An Integrated Model Quantitatively Describing Metabolism, Growth and Cell Cycle in Budding Yeast

  • Pasquale Palumbo
  • Marco Vanoni
  • Federico Papa
  • Stefano Busti
  • Meike Wortel
  • Bas Teusink
  • Lilia Alberghina
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 830)

Abstract

Computational models are expected to increase understanding of how complex biological functions arise from the interactions of large numbers of gene products and biologically active low molecular weight molecules. Recent studies underline the need to develop quantitative models of the whole cell in order to tackle this challenge and to accelerate biological discoveries.

In this work we describe three major functions of a yeast cell: Metabolism, Growth and Cycle, through two coarse grain models, MeGro (Metabolism + Growth) and GroCy (Growth + Cycle). GroCy effectively recapitulates major phenotypic properties of cells grown in glucose and ethanol supplement media. MeGro can act as a parameter generator for GroCy. The resulting iMeGroCy integrated model can be used as a scaffold for molecularly detailed models of yeast functions.

Keywords

Computational models Systems biology Whole cell models 

References

  1. 1.
    Sánchez, B.J., Nielsen, J.: Genome scale models of yeast: towards standardized evaluation and consistent omic integration. Integr. Biol. (Camb) 7, 846–858 (2015)CrossRefGoogle Scholar
  2. 2.
    Botstein, D., Fink, G.R.: Yeast: an experimental organism for 21st Century biology. Genetics 189, 695–704 (2011)CrossRefGoogle Scholar
  3. 3.
    Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B., Assad-Garcia, N., Glass, J.I., Covert, M.W.: A whole-cell computational model predicts phenotype from genotype. Cell 150, 389–401 (2012)CrossRefGoogle Scholar
  4. 4.
    Goffeau, A., Barrell, B.G., Bussey, H., Davis, R.W., Dujon, B., Feldmann, H., Galibert, F., Hoheisel, J.D., Jacq, C., Johnston, M., Louis, E.J., Mewes, H.W., Murakami, Y., Philippsen, P., Tettelin, H., Oliver, S.G.: Life with 6000 genes. Science 274(546), 563–567 (1996)Google Scholar
  5. 5.
    Conrad, M., Schothorst, J., Kankipati, H.N., Van Zeebroeck, G., Rubio-Texeira, M., Thevelein, J.M.: Nutrient sensing and signaling in the yeast Saccharomyces cerevisiae. FEMS Microbiol. Rev. 38, 254–299 (2014)CrossRefGoogle Scholar
  6. 6.
    Macklin, D.N., Ruggero, N.A., Covert, M.W.: The future of whole-cell modeling. Curr. Opin. Biotechnol. 28, 111–115 (2014)CrossRefGoogle Scholar
  7. 7.
    Molenaar, D., van Berlo, R., de Ridder, D., Teusink, B.: Shifts in growth strategies reflect tradeoffs in cellular economics. Mol. Syst. Biol. 5, 323 (2009)CrossRefGoogle Scholar
  8. 8.
    von der Haar, T.: A quantitative estimation of the global translational activity in logarithmically growing yeast cells. BMC Syst. Biol. 2, 87 (2008)CrossRefGoogle Scholar
  9. 9.
    Waldron, C., Jund, R., Lacroute, F.: The elongation rate of proteins of different molecular weight classes in yeast. FEBS Lett. 46, 11–16 (1974)CrossRefGoogle Scholar
  10. 10.
    Boehlke, K.W., Friesen, J.D.: Cellular content of ribonucleic acid and protein in Saccharomyces cerevisiae as a function of exponential growth rate: calculation of the apparent peptide chain elongation rate. J. Bacteriol. 121, 429–433 (1975)Google Scholar
  11. 11.
    Waldron, C., Lacroute, F.: Effect of growth rate on the amounts of ribosomal and transfer ribonucleic acids in yeast. J. Bacteriol. 122, 855–865 (1975)Google Scholar
  12. 12.
    Wortel, M.T., Bosdriesz, E., Teusink, B., Bruggeman, F.J.: Evolutionary pressures on microbial metabolic strategies in the chemostat. Sci. Rep. 6, 29503 (2016)CrossRefGoogle Scholar
  13. 13.
    Van Hoek, P., Van Dijken, J.P., Pronk, J.T.: Effect of specific growth rate on fermentative capacity of baker’s yeast. Appl. Environ. Microbiol. 64, 4226–4233 (1998)Google Scholar
  14. 14.
    Alberghina, L., Mavelli, G., Drovandi, G., Palumbo, P., Pessina, S., Tripodi, F., Coccetti, P., Vanoni, M.: Cell growth and cell cycle in Saccharomyces cerevisiae: basic regulatory design and protein-protein interaction network. Biotechnol. Adv. 30, 52–72 (2012)CrossRefGoogle Scholar
  15. 15.
    Porro, D., Vai, M., Vanoni, M., Alberghina, L., Hatzis, C.: Analysis and modeling of growing budding yeast populations at the single cell level. Cytom Part J. Int. Soc. Anal. Cytol. 75, 114–120 (2009)CrossRefGoogle Scholar
  16. 16.
    Porro, D., Brambilla, L., Alberghina, L.: Glucose metabolism and cell size in continuous cultures of Saccharomyces cerevisiae. FEMS Microbiol. Lett. 229, 165–171 (2003)CrossRefGoogle Scholar
  17. 17.
    Alberghina, L., Mariani, L., Martegani, E.: Cell cycle modelling. Biosystems 19, 23–44 (1986)CrossRefGoogle Scholar
  18. 18.
    Di Talia, S., Skotheim, J.M., Bean, J.M., Siggia, E.D., Cross, F.R.: The effects of molecular noise and size control on variability in the budding yeast cell cycle. Nature 448, 947–951 (2007)CrossRefGoogle Scholar
  19. 19.
    Hartwell, L.H., Unger, M.W.: Unequal division in Saccharomyces cerevisiae and its implications for the control of cell division. J. Cell Biol. 75, 422–435 (1977)CrossRefGoogle Scholar
  20. 20.
    Palumbo, P., Vanoni, M., Cusimano, V., Busti, S., Marano, F., Manes, C., Alberghina, L.: Whi5 phosphorylation embedded in the G1/S network dynamically controls critical cell size and cell fate. Nat. Commun. 7, ncomms11372 (2016)Google Scholar
  21. 21.
    Jorgensen, P., Edgington, N.P., Schneider, B.L., Rupes, I., Tyers, M., Futcher, B.: The size of the nucleus increases as yeast cells grow. Mol. Biol. Cell 18, 3523–3532 (2007)CrossRefGoogle Scholar
  22. 22.
    Alberghina, L., Rossi, R.L., Querin, L., Wanke, V., Vanoni, M.: A cell sizer network involving Cln3 and Far1 controls entrance into S phase in the mitotic cycle of budding yeast. J. Cell Biol. 167, 433–443 (2004)CrossRefGoogle Scholar
  23. 23.
    Fu, X., Ng, C., Feng, D., Liang, C.: Cdc48p is required for the cell cycle commitment point at Start via degradation of the G1-CDK inhibitor Far1p. J. Cell Biol. 163, 21 (2003)CrossRefGoogle Scholar
  24. 24.
    McKinney, J.D., Chang, F., Heintz, N., Cross, F.R.: Negative regulation of FAR1 at the Start of the yeast cell cycle. Genes Dev. 7, 833–843 (1993)CrossRefGoogle Scholar
  25. 25.
    Chang, F., Herskowitz, I.: Phosphorylation of FAR1 in response to alpha-factor: a possible requirement for cell-cycle arrest. Mol. Biol. Cell 3, 445–450 (1992)CrossRefGoogle Scholar
  26. 26.
    Peter, M., Gartner, A., Horecka, J., Ammerer, G., Herskowitz, I.: FAR1 links the signal transduction pathway to the cell cycle machinery in yeast. Cell 73, 747–760 (1993)CrossRefGoogle Scholar
  27. 27.
    Barberis, M., Klipp, E., Vanoni, M., Alberghina, L.: Cell size at S Phase Initiation: An Emergent Property of the G1/S Network. PLoS Comput. Biol. 3, e64 (2007)CrossRefGoogle Scholar
  28. 28.
    Johnston, G.C., Ehrhardt, C.W., Lorincz, A., Carter, B.L.: Regulation of cell size in the yeast Saccharomyces cerevisiae. J. Bacteriol. 137, 1–5 (1979)Google Scholar
  29. 29.
    Alberghina, L., Vai, M., Vanoni, M.: Probing control mechanisms of cell cycle and ageing in budding yeast. Curr. Genomics 5, 615–627 (2004)CrossRefGoogle Scholar
  30. 30.
    Vanoni, M., Vai, M., Popolo, L., Alberghina, L.: Structural heterogeneity in populations of the budding yeast Saccharomyces cerevisiae. J. Bacteriol. 156, 1282–1291 (1983)Google Scholar
  31. 31.
    Chen, K.C., Calzone, L., Csikasz-Nagy, A., Cross, F.R., Novak, B., Tyson, J.J.: Integrative analysis of cell cycle control in budding yeast. Mol. Biol. Cell 15, 3841–3862 (2004)CrossRefGoogle Scholar
  32. 32.
    Kaizu, K., Ghosh, S., Matsuoka, Y., Moriya, H., Shimizu-Yoshida, Y., Kitano, H.: A comprehensive molecular interaction map of the budding yeast cell cycle. Mol. Syst. Biol. 6, 415 (2010)CrossRefGoogle Scholar
  33. 33.
    Spiesser, T.W., Müller, C., Schreiber, G., Krantz, M., Klipp, E.: Size homeostasis can be intrinsic to growing cell populations and explained without size sensing or Signal. FEBS J. 279, 4213–4230 (2012)CrossRefGoogle Scholar
  34. 34.
    Brümmer, A., Salazar, C., Zinzalla, V., Alberghina, L., Höfer, T.: Mathematical modelling of DNA replication reveals a trade-off between coherence of origin activation and robustness against rereplication. PLoS Comput. Biol. 6, e1000783 (2010)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Barberis, M., Linke, C., Adrover, M.À., González-Novo, A., Lehrach, H., Krobitsch, S., Posas, F., Klipp, E.: Sic1 plays a role in timing and oscillatory behaviour of B-type cyclins. Biotechnol. Adv. 30, 108–130 (2012)CrossRefGoogle Scholar
  36. 36.
    Swierstra, T., Vermeulen, N., Braeckman, J., van Driel, R.: Rethinking the life sciences. To better serve society, biomedical research has to regain its trust and get organized to tackle larger projects. EMBO Rep. 14, 310–314 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Pasquale Palumbo
    • 1
    • 2
  • Marco Vanoni
    • 1
    • 3
  • Federico Papa
    • 1
    • 2
  • Stefano Busti
    • 1
    • 3
  • Meike Wortel
    • 4
    • 5
  • Bas Teusink
    • 4
  • Lilia Alberghina
    • 1
    • 3
  1. 1.SYSBIO Centre for Systems BiologyMilanItaly
  2. 2.Institute for System Analysis and Computer Science “Antonio Ruberti” – CNRRomeItaly
  3. 3.Department of Biotechnology and BiosciencesUniversity of Milano-BicoccaMilanItaly
  4. 4.Systems BioinformaticsVU UniversityAmsterdamThe Netherlands
  5. 5.Centre for Ecological and Evolutionary Synthesis (CEES), The Department of BiosciencesUniversity of OsloOsloNorway

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