Wang Liqing, Birol Inanç, Hatzimanikatis Vassily
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60616, USA.
Biophys J. 2004 Dec;87(6):3750-63. doi: 10.1529/biophysj.104.048090. Epub 2004 Oct 1.
Information about the enzyme kinetics in a metabolic network will enable understanding of the function of the network and quantitative prediction of the network responses to genetic and environmental perturbations. Despite recent advances in experimental techniques, such information is limited and existing experimental data show extensive variation and they are based on in vitro experiments. In this article, we present a computational framework based on the well-established (log)linear formalism of metabolic control analysis. The framework employs a Monte Carlo sampling procedure to simulate the uncertainty in the kinetic data and applies statistical tools for the identification of the rate-limiting steps in metabolic networks. We applied the proposed framework to a branched biosynthetic pathway and the yeast glycolysis pathway. Analysis of the results allowed us to interpret and predict the responses of metabolic networks to genetic and environmental changes, and to gain insights on how uncertainty in the kinetic mechanisms and kinetic parameters propagate into the uncertainty in predicting network responses. Some of the practical applications of the proposed approach include the identification of drug targets for metabolic diseases and the guidance for design strategies in metabolic engineering for the purposeful manipulation of the metabolism of industrial organisms.
代谢网络中酶动力学的信息将有助于理解网络的功能，并对网络对遗传和环境扰动的响应进行定量预测。尽管最近实验技术有所进步，但此类信息仍然有限，现有的实验数据显示出广泛的差异，并且它们是基于体外实验的。在本文中，我们提出了一个基于代谢控制分析中成熟的（对数）线性形式的计算框架。该框架采用蒙特卡罗采样程序来模拟动力学数据中的不确定性，并应用统计工具来识别代谢网络中的限速步骤。我们将所提出的框架应用于一个分支生物合成途径和酵母糖酵解途径。对结果的分析使我们能够解释和预测代谢网络对遗传和环境变化的响应，并深入了解动力学机制和动力学参数中的不确定性如何传播到预测网络响应的不确定性中。所提出方法的一些实际应用包括识别代谢疾病的药物靶点以及为代谢工程中的设计策略提供指导，以便有目的地操纵工业生物体的代谢。
