论文标题

时间和空间中微生物生态系统的计算(彗星):用于建模生态系统代谢的开源协作平台

Computation Of Microbial Ecosystems in Time and Space (COMETS): An open source collaborative platform for modeling ecosystems metabolism

论文作者

Dukovski, Ilija, Bajić, Djordje, Chacón, Jeremy M, Quintin, Michael, Vila, Jean CC, Sulheim, Snorre, Pacheco, Alan R, Bernstein, David B, Rieh, William J, Korolev, Kirill S, Sanchez, Alvaro, Harcombe, William R, Segrè, Daniel

论文摘要

代谢的基因组规模化学计量模型已成为建模细胞生理和生长的标准系统生物学工具。这种方法的扩展也成为预测,理解和设计微生物群落的宝贵途径。彗星(在时间和空间中的微生物生态系统的计算)最初是作为动态通量平衡分析的扩展而开发的,该分析结合了细胞和分子扩散,从而在空间结构化的环境中对多个微生物物种进行了模拟。在这里,我们描述了如何最好地使用和应用该平台Comets 2的最新版本,该版本结合了生长时微生物生物量扩展的更准确的生物物理模型,以及几个新的生物模拟模块,包括进化动力学和细胞外酶活性。 COMETS 2提供了与良好的眼镜蛇模型和方法兼容的用户友好型Python和MATLAB接口,以及全面的文档和教程,从而促进了与代谢模拟的各级专业知识的研究人员使用彗星的使用。该协议为安装,测试和应用彗星2在不同情况下提供了详细的指南,对跨生物群体和尺度的微生物群落具有广泛的适用性。

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales.

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