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用于识别沙门氏菌致病型之间分子和系统水平差异的网络生物学方法

Network Biology Approaches to Identify Molecular and Systems-Level Differences Between Salmonella Pathovars.

作者信息

Olbei Marton, Kingsley Robert A, Korcsmaros Tamas, Sudhakar Padhmanand

机构信息

Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.

Earlham Institute, Norwich Research Park, Norwich, UK.

出版信息

Methods Mol Biol. 2019;1918:265-273. doi: 10.1007/978-1-4939-9000-9_21.

Abstract

The field of systems biology endeavors to map, study, and simulate cellular systems and their underlying mechanisms. The internal mechanisms of biological systems can be represented with networks comprising nodes and edges. Nodes denote the constituents of the biological system whereas edges indicate the relationships among them. Likewise, every layer of cellular organization can be represented by networks. Multilayered networks capture interactions between various network types, such as transcriptional regulatory networks, protein-protein interaction networks, and metabolic networks from the same biological system. This property makes multilayered networks representative of the system while its internal mechanisms are investigated. However, there are not many multilayered networks containing integrated data for nonmodel organisms including the bacterial pathogens Salmonella. Here, we outline the steps to create such an integrated network database, through the example of SalmoNet, the first integrated multilayered data resource for multiple strains belonging to distinct Salmonella serovars.

摘要

系统生物学领域致力于绘制、研究和模拟细胞系统及其潜在机制。生物系统的内部机制可以用由节点和边组成的网络来表示。节点表示生物系统的组成部分,而边表示它们之间的关系。同样,细胞组织的每一层都可以用网络来表示。多层网络捕捉来自同一生物系统的各种网络类型之间的相互作用,如转录调控网络、蛋白质-蛋白质相互作用网络和代谢网络。在研究生物系统内部机制时,这一特性使多层网络能够代表该系统。然而,包含非模式生物(包括细菌病原体沙门氏菌)综合数据的多层网络并不多。在这里,我们以SalmoNet为例,概述创建这样一个综合网络数据库的步骤,SalmoNet是第一个针对属于不同沙门氏菌血清型的多个菌株的综合多层数据资源。

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