Biological process linkage networks.
Biological process linkage networks.
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DOI：
10.1371/journal.pone.0005313
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发表时间：
2009
期刊：
影响因子：
3.7
通讯作者：
Kasif S
中科院分区：
文献类型：
--
作者：
Dotan-Cohen D;Letovsky S;Melkman AA;Kasif S
研究复杂生物网络的传统方法是基于识别信号或代谢途径的内部组件之间的相互作用。相比之下，人们对高级生物系统之间的相互作用知之甚少，例如生物途径和过程。我们提出了一种通过分析蛋白质-蛋白质相互作用、转录共表达和遗传相互作用来收集生物过程之间相互作用模式的方法。该方法的核心是链接过程的概念以及由此产生的生物过程网络，即过程链接网络（PLN）。我们构建、编目和分析源自不同数据源和不同物种的不同类型的 PLN。当应用于基因本体时，许多由此产生的链接将层次结构中彼此远离的过程连接起来，尽管这种连接在生物学上具有重要意义。然而，其他一些则带有令人惊讶的因素，可能反映了所研究的生物体所独有的机制。在这方面，我们的方法补充了基因本体论固有的过程之间的链接结构，其本质上是与物种无关的。作为过程链接的实际应用，我们证明它可以有效地用于蛋白质功能预测，当仔细集成到预测方法中时，能够提高预测的覆盖范围和准确性。我们的方法为理解细胞作为一个系统的更高层次的组织提供了一个有前途的新方向，这应该有助于当前重新设计本体论的努力，并提高我们预测哪些蛋白质参与特定生物过程的能力。
The traditional approach to studying complex biological networks is based on the identification of interactions between internal components of signaling or metabolic pathways. By comparison, little is known about interactions between higher order biological systems, such as biological pathways and processes. We propose a methodology for gleaning patterns of interactions between biological processes by analyzing protein-protein interactions, transcriptional co-expression and genetic interactions. At the heart of the methodology are the concept of Linked Processes and the resultant network of biological processes, the Process Linkage Network (PLN). We construct, catalogue, and analyze different types of PLNs derived from different data sources and different species. When applied to the Gene Ontology, many of the resulting links connect processes that are distant from each other in the hierarchy, even though the connection makes eminent sense biologically. Some others, however, carry an element of surprise and may reflect mechanisms that are unique to the organism under investigation. In this aspect our method complements the link structure between processes inherent in the Gene Ontology, which by its very nature is species-independent. As a practical application of the linkage of processes we demonstrate that it can be effectively used in protein function prediction, having the power to increase both the coverage and the accuracy of predictions, when carefully integrated into prediction methods. Our approach constitutes a promising new direction towards understanding the higher levels of organization of the cell as a system which should help current efforts to re-engineer ontologies and improve our ability to predict which proteins are involved in specific biological processes.
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