Optimal metabolic regulation using a constraint-based model.
Optimal metabolic regulation using a constraint-based model.
复制标题
使用基于约束的模型进行最佳代谢调节。
DOI：
10.1142/9781848163003_0014
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Segrè,Daniel
中科院分区：
文献类型：
--
作者：
Riehl,WilliamJ;Segrè,Daniel
代谢酶的调节在维持代谢稳态和生命系统在多种环境条件下进行生理适应的能力中起着至关重要的作用。代谢调控是通过转录和转录后机制的复杂相互作用来实现的，其中一些机制已经通过实验表征了特定的途径和生物体。然而，许多细节，包括大多数动力学参数的值，已被证明难以阐明。因此，理解代谢调节策略的基本原理是一个持续的挑战。在代谢网络的基因组尺度稳态模型的背景下，已经表明进化可以驱动代谢网络朝向达到计算可预测的最佳状态，例如最大生长能力。在这里，我们开发了一种新的计算方法的基础上的假设，即调控系统的代谢网络上运行已经朝着一个最佳的架构以及。具体来说，我们假设代谢调节网络的拓扑结构已被选择用于最佳地保持系统在一个或多个稳态附近的平衡。基于这些假设，我们使用相关的通量平衡分析的方法来构建一个模型的代谢调控主要基于代谢网络的拓扑结构，绕过所有动力学参数的细节的要求。该模型预测了代谢相互作用的最佳调节网络，该网络可以解决代谢系统中给定稳态的扰动。我们探索的能力的模型来预测最佳的监管反应，在一个简单的玩具网络和一个片段的糖酵解途径。
Regulation of metabolic enzymes plays a crucial role in the maintenance of metabolic homeostasis, and in the capacity of living systems to undergo physiological adaptation under multiple environmental conditions. Metabolic regulation is achieved through a complex interplay of transcriptional and post-transcriptional mechanisms, some of which have been experimentally characterized for specific pathways and organisms. Many of the details, however, including the values of most kinetic parameters, have proven difficult to elucidate. Hence, understanding the principles that underlie metabolic regulation strategies constitutes an ongoing challenge. In the context of genome-scale steady state models of metabolic networks, it has been shown that evolution may drive metabolic networks towards reaching computationally predictable optimal states, such as maximal growth capacity. Here we develop a new computational approach based on the hypothesis that the regulatory systems operating on metabolic networks have evolved towards an optimal architecture as well. Specifically, we hypothesize that the topology of metabolic regulation networks has been selected for optimally maintaining the system balanced around one or more steady states. Based on these hypotheses, we use methods related to flux balance analysis to construct a model of metabolic regulation based primarily on a metabolic network's topology, bypassing the requirement for the details of all kinetic parameters. This model predicts an optimal regulatory network of metabolic interactions that can resolve perturbations to a given steady state in a metabolic system. We explore the ability of the model to predict optimal regulatory responses in both a simple toy network and in a fragment of the well-described glycolysis pathway.