文章基本信息

标题：Mining large-scale response networks reveals ‘topmost activities’ in Mycobacterium tuberculosis infection
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作者：Awanti Sambarey ; Karyala Prashanthi ; Nagasuma Chandra 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2013
卷号：3
期号：1
DOI：10.1038/srep02302
语种：English
出版社：Springer Nature
摘要：Mycobacterium tuberculosis owes its high pathogenic potential to its ability to evade host immune responses and thrive inside the macrophage. The outcome of infection is largely determined by the cellular response comprising a multitude of molecular events. The complexity and inter-relatedness in the processes makes it essential to adopt systems approaches to study them. In this work, we construct a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions. We then compute response networks based on available gene expression profiles corresponding to states of health, disease and drug treatment. We use a novel formulation for mining response networks that has led to identifying highest activities in the cell. Highest activity paths provide mechanistic insights into pathogenesis and response to treatment. The approach used here serves as a generic framework for mining dynamic changes in genome-scale protein interaction networks.