Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations
Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations
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DOI：
10.1016/j.ymben.2018.10.005
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发表时间：
2019-03-01
影响因子：
8.4
通讯作者：
Hatzimanikatis, Vassily
中科院分区：
文献类型：
--
作者：
Hameri, Tuure;Fengos, Georgios;Hatzimanikatis, Vassily
大规模动力学模型用于设计，预测和理解活细胞的代谢反应。动力学模型对于细胞中靶分子的生物合成特别有吸引力，因为它们在捕获复杂的细胞生物化学方面通常比其他类型的模型更好。使用更简单的化学计量模型作为支架，动力学模型建立在稳态通量分布和代谢物浓度矢量，通常通过优化确定。然而，由于潜在的优化问题是欠定的，即使在将可用的实验组学数据，不能唯一地确定代谢通量和代谢物浓度方面的操作配置。因此，一些反应可以正向或反向运作，同时仍然符合观察到的生理学。在这里，我们分析了细胞内通量和浓度的潜在不确定性如何影响代谢工程和系统生物学研究中构建的动力学模型及其设计的预测。为此，我们将最佳生长的大肠杆菌的组学数据整合到化学计量模型中，并构建了与大肠杆菌生理学一致的替代稳态溶液的非线性大规模动力学模型。大肠杆菌有氧代谢我们对这些模型进行了代谢控制分析（MCA），强调基于MCA的代谢工程决策受到所选稳态的强烈影响，并且似乎对浓度值而不是通量值更敏感。为了将其纳入未来的研究中，我们提出了一个工作流程，以实现与所有替代稳态解决方案一致的更可靠和更强大的预测。此工作流程可应用于所有动力学模型，以提高其预测的一致性和准确性。此外，我们表明，无论替代的稳态解决方案，磷酸果糖激酶的活性增加和ATP维持需求减少，将改善细胞生长的最佳生长的E。杆菌
Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grown Escherichia coli into a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of the E. coli aerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grown E. coli.