基于栅格的蒙特卡罗模拟营养浓度和磁场暴露对酵母菌落生长和形态的影响。

Q2 Medicine In Silico Biology Pub Date : 2021-01-01 DOI:10.3233/ISB-210233
Rebekah Hall, Daniel A Charlebois
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引用次数: 1

摘要

酵母菌存在于群体中,在空间和时间上扩展,形成复杂的结构和图案。我们开发了一个基于网格的框架,对暴露于不同营养和磁场条件下的出芽酵母菌落进行时空蒙特卡罗模拟。单倍体和二倍体酵母细胞的出芽模式被纳入框架,以及在营养限制条件下在酵母菌落中发生的丝状生长。该框架的模拟预测磁场会降低菌落的生长速度、硬度和圆度。磁场模拟进一步预测了菌落延伸和边界波动以营养和倍性依赖的方式增加。这些计算机预测是理解物理化学环境对微生物菌落影响的重要一步,也是为酵母菌落生物膜和真菌病原体的生物电磁实验提供信息的重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Lattice-based Monte Carlo simulation of the effects of nutrient concentration and magnetic field exposure on yeast colony growth and morphology.

Yeasts exist in communities that expand over space and time to form complex structures and patterns. We developed a lattice-based framework to perform spatial-temporal Monte Carlo simulations of budding yeast colonies exposed to different nutrient and magnetic field conditions. The budding patterns of haploid and diploid yeast cells were incorporated into the framework, as well as the filamentous growth that occurs in yeast colonies under nutrient limiting conditions. Simulation of the framework predicted that magnetic fields decrease colony growth rate, solidity, and roundness. Magnetic field simulations further predicted that colony elongation and boundary fluctuations increase in a nutrient- and ploidy-dependent manner. These in-silico predictions are an important step towards understanding the effects of the physico-chemical environment on microbial colonies and for informing bioelectromagnetic experiments on yeast colony biofilms and fungal pathogens.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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