Klamt Steffen, Gilles Ernst Dieter
Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr.1, D-39106 Magdeburg, Germany.
Bioinformatics. 2004 Jan 22;20(2):226-34. doi: 10.1093/bioinformatics/btg395.
Structural studies of metabolic networks yield deeper insight into topology, functionality and capabilities of the metabolisms of different organisms. Here, we address the analysis of potential failure modes in metabolic networks whose occurrence will render the network structurally incapable of performing certain functions. Such studies will help to identify crucial parts in the network structure and to find suitable targets for repressing undesired metabolic functions.
We introduce the concept of minimal cut sets for biochemical networks. A minimal cut set (MCS) is a minimal (irreducible) set of reactions in the network whose inactivation will definitely lead to a failure in certain network functions. We present an algorithm which enables the computation of the MCSs in a given network related to user-defined objective reactions. This algorithm operates on elementary modes. A number of potential applications are outlined, including network verifications, phenotype predictions, assessing structural robustness and fragility, metabolic flux analysis and target identification in drug discovery. Applications are illustrated by the MCSs in the central metabolism of Escherichia coli for growth on different substrates.
Computation and analysis of MCSs is an additional feature of the FluxAnalyzer (freely available for academic users upon request, special contracts for industrial companies; see web page below).
代谢网络的结构研究能让我们更深入地了解不同生物体代谢的拓扑结构、功能和能力。在此，我们探讨代谢网络中潜在故障模式的分析，这些故障模式的出现会使网络在结构上无法执行某些功能。此类研究将有助于识别网络结构中的关键部分，并找到抑制不期望代谢功能的合适靶点。
我们引入了生化网络最小割集的概念。最小割集（MCS）是网络中一组最小（不可约）的反应，其失活肯定会导致某些网络功能失效。我们提出了一种算法，能够计算给定网络中与用户定义的目标反应相关的最小割集。该算法基于基本模式运行。概述了许多潜在应用，包括网络验证、表型预测、评估结构稳健性和脆弱性、代谢通量分析以及药物发现中的靶点识别。通过大肠杆菌在不同底物上生长时中心代谢的最小割集来说明这些应用。
最小割集的计算和分析是FluxAnalyzer的一项附加功能（学术用户可根据要求免费获取，工业公司需签订特殊合同；见下方网页）。
