PathMiner：通过启发式搜索预测代谢途径。
PathMiner: predicting metabolic pathways by heuristic search.
作者信息
McShan D C, Rao S, Shah I
机构信息
School of Medicine, University of Colorado, 4200 East Ninth Avenue, C-245 Denver, Colorado 80262, USA.
出版信息
Bioinformatics. 2003 Sep 1;19(13):1692-8. doi: 10.1093/bioinformatics/btg217.
MOTIVATION
Automated methods for biochemical pathway inference are becoming increasingly important for understanding biological processes in living and synthetic systems. With the availability of data on complete genomes and increasing information about enzyme-catalyzed biochemistry it is becoming feasible to approach this problem computationally. In this paper we present PathMiner, a system for automatic metabolic pathway inference. PathMiner predicts metabolic routes by reasoning over transformations using chemical and biological information.
RESULTS
We build a biochemical state-space using data from known enzyme-catalyzed transformations in Ligand, including, 2917 unique transformations between 3890 different compounds. To predict metabolic pathways we explore this state-space by developing an informed search algorithm. For this purpose we develop a chemically motivated heuristic to guide the search. Since the algorithm does not depend on predefined pathways, it can efficiently identify plausible routes using known biochemical transformations.
动机
用于生化途径推断的自动化方法对于理解生命系统和合成系统中的生物过程变得越来越重要。随着完整基因组数据的可得性以及关于酶催化生物化学的信息不断增加，通过计算方法解决这个问题变得可行。在本文中，我们介绍了PathMiner，一个用于自动代谢途径推断的系统。PathMiner通过利用化学和生物学信息对转化进行推理来预测代谢途径。
结果
我们利用来自Ligand中已知酶催化转化的数据构建了一个生化状态空间，包括3890种不同化合物之间的2917种独特转化。为了预测代谢途径，我们通过开发一种启发式搜索算法来探索这个状态空间。为此，我们开发了一种基于化学的启发式方法来指导搜索。由于该算法不依赖于预定义的途径，它可以使用已知的生化转化有效地识别合理的途径。
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