Competition dynamics in long‐term propagations of <i>Schizosaccharomyces pombe</i> strain communities

Durão, Paulo; Amicone, Massimo; Perfeito, Lília; Gordo, Isabel

doi:10.1002/ece3.8191

Cited by 8 publications

(5 citation statements)

References 60 publications

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“…It should be noted that the predicted maximal growth rate in the minimal EMM2 medium (μ max = 0.29 h ‒1 ) is dependent on the minimal UP fraction in the proteome, a parameter we fit. However, we argue that our estimate is reasonable, since pcPombe correctly predicts the maximal growth rate on the rich YES medium with the same parameter values (μ max = 0.34 h ‒1 ) (Durão et al, 2021). To summarize, here we used the pcPombe model together with the existing knowledge on S. cerevisiae to verify the identity of proteome constraints, which actively limit growth in a condition-dependent manner.…”

Section: Resultsmentioning

confidence: 69%

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

Grigaitis

Grundel

Pelt-KleinJan

et al. 2022

Preprint

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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 use 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 behaviour and the role of resource allocation in driving different strategies in fission yeast.

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Section: Resultsmentioning

confidence: 69%

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

Grigaitis

Grundel

Pelt-KleinJan

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It should be noted that the predicted maximal growth rate in the EMM2 medium ( ) is dependent on the minimal UP fraction in the proteome, a parameter we fit. However, we argue that our estimate is reasonable, since pcPombe correctly predicts the maximal growth rate on the rich yeast extract with supplements (YES) medium with the same parameter values ( ) ( 31 ). To summarize, here, we used the pcPombe model together with the existing knowledge on S. cerevisiae to verify the identity of proteome constraints, which actively limit growth in a condition-dependent manner.…”

Section: Resultsmentioning

confidence: 77%

A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast

et al. 2022

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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.

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“…Laboratory evolution studies have been carried out in various antagonistic (usually, bacteria-phage [24,26,), mutualistic and commensal [141][142][143][144][145][146][147][148][149][150][151][152][153][154][155][156] systems with two or more [22,[157][158][159][160][161][162][163][164][165] species and in adaptive diversifications [61,106,107,[166][167][168][169][170][171][172]. While most of these studies do not focus on evolutionary repeatability per se, the data they collect show that various community-level properties, such as absolute and relative abundances of community members [111,145,146,173], species interactions [24,174,175], community growth rate [142,…”

Section: (Iii) Challengesmentioning

confidence: 99%

Evolutionary repeatability of emergent properties of ecological communities

Venkataram

Kryazhimskiy

2023

Phil. Trans. R. Soc. B

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Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.

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Competition dynamics in long‐term propagations of Schizosaccharomyces pombe strain communities

Cited by 8 publications

References 60 publications

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast

Evolutionary repeatability of emergent properties of ecological communities

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