Department of Bioengineering, University of California, San Diego, La Jolla, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA.
Department of Bioengineering, University of California, San Diego, La Jolla, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
Curr Opin Biotechnol. 2018 Jun;51:130-136. doi: 10.1016/j.copbio.2018.01.009. Epub 2018 Feb 3.
The large-scale generation of '-omic' data holds the potential to increase and deepen our understanding of biological phenomena, but the ability to synthesize information and extract knowledge from these data sets still represents a significant challenge. Bottom-up systems biology overcomes this hurdle through the integration of disparate -omic data types, and absolutely quantified experimental measurements allow for direct integration into quantitative, mechanistic models. The human red blood cell has served as a starting point for the application of systems biology approaches and has been the focus of a recent burst of generated quantitative metabolomics and proteomics data. Thus, the red blood cell represents the perfect case study through which to examine our ability to glean knowledge from the integration of multiple disparate data types.
大规模的“组学”数据的产生有可能增加和深化我们对生物现象的理解，但从这些数据集综合信息和提取知识的能力仍然是一个重大挑战。通过整合不同的“组学”数据类型，自下而上的系统生物学克服了这一障碍，而绝对定量的实验测量则允许直接整合到定量的、机械的模型中。人类红细胞已经成为系统生物学方法应用的起点，并且是最近大量产生的定量代谢组学和蛋白质组学数据的焦点。因此，红细胞是一个完美的案例研究，可以通过它来检验我们从多种不同数据类型的整合中获取知识的能力。
