Varner J, Ramkrishna D
School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA.
Biotechnol Prog. 1999 May;15(3):407-25. doi: 10.1021/bp990017p.
The theoretical basis of a cybernetic metabolic network design and analysis framework, which has been subsequently successfully applied to predict system response to genetic alteration, is presented. This conceptual methodology consists of three main branches, namely, a model realization framework, a representation of genetic alteration, and lastly, a metabolic design component. These concepts are introduced as a series of postulates that describe the basic tenets of the approach. Each branch is discussed in turn, starting with the cybernetic representation of arbitrarily complex metabolic networks. A set of postulates is put forth that affords the modular construction of cybernetic models of metabolic networks using as a base a library of elementary pathways. This is followed by a discussion of the representation of genetic alterations within the cybernetic framework. It is postulated that the objective of the base network and the altered system are identical (at least on the time scale required for the organism to "learn" new objectives). This implies, with respect to resource allocation, that the base network and its genetically altered counterpart may still be treated as optimal systems; however, the set of competing physiological choices open to the altered network expands or contracts depending upon the nature of the genetic perturbation. Lastly, to add a predictive design aspect to the methodology, we present a set of postulates that outline the application of metabolic control analysis to cybernetic model systems. We postulate that sensitivity coefficients computed from a cybernetic model, although still local in scope, have the added benefit of a systematic representation of regulatory function as described by the cybernetic variables. Thus, information gained from sensitivity measurements stemming from a cybernetic model include the explicit input of metabolic regulation, a component that is lacking in a purely kinetic representation of metabolic function. The sensitivity results can then be employed to develop qualitative strategies for the rational alteration of metabolic function, which can be evaluated by simulation of an appropriately modified cybernetic model of the base network.
本文介绍了一种控制论代谢网络设计与分析框架的理论基础，该框架随后已成功应用于预测系统对基因改变的反应。这种概念性方法由三个主要分支组成，即模型实现框架、基因改变的表示，以及最后一个代谢设计组件。这些概念被引入为一系列描述该方法基本原理的假设。依次讨论每个分支，首先是任意复杂代谢网络的控制论表示。提出了一组假设，这些假设允许使用基本途径库作为基础，以模块化方式构建代谢网络的控制论模型。接下来讨论控制论框架内基因改变的表示。假设基础网络和改变后的系统目标相同（至少在生物体“学习”新目标所需的时间尺度上）。这意味着，在资源分配方面，基础网络及其基因改变后的对应物仍可被视为最优系统；然而，改变后的网络可选择的竞争生理选择集根据基因扰动的性质而扩大或缩小。最后，为了给该方法增加预测设计方面的内容，我们提出了一组假设，概述了代谢控制分析在控制论模型系统中的应用。我们假设从控制论模型计算出的灵敏度系数虽然在范围上仍然是局部的，但具有由控制论变量描述的调节功能的系统表示的额外优势。因此，从控制论模型的灵敏度测量中获得的信息包括代谢调节的明确输入，这是代谢功能的纯动力学表示中所缺乏的一个组成部分。然后，灵敏度结果可用于制定合理改变代谢功能的定性策略，这可以通过对基础网络的适当修改后的控制论模型进行模拟来评估。
