Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.
Bioinformatics. 2010 Feb 15;26(4):529-35. doi: 10.1093/bioinformatics/btp701. Epub 2009 Dec 22.
Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information.
Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein-protein interaction, genetic interaction, domain-domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view.
许多生物现象涉及细胞中存在的许多生物途径之间的广泛相互作用。然而，提取所有内在的生物途径仍然是系统生物学中的一个主要挑战。随着高通量功能基因组技术的出现，现在可以通过整合不同的生物信息，以系统的方式推断生物途径和途径组织。
在这里，我们提出了一种使用网络拓扑结构来预测生物途径的新的综合方法。我们整合了四种类型的生物证据（蛋白质-蛋白质相互作用、遗传相互作用、结构域-结构域相互作用和基因本体论术语的语义相似性）来生成一个功能相关的网络。然后，该网络被用于开发一种新的途径发现算法，以预测酵母中的生物途径。我们的方法在酵母中发现了 195 种生物途径和 31 对功能冗余的途径对。通过将我们鉴定的途径与三个公共途径数据库（KEGG、BioCyc 和 Reactome）进行比较，我们观察到我们的方法实现了 12.8%的最大阳性预测值，并优于其他预测方法。这项研究使我们能够重建生物途径，并以系统的视角描绘细胞机制。
