Community flux balance analysis for microbial consortia at balanced growth.
Community flux balance analysis for microbial consortia at balanced growth.
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DOI：
10.1371/journal.pone.0064567
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Bruggeman FJ
中科院分区：
文献类型：
--
作者：
Khandelwal RA;Olivier BG;Röling WF;Teusink B;Bruggeman FJ
微生物群落研究的一个中心焦点是阐明基因型、表型和动态群落结构之间的关系。在这里，我们提出了一种新的计算方法，称为社区通量平衡分析（cFBA）来研究微生物群落的代谢行为。cFBA整合了个体微生物在（基因组规模）代谢化学计量模型方面的综合代谢能力，以及群落中物种和非生物过程之间的代谢相互作用。此外，cFBA考虑了来自反应化学计量、反应热力学和生态系统的约束。cFBA预测平衡增长的群落的最大群落增长率，微生物内部和之间的代谢反应所需的速率和相对物种丰度。为了预测最佳群落增长率下的物种丰度和代谢活动，需要求解一个非线性优化问题。我们概述了cFBA的方法，并用两个微生物群落的例子说明了这种方法。这些实施例说明了cFBA的两个有用的应用。首先，cFBA可用于研究反应能力的特定生化限制如何导致微生物聚生体可能遇到的不同类型的代谢限制。这些最大容量的计算机模拟变化允许对财团对各种代谢和环境约束的响应进行全局观察。其次，cFBA对于比较不同代谢交叉喂养策略的性能非常有用，以找到与实验数据一致的策略或对微生物群落最有效的策略。
A central focus in studies of microbial communities is the elucidation of the relationships between genotype, phenotype, and dynamic community structure. Here, we present a new computational method called community flux balance analysis (cFBA) to study the metabolic behavior of microbial communities. cFBA integrates the comprehensive metabolic capacities of individual microorganisms in terms of (genome-scale) stoichiometric models of metabolism, and the metabolic interactions between species in the community and abiotic processes. In addition, cFBA considers constraints deriving from reaction stoichiometry, reaction thermodynamics, and the ecosystem. cFBA predicts for communities at balanced growth the maximal community growth rate, the required rates of metabolic reactions within and between microbes and the relative species abundances. In order to predict species abundances and metabolic activities at the optimal community growth rate, a nonlinear optimization problem needs to be solved. We outline the methodology of cFBA and illustrate the approach with two examples of microbial communities. These examples illustrate two useful applications of cFBA. Firstly, cFBA can be used to study how specific biochemical limitations in reaction capacities cause different types of metabolic limitations that microbial consortia can encounter. In silico variations of those maximal capacities allow for a global view of the consortium responses to various metabolic and environmental constraints. Secondly, cFBA is very useful for comparing the performance of different metabolic cross-feeding strategies to either find one that agrees with experimental data or one that is most efficient for the community of microorganisms.
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