Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism.
Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism.
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DOI：
10.1371/journal.pone.0139507
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Rocco A
中科院分区：
文献类型：
--
作者：
Mannan AA;Toya Y;Shimizu K;McFadden J;Kierzek AM;Rocco A
从动力学模型的动力学系统分析中寻求对细菌细胞的代谢谱的动力学的理解。这种建模形式主义依赖于酶动力学及其代谢物调节的确定性数学描述。然而，由于缺乏可用的动力学信息，它受到严重阻碍，限制了可以建模的系统的大小。此外，代谢网络的子系统，其动态可以建模面临着三个问题：如何参数化的模型与大多数不完整的稳态数据，如何关闭现在是一个固有的开放系统，以及如何占增长的影响。在这项研究中，我们通过利用多组学稳态数据和基因组规模的代谢网络模型来解决动力学建模的这些挑战。我们使用这些来生成参数，这些参数将嵌入在基因组尺度代谢网络模型中的知识整合到E.到目前为止实现的。作为一个应用，我们进行了动力系统分析所产生的丰富的模型。这揭示了中心碳代谢的双稳态性，因此它有可能表达两种不同的代谢状态。此外，由于我们的模型通知技术确保两个稳定状态都受到相同的物理可行的稳态增长率的约束，因此随之而来的双稳态代表了两个状态的暂时共存，并通过扩展，揭示了表型异质种群的出现。
An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-‘omics’ steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.