Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Nat Rev Genet. 2020 Jun;21(6):339-354. doi: 10.1038/s41576-020-0212-5. Epub 2020 Feb 14.
Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.
在 21 世纪,社会、政治和生态的持续变化使得比以往任何时候都有更多的人面临危及生命的急性和慢性感染的风险。开发新的诊断、预防、治疗和治愈策略对于应对这一负担至关重要,但前提是要详细了解病原体与其宿主之间极其复杂的关系。传统的、还原论的方法来研究这种动态往往缺乏规模和/或范围来忠实地模拟这种双重的、相互依赖的关系,从而限制了转化努力的成功。随着大规模定量组学方法以及综合分析策略的最新进展,系统生物学方法在传染病研究方面正在迅速形成一种新的范式,用于理解和模拟传染病的宿主-病原体关系,以实现转化应用。在这里,我们将系统生物学方法应用于传染病研究分为三个部分:发现——设计、收集和分析组学数据;表示——对复杂数据集的迭代建模、整合和可视化;以及应用——基于解释和假设的转化结果的探究。