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Towards the Reconstruction of Integrated Genome-Scale Models of Metabolism and Gene Expression

  • Fernando CruzEmail author
  • Diogo Lima
  • José P. Faria
  • Miguel Rocha
  • Oscar DiasEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1005)

Abstract

The reconstruction of integrated genome-scale models of metabolism and gene expression has been a challenge for a while now. In fact, various methods that allow integrating reconstructions of Transcriptional Regulatory Networks, gene expression data or both into Genome-Scale Metabolic Models have been proposed. Several of these methods are surveyed in this article, which allowed identifying their strengths and weaknesses concerning the reconstruction of integrated models for multiple prokaryotic organisms. Additionally, the main resources of regulatory information were also surveyed, as the existence of novel sources of regulatory information and gene expression data may contribute for the improvement of methodologies referred herein.

Keywords

Genome-Scale Metabolic Models Genome-scale models of metabolism and gene expression Regulation of gene expression Databases of regulatory information 

Notes

Acknowledgements

This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020-Programa Operacional Regional do Norte. Fernando Cruz holds a doctoral fellowship (SFRH/BD/139198/2018) funded by the FCT. The authors thank project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (814408) funded by the European Commission.

References

  1. 1.
    Nelson, D.L., Cox, M.M.: Lehninger Principles of Biochemistry. W.H. Freeman, New York (2008)Google Scholar
  2. 2.
    Lodish, H., Berk, A., Zipursky, S.L., Matsudaira, P., Baltimore, D., Darnell, J.: Molecular Cell Biology. W.H. Freeman, New York (2000)Google Scholar
  3. 3.
    Willson, C., Pebrin, D., Cohn, M., Jacob, F., Monod, J.: Non-inducible mutants of the regulator gene in the “lactose” system of Escherichia coli. J. Mol. Biol. 8, 582–592 (1964)CrossRefGoogle Scholar
  4. 4.
    Sierro, N., Makita, Y., de Hoon, M., Nakai, K.: DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information. Nucleic Acids Res. 36, D93–D96 (2008)CrossRefGoogle Scholar
  5. 5.
    Faria, J.P., Overbeek, R., Taylor, R.C., Conrad, N., Vonstein, V., Goelzer, A., Fromion, V., Rocha, M., Rocha, I., Henry, C.S.: Reconstruction of the regulatory network for bacillus subtilis and reconciliation with gene expression data. Front. Microbiol. 7, 275 (2016)Google Scholar
  6. 6.
    Faria, J.P., Davis, J.J., Edirisinghe, J.N., Taylor, R.C., Weisenhorn, P., Olson, R.D., Stevens, R.L., Rocha, M., Rocha, I., Best, A.A., DeJongh, M., Tintle, N.L., Parrello, B., Overbeek, R., Henry, C.S.: Computing and applying atomic regulons to understand gene expression and regulation. Front. Microbiol. 7, 1819 (2016)Google Scholar
  7. 7.
    Santos-Zavaleta, A., Sánchez-Pérez, M., Salgado, H., Velázquez-Ramírez, D.A., Gama-Castro, S., Tierrafría, V.H., Busby, S.J.W., Aquino, P., Fang, X., Palsson, B.O., Galagan, J.E., Collado-Vides, J.: A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0. BMC Biol. 16, 91 (2018)CrossRefGoogle Scholar
  8. 8.
    Novichkov, P.S., Brettin, T.S., Novichkova, E.S., Dehal, P.S., Arkin, A.P., Dubchak, I., Rodionov, D.A.: RegPrecise web services interface: programmatic access to the transcriptional regulatory interactions in bacteria reconstructed by comparative genomics. Nucleic Acids Res. 40, W604–W608 (2012)CrossRefGoogle Scholar
  9. 9.
    Novichkov, P.S., Kazakov, A.E., Ravcheev, D.A., Leyn, S.A., Kovaleva, G.Y., Sutormin, R.A., Kazanov, M.D., Riehl, W., Arkin, A.P., Dubchak, I., Rodionov, D.A.: RegPrecise 3.0 – a resource for genome-scale exploration of transcriptional regulation in bacteria. BMC Genom. 14, 745 (2013)CrossRefGoogle Scholar
  10. 10.
    Eckweiler, D., Dudek, C.-A., Hartlich, J., Brötje, D., Jahn, D.: PRODORIC2: the bacterial gene regulation database in 2018. Nucleic Acids Res. 46, D320–D326 (2018)CrossRefGoogle Scholar
  11. 11.
    Young, R.A.: Biomedical discovery with DNA arrays. Cell 102, 9–15 (2000)CrossRefGoogle Scholar
  12. 12.
    Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., Wold, B.: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008)CrossRefGoogle Scholar
  13. 13.
    Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M., Snyder, M.: The transcriptional landscape of the yeast genome defined by RNA sequencing. Science (80-.) 320, 1344–1349 (2008)CrossRefGoogle Scholar
  14. 14.
    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, 533–538 (2001)CrossRefGoogle Scholar
  15. 15.
    Velculescu, V.E., Zhang, L., Zhou, W., Vogelstein, J., Basrai, M.A., Bassett, D.E., Hieter, P., Vogelstein, B., Kinzler, K.W.: Characterization of the yeast transcriptome. Cell 88, 243–251 (1997)CrossRefGoogle Scholar
  16. 16.
    Johnson, D.S., Mortazavi, A., Myers, R.M., Wold, B.: Genome-wide mapping of in vivo protein-DNA interactions. Science (80-.) 316, 1497–1502 (2007)CrossRefGoogle Scholar
  17. 17.
    Price, M.N., Deutschbauer, A.M., Kuehl, J.V., Liu, H., Witkowska, H.E., Arkin, A.P.: Evidence-based annotation of transcripts and proteins in the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough. J. Bacteriol. 193, 5716–5727 (2011)CrossRefGoogle Scholar
  18. 18.
    Barrett, T., Wilhite, S.E., Ledoux, P., Evangelista, C., Kim, I.F., Tomashevsky, M., Marshall, K.A., Phillippy, K.H., Sherman, P.M., Holko, M., Yefanov, A., Lee, H., Zhang, N., Robertson, C.L., Serova, N., Davis, S., Soboleva, A.: NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 41, D991–D995 (2012)CrossRefGoogle Scholar
  19. 19.
    Kolesnikov, N., Hastings, E., Keays, M., Melnichuk, O., Tang, Y.A., Williams, E., Dylag, M., Kurbatova, N., Brandizi, M., Burdett, T., Megy, K., Pilicheva, E., Rustici, G., Tikhonov, A., Parkinson, H., Petryszak, R., Sarkans, U., Brazma, A.: ArrayExpress update—simplifying data submissions. Nucleic Acids Res. 43, D1113–D1116 (2015)CrossRefGoogle Scholar
  20. 20.
    Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C.A., Causton, H.C., Gaasterland, T., Glenisson, P., Holstege, F.C.P., Kim, I.F., Markowitz, V., Matese, J.C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M.: Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet. 29, 365–371 (2001)CrossRefGoogle Scholar
  21. 21.
    Moretto, M., Sonego, P., Dierckxsens, N., Brilli, M., Bianco, L., Ledezma-Tejeida, D., Gama-Castro, S., Galardini, M., Romualdi, C., Laukens, K., Collado-Vides, J., Meysman, P., Engelen, K.: COLOMBOS v3.0: leveraging gene expression compendia for cross-species analyses. Nucleic Acids Res. 44, D620–D623 (2016)CrossRefGoogle Scholar
  22. 22.
    Faith, J.J., Driscoll, M.E., Fusaro, V.A., Cosgrove, E.J., Hayete, B., Juhn, F.S., Schneider, S.J., Gardner, T.S.: Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res. 36, D866–D870 (2007)CrossRefGoogle Scholar
  23. 23.
    Faria, J.P., Overbeek, R., Xia, F., Rocha, M., Rocha, I., Henry, C.S.: Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models. Brief. Bioinform. 15, 592–611 (2014)CrossRefGoogle Scholar
  24. 24.
    Blazier, A.S., Papin, J.A.: Integration of expression data in genome-scale metabolic network reconstructions. Front. Physiol. 3, 299 (2012)CrossRefGoogle Scholar
  25. 25.
    Machado, D., Herrgård, M.: Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput. Biol. 10, e1003580 (2014)CrossRefGoogle Scholar
  26. 26.
    Jensen, P.A., Papin, J.A.: Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27, 541–547 (2011)CrossRefGoogle Scholar
  27. 27.
    Colijn, C., Brandes, A., Zucker, J., Lun, D.S., Weiner, B., Farhat, M.R., Cheng, T.-Y., Moody, D.B., Murray, M., Galagan, J.E.: Interpreting expression data with metabolic flux models: predicting mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5, e1000489 (2009)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Chandrasekaran, S., Price, N.D.: 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 (2010)CrossRefGoogle Scholar
  29. 29.
    van Berlo, R.J.P., de Ridder, D., Daran, J.-M., Daran-Lapujade, P.A.S., Teusink, B., Reinders, M.J.T.: Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM Trans. Comput. Biol. Bioinform. 8, 206–216 (2011)CrossRefGoogle Scholar
  30. 30.
    Lee, D., Smallbone, K., Dunn, W.B., Murabito, E., Winder, C.L., Kell, D.B., Mendes, P., Swainston, N.: Improving metabolic flux predictions using absolute gene expression data. BMC Syst. Biol. 6, 73 (2012)CrossRefGoogle Scholar
  31. 31.
    Covert, M.W., Schilling, C.H., Palsson, B.: Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213, 73–88 (2001)CrossRefGoogle Scholar
  32. 32.
    Shlomi, T., Eisenberg, Y., Sharan, R., Ruppin, E.: A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol. Syst. Biol. 3, 101 (2007)CrossRefGoogle Scholar
  33. 33.
    Jensen, P.A., Lutz, K.A., Papin, J.A.: TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks. BMC Syst. Biol. 5, 147 (2011)CrossRefGoogle Scholar
  34. 34.
    Schmidt, B.J., Ebrahim, A., Metz, T.O., Adkins, J.N., Palsson, B.Ø., Hyduke, D.R.: GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics 29, 2900–2908 (2013)CrossRefGoogle Scholar
  35. 35.
    Marmiesse, L., Peyraud, R., Cottret, L.: FlexFlux: combining metabolic flux and regulatory network analyses. BMC Syst. Biol. 9, 93 (2015)CrossRefGoogle Scholar
  36. 36.
    Motamedian, E., Mohammadi, M., Shojaosadati, S.A., Heydari, M.: TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data. Bioinformatics. 33, btw772 (2017)CrossRefGoogle Scholar
  37. 37.
    Banos, D.T., Trébulle, P., Elati, M.: Integrating transcriptional activity in genome-scale models of metabolism. BMC Syst. Biol. 11, 134 (2017)CrossRefGoogle Scholar
  38. 38.
    Lloyd, C.J., Ebrahim, A., Yang, L., King, Z.A., Catoiu, E., O’Brien, E.J., Liu, J.K., Palsson, B.O.: COBRAme: a computational framework for genome-scale models of metabolism and gene expression. PLoS Comput. Biol. 14, e1006302 (2018)CrossRefGoogle Scholar
  39. 39.
    Orth, J.D., Thiele, I., Palsson, B.O.: What is flux balance analysis? Nat. Publ. Gr. 28, 245–248 (2010)Google Scholar
  40. 40.
    Lewis, N.E., Hixson, K.K., Conrad, T.M., Lerman, J.A., Charusanti, P., Polpitiya, A.D., Adkins, J.N., Schramm, G., Purvine, S.O., Lopez-Ferrer, D., Weitz, K.K., Eils, R., König, R., Smith, R.D., Palsson, B.Ø.: Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models. Mol. Syst. Biol. 6, 390 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Centre of Biological EngineeringUniversity of MinhoBragaPortugal
  2. 2.Mathematics and Computer Science DivisionArgonne National LaboratoryArgonneUSA

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