文章基本信息

标题：Enumerating Minimal Active Metabolic Pathways by Model Generation
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作者：Takehide Soh ; Katsumi Inoue
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2012
卷号：27
期号：3
页码：204-212
DOI：10.1527/tjsai.27.204
出版社：The Japanese Society for Artificial Intelligence
摘要：In systems biology, identifying vital functions like glycolysis from a given metabolic pathway is important to understand living organisms. In this paper, we particularly focus on the problem of enumerating minimal active pathways producing target metabolites from source metabolites. We represent the problem in propositional formulas and solve it through minimal model generation. An advantage of our method is that each solution satisfies qualitative laws of biochemical reactions. Moreover, we can calculate such solutions for a cellular scale metabolic pathway within a few seconds. In experiments, we have applied our method to a whole Escherichia coli metabolic pathway. As a result, we found a minimal set of reactions corresponding to the conventional glycolysis pathway described in a biological database EcoCyc.
关键词：minimal model generation ; metabolic pathway ; propositional formula ; encoding ; Systems Biology