Eveillard Damien, Bouskill Nicholas J, Vintache Damien, Gras Julien, Ward Bess B, Bourdon Jérémie
LS2N, UMR6004 CNRS, Université de Nantes, Centrale Nantes, IMTA, Nantes, France.
Research Federation (FR2022) Tara Oceans GO-SEE, Paris, France.
Front Microbiol. 2019 Jan 28;9:3298. doi: 10.3389/fmicb.2018.03298. eCollection 2018.
Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.
充分理解微生物群落与其环境之间的相互作用，以便根据物理化学参数预测多样性，这是微生物生态学的一项基本追求，但我们仍然难以实现。然而，对微生物群落进行建模存在问题，因为（i）群落很复杂，（ii）大多数描述是定性的，并且（iii）对群落与其周围环境相互作用方式的定量理解仍然不完整。克服这些复杂性的一种方法是将部分定性和定量描述整合到更复杂的网络中。在这里，我们概述了一种基于事件转移图（ETG）理论的概率框架的开发，以预测观察到的化学数据中的微生物群落结构。通过逆向工程，我们从ETG中得出概率，这些概率准确地代表了实验观察结果，并预测了动态环境中群落的假定限制。这些预测可以通过强调表征微生物生态系统所需的最重要的功能反应和相关微生物菌株，反馈到未来的野外实验发展中。
