Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data
Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data
复制标题
通过实验数据的逻辑分析自动修正代谢网络
DOI：
10.1007/978-3-642-13840-9_18
复制
发表时间：
2009
影响因子：
3
通讯作者：
R. King
中科院分区：
文献类型：
--
作者：
O. Ray;Ken E. Whelan;R. King
本文通过对实验数据的逻辑分析，提出了一种用于代谢网络自动修正的非单调ILP方法。该方法在两个方面扩展了以前的工作：通过提出涉及添加和删除信息的修订；并建议修订涉及基因功能，酶抑制和代谢反应的组合。我们的建议是基于一个新的代谢的声明模型表达在一个非单调的逻辑规划形式化。对于该模型，使用溯因推理和归纳推理的混合来计算一组最小修正，以使给定网络与某些观测数据一致。通过这种方式，我们描述了一个名为XHAIL的推理系统如何能够根据名为Robot Scientist的自主实验室平台获得的真实实验数据，正确地修改最先进的代谢途径。
This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks through the logical analysis of experimental data. The method extends previous work in two respects: by suggesting revisions that involve both the addition and removal of information; and by suggesting revisions that involve combinations of gene functions, enzyme inhibitions, and metabolic reactions. Our proposal is based on a new declarative model of metabolism expressed in a nonmonotonic logic programming formalism. With respect to this model, a mixture of abductive and inductive inference is used to compute a set of minimal revisions needed to make a given network consistent with some observed data. In this way, we describe how a reasoning system called XHAIL was able to correctly revise a state-of-the-art metabolic pathway in the light of real-world experimental data acquired by an autonomous laboratory platform called the Robot Scientist.