表征丝状酵母菌群形态的离格离散模型

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-11-21 DOI:10.1371/journal.pcbi.1012605
Kai Li, J Edward F Green, Hayden Tronnolone, Alexander K Y Tam, Andrew J Black, Jennifer M Gardner, Joanna F Sundstrom, Vladimir Jiranek, Benjamin J Binder
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引用次数: 0

摘要

我们将基于非晶格代理的数学模型与实验相结合,探索酵母菌群的丝状生长。在环境压力下,酿酒酵母细胞可从双极(饱食)萌发机制过渡到单极(假头)萌发机制,在单极萌发机制中,细胞伸长并端对端萌发。这种出芽的不对称产生了空间上的非均匀生长,菌丝从菌落中心向外延伸,觅食。我们使用近似贝叶斯计算方法来量化实验中观察到的单个细胞出芽机制是如何产生空间模式的。我们将这种参数推断方法应用于低养分和高养分环境下两种 S. cerevisiae 菌株菌落的实验图像。从饱食生长向假茎生长过渡时的菌落大小以及假茎细胞增殖的分叉机制是驱动菌落形态的关键特征。利用最有可能推断出的参数进行模拟,得出的菌落形态与实验结果非常相似。
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An off-lattice discrete model to characterise filamentous yeast colony morphology.

We combine an off-lattice agent-based mathematical model and experimentation to explore filamentous growth of a yeast colony. Under environmental stress, Saccharomyces cerevisiae yeast cells can transition from a bipolar (sated) to unipolar (pseudohyphal) budding mechanism, where cells elongate and bud end-to-end. This budding asymmetry yields spatially non-uniform growth, where filaments extend away from the colony centre, foraging for food. We use approximate Bayesian computation to quantify how individual cell budding mechanisms give rise to spatial patterns observed in experiments. We apply this method of parameter inference to experimental images of colonies of two strains of S. cerevisiae, in low and high nutrient environments. The colony size at the transition from sated to pseudohyphal growth, and a forking mechanism for pseudohyphal cell proliferation are the key features driving colony morphology. Simulations run with the most likely inferred parameters produce colony morphologies that closely resemble experimental results.

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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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