| Autor: |
Grigaitis P; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands., Grundel DAJ; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands., van Pelt-KleinJan E; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.; TiFN, Wageningen, The Netherlands., Isaku M; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands., Xie G; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands., Mendoza Farias S; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands., Teusink B; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands., van Heerden JH; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. |
| Abstrakt: |
The fission yeast, Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker's yeast, Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually curated high-quality reconstruction of S. pombe 's metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behavior and the role of resource allocation in driving different strategies in fission yeast. IMPORTANCE Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species thus remains largely unexplored in mechanistic terms. Computational modeling of metabolic networks offers an attractive platform to bridge the knowledge gap and gain new insights into physiology of lesser-studied organisms. Here we showcase an example of successful knowledge transfer from the budding yeast Saccharomyces cerevisiae to a popular model organism in molecular and cell biology, fission yeast Schizosaccharomyces pombe, using computational models. |
