A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Marco Vanoni, Pasquale Palumbo, Federico Papa, Stefano Busti, Laura Gotti, Meike Wortel, Bas Teusink, Ivan Orlandi, Alex Pessina, Cristina Airoldi, Luca Brambilla, Marina Vai, Lilia Alberghina
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引用次数: 0

Abstract

For unicellular organisms, the reproduction rate and growth are crucial fitness determinants and functional manifestations of the organism genotype. Using the budding yeast Saccharomyces cerevisiae as a model organism, we integrated metabolism, which provides energy and building blocks for growth, with cell mass growth and cell cycle progression into a low-granularity, multiscale (from cell to population) computational model. This model predicted that cells with constitutive respiration do not modulate cell size according to the growth conditions. We experimentally validated the model predictions using mutants with defects in the upper part of glycolysis or glucose transport. Plugging in molecular details of cellular subsystems allowed us to refine predictions from the cellular to the molecular level. Our hybrid multiscale modeling approach provides a framework for structuring molecular knowledge and predicting cell phenotypes under various genetic and environmental conditions.

一个整合代谢、生长和细胞周期的模块化模型预测,发酵需要调节酵母种群的细胞大小。
对于单细胞生物来说,繁殖率和生长是生物基因型的关键适应度决定因素和功能表现。我们以出芽酵母酿酒酵母(Saccharomyces cerevisiae)为模型生物,将为生长提供能量和基础的代谢、细胞质量生长和细胞周期进程整合为一个低粒度、多尺度(从细胞到群体)的计算模型。该模型预测具有本构呼吸的细胞不会根据生长条件调节细胞大小。我们用糖酵解或葡萄糖运输上部缺陷的突变体实验验证了模型预测。插入细胞子系统的分子细节使我们能够从细胞到分子水平改进预测。我们的混合多尺度建模方法为构建分子知识和预测各种遗传和环境条件下的细胞表型提供了框架。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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