feist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L. and Palsson, B. O. (2009) Reconstruction of biochemical networks in microorganisms. Nat. Rev. Microbiol., 7, 129–143.

[6] Hughes, T. R. and de Boer, C. G. (2013) Mapping yeast transcriptional networks. Genetics, 195, 9–36.

[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.

[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.

[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.

[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.

[11] Abdulrehman, D., Monteiro, P. T., Teixeira, M. C., Mira, N. P., Lourenço, 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.

[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.

[13] Broach, J. R. (2012) Nutritional control of growth and development in yeast. Genetics, 192, 73–105.

[14] Ljungdahl, P. O. and Daignan-Fornier, B. (2012) Regulation of amino acid, nucleotide, and phosphate metabolism in Saccharomyces cerevisiae. Genetics, 190, 885–929.

[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.

[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.

[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.

[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.

[19] Österlund, 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.

[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.

[21] Klein, C. J., Rasmussen, J. J., Rønnow, 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.

[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.

[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.

[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.

[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.

[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.

[27] Andréasson, C. and Ljungdahl, P. O. (2002) Receptor-mediated endoproteolytic activation of two transcription factors in yeast. Genes Dev., 16, 3158–3172.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[40] Geertz, M. and Maerkl, S. J. (2010) Experimental strategies for studying transcription factor-DNA binding specificities. Brief Funct. Genomics, 9, 362–373.

[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.

[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.

[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.

[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.

[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.

[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.

[47] Park, P. J. (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet., 10, 669–680.

[48] Rhee, H. S. and Pugh, B. F. (2011) Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell, 147, 1408–1419.

[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.

[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.

[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.

[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.

[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.

[54] Hu, Z., Killion, P. J. and Iyer, V. R. (2007) Genetic reconstruction of a functional transcriptional regulatory network. Nat. Genet., 39, 683–687.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[63] Beer, M. A. and Tavazoie, S. (2004) Predicting gene expression from sequence. Cell, 117, 185–198.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[72] Gordân, 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.

[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.

[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.

[75] Zhu, J. and Zhang, M. Q. (1999) SCPD: a promoter database of the yeast Saccharomyces cerevisiae. Bioinformatics, 15, 607–611.

[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.

[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.

[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.

[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.

[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.

[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.

[82] Novershtern, N., Regev, A. and Friedman, N. (2011) Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinformatics, 27, i177–i185.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[92] Herrgård, 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.

[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.

[94] Schlitt, T. and Brazma, A. (2007) Current approaches to gene regulatory network modelling. BMC Bioinformatics, 8, S9.

[95] Karlebach, G. and Shamir, R. (2008) Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol., 9, 770–780.

[96] de Jong, H. (2002) Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol., 9, 67–103.

[97] Glass, L. and Kauffman, S. A. (1973) The logical analysis of continuous, non-linear biochemical control networks. J. Theor. Biol., 39, 103–129.

[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.

[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.

[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.

[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.

[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.

[103] Herrgård, 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.

[104] Maslov, S. and Sneppen, K. (2002) Specificity and stability in topology of protein networks. Science, 296, 910–913.

[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.

[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.

[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.

[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.

[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.

[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.

[111] Thiele, I. and Palsson, B. O. (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc., 5, 93–121.

[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.

[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.

[114] Herrgård, 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.

[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.

[116] Orth, J. D., Thiele, I. and Palsson, B. O. (2010) What is flux balance analysis? Nat. Biotechnol., 28, 245–248.

[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.

[118] Feist, A. M. and Palsson, B. O. (2010) The biomass objective function. Curr. Opin. Microbiol., 13, 344–349.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[130] Zamboni, N. (2011) ¹³C metabolic flux analysis in complex systems. Curr. Opin. Biotechnol., 22, 103–108.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[141] Becker, S. A. and Palsson, B. O. (2008) Context-specific metabolic networks are consistent with experiments. PLOS Comput. Biol., 4, e1000082.

[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.

[143] Blazier, A. S. and Papin, J. A. (2012) Integration of expression data in genome-scale metabolic network reconstructions. Front. Physiol., 3, 299.

[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.

[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.

[146] Chandrasekaran, S. and Price, N. D. (2013) Metabolic constraint-based refinement of transcriptional regulatory networks. PLOS Comput. Biol., 9, e1003370.

[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.

[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.

[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.

[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.

[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.

[152] Feizi, A., Österlund, T., Petranovic, D., Bordel, S. and Nielsen, J. (2013) Genome-scale modeling of the protein secretory machinery in yeast. PLoS ONE, 8, e63284.

ACKNOWLEDGEMENTS

This work has been funded by the European Research Council (INSYSBIO, Grant No. 247013) and the Novo Nordisk Foundation.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg