Hancock Robert E W
Nestle Nutr Inst Workshop Ser. 2016;84:35-46. doi: 10.1159/000436950. Epub 2016 Jan 14.
While scientific methods have dominated research approaches in biology over the past decades, it is increasingly recognized that the complexity of biological systems must be addressed by a different approach, namely unbiased research involving the collection of large amounts of genome-wide information. To enable analysis of this information we and others are developing a variety of computational tools that allow bioinformaticists and wet laboratory biologists to extract novel patterns of data from these results and generate novel biological insights while generating new hypotheses for testing in the laboratory. There are two types of critical tools, databases to collate all information on biomolecules, especially interactions, and tools that reorganize information in a supervised (e.g. pathway analysis or gene ontology) or unsupervised (nonhierarchical clustering and network analysis) manner. Here we describe some of the tools we have developed and how we have used these to gain new ideas in the general area of infection and innate immunity/inflammation. In particular, it is illustrated how such analyses enable novel hypotheses about mechanisms associated with diseases and the mechanisms of action of immunomodulatory and other interventions, the definition of mechanism-based biomarkers/diagnostics, and prospective new interventions based on drug repurposing.
在过去几十年里,科学方法主导了生物学的研究途径,但人们越来越认识到,生物系统的复杂性必须通过一种不同的方法来解决,即进行无偏见研究,包括收集大量全基因组信息。为了能够分析这些信息,我们和其他研究人员正在开发各种计算工具,使生物信息学家和实验生物学研究人员能够从这些结果中提取新的数据模式,产生新的生物学见解,同时生成新的假设以便在实验室中进行检验。有两种关键工具,一种是用于整理关于生物分子的所有信息(特别是相互作用信息)的数据库,另一种是以有监督方式(例如通路分析或基因本体论)或无监督方式(非层次聚类和网络分析)重新组织信息的工具。在这里,我们描述了我们开发的一些工具,以及我们如何利用这些工具在感染和固有免疫/炎症这一总体领域获得新的思路。特别是,阐述了此类分析如何能够产生关于疾病相关机制以及免疫调节和其他干预措施作用机制的新假设,基于机制的生物标志物/诊断方法的定义,以及基于药物重新利用的前瞻性新干预措施。