Data-driven integration of genome-scale regulatory and metabolic network models

Frontiers in Microbiology

Volumn 6, Issue MAY, 2015, Pages

  Imam, Saheed ^a   Schäuble, Sascha ^a,b   Brooks, Aaron N ^a   Baliga, Nitin S ^a,c,d   Price, Nathan D ^a  

^a INSTITUTE FOR SYSTEMS BIOLOGY   (United States)

^b FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

^c UNIVERSITY OF WASHINGTON   (United States)

^d LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

Constraint based modeling; Flux balance analysis; Metabolic networks; Metabolism; Network integration; Regulation; Signaling; Transcriptional networks

Indexed keywords

COMPUTER MODEL; CONSTRAINT BASED METABOLIC MODEL; ENVIRONMENTAL CHANGE; GENE EXPRESSION; GENETIC TRANSCRIPTION; GENOME SCALE METABOLIC MODEL; INFORMATION MODEL; INFORMATION PROCESSING; MICROBIAL METABOLISM; NONHUMAN; REVIEW; SIGNAL TRANSDUCTION; SIGNALING NETWORK MODEL; TRANSCRIPTIONAL REGULATORY NETWORK MODEL;

EID: 84931273722     PISSN: None     EISSN: 1664302X     Source Type: Journal    
DOI: 10.3389/fmicb.2015.00409     Document Type: Review

References (96)

1. 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. doi: 10.1371/journal.pcbi.1002980.
2. Aldridge, S., Watt, S., Quail, M. A., Rayner, T., Lukk, M., Bimson, M. F., et al. (2013). AHT-ChIP-seq: a completely automated robotic protocol for high-throughput chromatin immunoprecipitation. Genome Biol. 14:R124. doi: 10.1186/gb-2013-14-11-r124.
3. Alper, H., Jin, Y. S., Moxley, J. F., and Stephanopoulos, G. (2005). Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 7, 155-164. doi: 10.1016/j.ymben.2004.12.003.
4. Bakker, B. M., Van Eunen, K., Jeneson, J. A., Van Riel, N. A., Bruggeman, F. J., and Teusink, B. (2010). Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models. Biochem. Soc. Trans. 38, 1294-1301. doi: 10.1042/BST0381294.
5. 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. doi: 10.1371/journal.pcbi.1000970.
6. Benedict, M. N., Mundy, M. B., Henry, C. S., Chia, N., and Price, N. D. (2014). Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models. PLoS Comput. Biol. 10:e1003882. doi: 10.1371/journal.pcbi.1003882.
7. Berthoumieux, S., De Jong, H., Baptist, G., Pinel, C., Ranquet, C., Ropers, D., et al. (2013). Shared control of gene expression in bacteria by transcription factors and global physiology of the cell. Mol. Syst. Biol. 9, 634. doi: 10.1038/msb.2012.70.
8. Bochner, B. R., Gadzinski, P., and Panomitros, E. (2001). Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res. 11, 1246-1255. doi: 10.1101/gr.186501.
9. Bonneau, R., Facciotti, M. T., Reiss, D. J., Schmid, A. K., Pan, M., Kaur, A., et al. (2007). A predictive model for transcriptional control of physiology in a free living cell. Cell 131, 1354-1365. doi: 10.1016/j.cell.2007.10.053.
10. Bordbar, A., Monk, J. M., King, Z. A., and Palsson, B. O. (2014). Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15, 107-120. doi: 10.1038/nrg3643.
11. Brooks, A. N., Reiss, D. J., Allard, A., Wu, W. J., Salvanha, D. M., Plaisier, C. L., et al. (2014). A system-level model for the microbial regulatory genome. Mol. Syst. Biol. 10, 740. doi: 10.15252/msb.20145160.
12. Burgard, A. P., Pharkya, P., and Maranas, C. D. (2003). Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647-657. doi: 10.1002/bit.10803.
13. Carrera, J., Estrela, R., Luo, J., Rai, N., Tsoukalas, A., and Tagkopoulos, I. (2014). An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol. Syst. Biol. 10, 735. doi: 10.15252/msb.20145108.
14. 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. doi: 10.1073/pnas.1005139107.
15. Chandrasekaran, S., and Price, N. D. (2013). Metabolic constraint-based refinement of transcriptional regulatory networks. PLoS Comput. Biol. 9:e1003370. doi: 10.1371/journal.pcbi.1003370.
16. Chiang, S. M., and Schellhorn, H. E. (2012). Regulators of oxidative stress response genes in Escherichia coli and their functional conservation in bacteria. Arch. Biochem. Biophys. 525, 161-169. doi: 10.1016/j.abb.2012.02.007.
17. Chubukov, V., Gerosa, L., Kochanowski, K., and Sauer, U. (2014). Coordination of microbial metabolism. Nat. Rev. Microbiol. 12, 327-340. doi: 10.1038/nrmicro3238.
18. 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. doi: 10.1038/nature02456.
19. Covert, M. W., and Palsson, B. O. (2002). Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J. Biol. Chem. 277, 28058-28064. doi: 10.1074/jbc.M201691200.
20. 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. doi: 10.1006/jtbi.2003.3071.
21. 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. doi: 10.1093/bioinformatics/btn352.
22. De Smet, R., and Marchal, K. (2010). Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 8, 717-729. doi: 10.1038/nrmicro2419.
23. Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., et al. (2007). Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5:e8. doi: 10.1371/journal.pbio.0050008.
24. Farr, S. B., and Kogoma, T. (1991). Oxidative stress responses in Escherichia coli and Salmonella typhimurium. Microbiol. Rev. 55, 561-585.
25. Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., et al. (2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3, 121. doi: 10.1038/msb4100155.
26. Gama-Castro, S., Salgado, H., Peralta-Gil, M., Santos-Zavaleta, A., Muniz-Rascado, L., Solano-Lira, H., et al. (2011). RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units). Nucleic Acids Res. 39, D98-D105. doi: 10.1093/nar/gkq1110.
27. Gasper, W. C., Marinov, G. K., Pauli-Behn, F., Scott, M. T., Newberry, K., Desalvo, G., et al. (2014). Fully automated high-throughput chromatin immunoprecipitation for ChIP-seq: identifying ChIP-quality p300 monoclonal antibodies. Sci. Rep. 4:5152. doi: 10.1038/srep05152.
28. Gerosa, L., and Sauer, U. (2011). Regulation and control of metabolic fluxes in microbes. Curr. Opin. Biotechnol. 22, 566-575. doi: 10.1016/j.copbio.2011.04.016.
29. Gorke, B., and Stulke, J. (2008). Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat. Rev. Microbiol. 6, 613-624. doi: 10.1038/nrmicro1932.
30. Hanson, A. D., Pribat, A., Waller, J. C., and De Crecy-Lagard, V. (2009). 'Unknown' proteins and 'orphan' enzymes: the missing half of the engineering parts list-and how to find it. Biochem. J. 425, 1-11. doi: 10.1042/BJ20091328.
31. Henry, C. S., Dejongh, M., Best, A. A., Frybarger, P. M., Linsay, B., and Stevens, R. L. (2010). High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977-982. doi: 10.1038/nbt.1672.
32. Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., and Geurts, P. (2010). Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5:e12776. doi: 10.1371/journal.pone.0012776.
33. Hyduke, D. R., and Palsson, B. O. (2010). Towards genome-scale signalling network reconstructions. Nat. Rev. Genet. 11, 297-307. doi: 10.1038/nrg2750.
34. Imam, S., Noguera, D. R., and Donohue, T. J. (2013). Global insights into energetic and metabolic networks in Rhodobacter sphaeroides. BMC Syst. Biol. 7:89. doi: 10.1186/1752-0509-7-89.
35. Imam, S., Noguera, D. R., and Donohue, T. J. (2015). CceR and AkgR regulate central carbon and energy metabolism in Alphaproteobacteria. mBio 6:e02461-14. doi: 10.1128/mBio.02461-14.
36. Jurica, M. S., Mesecar, A., Heath, P. J., Shi, W., Nowak, T., and Stoddard, B. L. (1998). The allosteric regulation of pyruvate kinase by fructose-1,6-bisphosphate. Structure 6, 195-210. doi: 10.1016/S0969-2126(98)00021-5.
37. Karp, P. D., Paley, S. M., Krummenacker, M., Latendresse, M., Dale, J. M., Lee, T. J., et al. (2010). Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief. Bioinformatics 11, 40-79. doi: 10.1093/bib/bbp043.
38. Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B. Jr., et al. (2012). A whole-cell computational model predicts phenotype from genotype. Cell 150, 389-401. doi: 10.1016/j.cell.2012.05.044.
39. Khatiwara, A., Jiang, T., Sung, S. S., Dawoud, T., Kim, J. N., Bhattacharya, D., et al. (2012). Genome scanning for conditionally essential genes in Salmonella enterica Serotype Typhimurium. Appl. Environ. Microbiol. 78, 3098-3107. doi: 10.1128/AEM.06865-11.
40. Kim, J., and Reed, J. L. (2010). OptORF: optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol. 4:53. doi: 10.1186/1752-0509-4-53.
41. Kim, T. Y., Sohn, S. B., Kim, Y. B., Kim, W. J., and Lee, S. Y. (2012). Recent advances in reconstruction and applications of genome-scale metabolic models. Curr. Opin. Biotechnol. 23, 617-623. doi: 10.1016/j.copbio.2011.10.007.
42. Klamt, S., Saez-Rodriguez, J., Lindquist, J. A., Simeoni, L., and Gilles, E. D. (2006). A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics 7:56. doi: 10.1186/1471-2105-7-56.
43. Lengauer, T., and Rarey, M. (1996). Computational methods for biomolecular docking. Curr. Opin. Struct. Biol. 6, 402-406. doi: 10.1016/S0959-440X(96)80061-3.
44. Lerman, J. A., Hyduke, D. R., Latif, H., Portnoy, V. A., Lewis, N. E., Orth, J. D., et al. (2012). In silico method for modelling metabolism and gene product expression at genome scale. Nat. Commun. 3, 929. doi: 10.1038/ncomms1928.
45. Leyn, S. A., Li, X., Zheng, Q., Novichkov, P. S., Reed, S., Romine, M. F., et al. (2011). Control of proteobacterial central carbon metabolism by the HexR transcriptional regulator: a case study in Shewanella oneidensis. J. Biol. Chem. 286, 35782-35794. doi: 10.1074/jbc.M111.267963.
46. Li, X., Gianoulis, T. A., Yip, K. Y., Gerstein, M., and Snyder, M. (2010). Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses. Cell 143, 639-650. doi: 10.1016/j.cell.2010.09.048.
47. Lim, D. B., Oppenheim, J. D., Eckhardt, T., and Maas, W. K. (1987). Nucleotide sequence of the argR gene of Escherichia coli K-12 and isolation of its product, the arginine repressor. Proc. Natl. Acad. Sci. U.S.A. 84, 6697-6701. doi: 10.1073/pnas.84.19.6697.
48. Lozada-Chavez, I., Janga, S. C., and Collado-Vides, J. (2006). Bacterial regulatory networks are extremely flexible in evolution. Nucleic Acids Res. 34, 3434-3445. doi: 10.1093/nar/gkl423.
49. 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. doi: 10.1186/2191-0855-1-45.
50. Machado, D., and Herrgard, M. (2014). Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput. Biol. 10:e1003580. doi: 10.1371/journal.pcbi.1003580.
51. Madan Babu, M., Teichmann, S. A., and Aravind, L. (2006). Evolutionary dynamics of prokaryotic transcriptional regulatory networks. J. Mol. Biol. 358, 614-633. doi: 10.1016/j.jmb.2006.02.019.
52. Marbach, D., Costello, J. C., Kuffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., et al. (2012). Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796-804. doi: 10.1038/nmeth.2016.
53. Marbach, D., Prill, R. J., Schaffter, T., Mattiussi, C., Floreano, D., and Stolovitzky, G. (2010). Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl. Acad. Sci. U.S.A. 107, 6286-6291. doi: 10.1073/pnas.0913357107.
54. Metallo, C. M., and Vander Heiden, M. G. (2013). Understanding metabolic regulation and its influence on cell physiology. Mol. Cell 49, 388-398. doi: 10.1016/j.molcel.2013.01.018.
55. Milne, C. B., Kim, P. J., Eddy, J. A., and Price, N. D. (2009). Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology. Biotechnol. J. 4, 1653-1670. doi: 10.1002/biot.200900234.
56. Nichols, R. J., Sen, S., Choo, Y. J., Beltrao, P., Zietek, M., Chaba, R., et al. (2011). Phenotypic landscape of a bacterial cell. Cell 144, 143-156. doi: 10.1016/j.cell.2010.11.052.
57. Novichkov, P. S., Rodionov, D. A., Stavrovskaya, E. D., Novichkova, E. S., Kazakov, A. E., Gelfand, M. S., et al. (2010). RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach. Nucleic Acids Res. 38, W299-W307. doi: 10.1093/nar/gkq531.
58. Oberhardt, M. A., Palsson, B. O., and Papin, J. A. (2009). Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 5, 320. doi: 10.1038/msb.2009.77.
59. 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. doi: 10.1038/msb.2013.52.
60. Oh, Y. K., Palsson, B. O., Park, S. M., Schilling, C. H., and Mahadevan, R. (2007). Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J. Biol. Chem. 282, 28791-28799. doi: 10.1074/jbc.M703759200.
61. Palsson, B. (2000). The challenges of in silico biology. Nat. Biotechnol. 18, 1147-1150. doi: 10.1038/81125.
62. Papin, J. A., and Palsson, B. O. (2004). Topological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk. J. Theor. Biol. 227, 283-297. doi: 10.1016/j.jtbi.2003.11.016.
63. Park, J. H., Lee, K. H., Kim, T. Y., and Lee, S. Y. (2007). Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. U.S.A. 104, 7797-7802. doi: 10.1073/pnas.0702609104.
64. Pharkya, P., Burgard, A. P., and Maranas, C. D. (2004). OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 14, 2367-2376. doi: 10.1101/gr.2872004.
65. Pietack, N., Becher, D., Schmidl, S. R., Saier, M. H., Hecker, M., Commichau, F. M., et al. (2010). In vitro phosphorylation of key metabolic enzymes from Bacillus subtilis: PrkC phosphorylates enzymes from different branches of basic metabolism. J. Mol. Microbiol. Biotechnol. 18, 129-140. doi: 10.1159/000308512.
66. Price, M. N., Deutschbauer, A. M., Skerker, J. M., Wetmore, K. M., Ruths, T., Mar, J. S., et al. (2013). Indirect and suboptimal control of gene expression is widespread in bacteria. Mol. Syst. Biol. 9, 660. doi: 10.1038/msb.2013.16.
67. Ramseier, T. M., Bledig, S., Michotey, V., Feghali, R., and Saier, M. H. Jr. (1995). The global regulatory protein FruR modulates the direction of carbon flow in Escherichia coli. Mol. Microbiol. 16, 1157-1169. doi: 10.1111/j.1365-2958.1995.tb02339.x.
68. Ramseier, T. M., Negre, D., Cortay, J. C., Scarabel, M., Cozzone, A. J., and Saier, M. H. Jr (1993). In vitro binding of the pleiotropic transcriptional regulatory protein, FruR, to the fru, pps, ace, pts and icd operons of Escherichia coli and Salmonella typhimurium. J. Mol. Biol. 234, 28-44. doi: 10.1006/jmbi.1993.1561.
69. Reed, J. L. (2012). Shrinking the metabolic solution space using experimental datasets. PLoS Comput. Biol. 8:e1002662. doi: 10.1371/journal.pcbi.1002662.
70. Reed, J. L., and Palsson, B. O. (2004). Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res. 14, 1797-1805. doi: 10.1101/gr.2546004.
71. Richards, M. A., Cassen, V., Heavner, B. D., Ajami, N. E., Herrmann, A., Simeonidis, E., et al. (2014). MediaDB: a database of microbial growth conditions in defined media. PLoS ONE 9:e103548. doi: 10.1371/journal.pone.0103548.
72. Schellenberger, J., Lewis, N. E., and Palsson, B. O. (2011). Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys. J. 100, 544-553. doi: 10.1016/j.bpj.2010.12.3707.
73. Schmidl, S. R., Gronau, K., Pietack, N., Hecker, M., Becher, D., and Stulke, J. (2010). The phosphoproteome of the minimal bacterium Mycoplasma pneumoniae: analysis of the complete known Ser/Thr kinome suggests the existence of novel kinases. Mol. Cell. Proteomics 9, 1228-1242. doi: 10.1074/mcp.M900267-MCP200.
74. Segre, D., Vitkup, D., and Church, G. M. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. U.S.A. 99, 15112-15117. doi: 10.1073/pnas.232349399.
75. Shlomi, T., Berkman, O., and Ruppin, E. (2005). Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc. Natl. Acad. Sci. U.S.A. 102, 7695-7700. doi: 10.1073/pnas.0406346102.
76. 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. doi: 10.1038/msb4100141.
77. Steuer, R., Gross, T., Selbig, J., and Blasius, B. (2006). Structural kinetic modeling of metabolic networks. Proc. Natl. Acad. Sci. U.S.A. 103, 11868-11873. doi: 10.1073/pnas.0600013103.
78. Stolovitzky, G., Monroe, D., and Califano, A. (2007). Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann. N.Y. Acad. Sci. 1115, 1-22. doi: 10.1196/annals.1407.021.
79. Swainston, N., Smallbone, K., Mendes, P., Kell, D., and Paton, N. (2011). The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks. J. Integr. Bioinform. 8:186. doi: 10.2390/biecoll-jib-2011-186.
80. Tagore, R., Thomas, H. R., Homan, E. A., Munawar, A., and Saghatelian, A. (2008). A global metabolite profiling approach to identify protein-metabolite interactions. J. Am. Chem. Soc. 130, 14111-14113. doi: 10.1021/ja806463c.
81. Tan, Y., and Liao, J. C. (2012). Metabolic ensemble modeling for strain engineers. Biotechnol. J. 7, 343-353. doi: 10.1002/biot.201100186.
82. Tervo, C. J., and Reed, J. L. (2013). BioMog: a computational framework for the de novo generation or modification of essential biomass components. PLoS ONE 8:e81322. doi: 10.1371/journal.pone.0081322.
83. 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. doi: 10.1371/journal.pcbi.1000312.
84. Thiele, I., and Palsson, B. O. (2010). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93-121. doi: 10.1038/nprot.2009.203.
85. Tomita, M. (2001). Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol. 19, 205-210. doi: 10.1016/S0167-7799(01)01636-5.
86. Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T. S., Matsuzaki, Y., Miyoshi, F., et al. (1999). E-CELL: software environment for whole-cell simulation. Bioinformatics 15, 72-84. doi: 10.1093/bioinformatics/15.1.72.
87. Tran, L. M., Rizk, M. L., and Liao, J. C. (2008). Ensemble modeling of metabolic networks. Biophys. J. 95, 5606-5617. doi: 10.1529/biophysj.108.135442.
88. Ulrich, L. E., and Zhulin, I. B. (2010). The MiST2 database: a comprehensive genomics resource on microbial signal transduction. Nucleic Acids Res. 38, D401-D407. doi: 10.1093/nar/gkp940.
89. Vandersluis, B., Hess, D. C., Pesyna, C., Krumholz, E. W., Syed, T., Szappanos, B., et al. (2014). Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection. Genome Biol. 15:R64. doi: 10.1186/gb-2014-15-4-r64.
90. Van Opijnen, T., Bodi, K. L., and Camilli, A. (2009). Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nat. Methods 6, 767-772. doi: 10.1038/nmeth.1377.
91. Varma, A., and Palsson, B. O. (1994). Metabolic flux balancing: basic concepts, scientific and practical use. Nat. Biotechnol. 12, 994-998. doi: 10.1038/nbt1094-994.
92. Wang, Q., Zhang, Y., Yang, C., Xiong, H., Lin, Y., Yao, J., et al. (2010). Acetylation of metabolic enzymes coordinates carbon source utilization and metabolic flux. Science 327, 1004-1007. doi: 10.1126/science.1179687.
93. Waygood, E. B., Mort, J. S., and Sanwal, B. D. (1976). The control of pyruvate kinase of Escherichia coli. Binding of substrate and allosteric effectors to the enzyme activated by fructose 1,6-bisphosphate. Biochemistry 15, 277-282. doi: 10.1021/bi00647a006.
94. Yachie, N., Saito, R., Sugiyama, N., Tomita, M., and Ishihama, Y. (2011). Integrative features of the yeast phosphoproteome and protein-protein interaction map. PLoS Comput. Biol. 7:e1001064. doi: 10.1371/journal.pcbi.1001064.
95. Zhang, Z., Tan, M., Xie, Z., Dai, L., Chen, Y., and Zhao, Y. (2010). Identification of lysine succinylation as a new post-translational modification. Nat. Chem. Biol. 7, 58-63. doi: 10.1038/nchembio.495.
96. Zielinski, D. C., and Palsson, B. (2012). "Kinetic modeling of metabolic networks," in Systems Metabolic Engineering, eds C. Wittmann and S.Y. Lee (Dordrecht: Springer Netherlands), 25-55. doi: 10.1007/978-94-007-4534-6_2.