More is Different: Metabolic Modeling of Diverse Microbial Communities.

More is Different: Metabolic Modeling of Diverse Microbial Communities.
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更多就是不同:不同微生物群落的代谢模型。

DOI:
10.1128/msystems.01270-22
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发表时间:
2023-04-27
期刊:
影响因子:
6.4
通讯作者:
--
中科院分区:
生物学2区
文献类型:
--
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从土壤中的固氮到向动物宿主提供代谢分解产物,微生物联合体驱动着基本的过程。然而,将微生物联合体的组成转化为它们的新功能是具有挑战性的。社区规模的代谢模型有可能在给定的环境背景下模拟复杂微生物群落的输出,但目前对于整个群落在存在生态相互作用时的适应度函数应该是什么样子,以及整个社区的增长是否接近最大值,还没有达成共识。从单分类单元基因组规模的代谢模型过渡到多分类单元模型意味着没有针对单个分类单元的明确的生长速率解决方案的生长锥体。在这里,我们认为动态方法自然克服了这些限制,但它们是以计算成本为代价的。此外,我们展示了两种非动态的、稳态的方法如何逼近动态轨迹,并在改进的计算可伸缩性下从社区增长锥体中挑选生态相关的解决方案。
Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.
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