Nutritional Sciences Division, University of Surrey, Guildford, Surrey, UK.
Adv Nutr. 2011 Jul;2(4):355-64. doi: 10.3945/an.111.000554. Epub 2011 Jun 28.
In the last decade, advances in genomics, proteomics, and metabolomics have yielded large-scale datasets that have driven an interest in global analyses, with the objective of understanding biological systems as a whole. Systems biology integrates computational modeling and experimental biology to predict and characterize the dynamic properties of biological systems, which are viewed as complex signaling networks. Whereas the systems analysis of disease-perturbed networks holds promise for identification of drug targets for therapy, equally the identified critical network nodes may be targeted through nutritional intervention in either a preventative or therapeutic fashion. As such, in the context of the nutritional sciences, it is envisioned that systems analysis of normal and nutrient-perturbed signaling networks in combination with knowledge of underlying genetic polymorphisms will lead to a future in which the health of individuals will be improved through predictive and preventative nutrition. Although high-throughput transcriptomic microarray data were initially most readily available and amenable to systems analysis, recent technological and methodological advances in MS have contributed to a linear increase in proteomic investigations. It is now commonplace for combined proteomic technologies to generate complex, multi-faceted datasets, and these will be the keystone of future systems biology research. This review will define systems biology, outline current proteomic methodologies, highlight successful applications of proteomics in nutrition research, and discuss the challenges for future applications of systems biology approaches in the nutritional sciences.
在过去的十年中，基因组学、蛋白质组学和代谢组学的进展产生了大规模数据集，这激发了人们对全局分析的兴趣，旨在全面了解生物系统。系统生物学将计算建模和实验生物学相结合，以预测和描述生物系统的动态特性，这些系统被视为复杂的信号网络。虽然对疾病干扰网络的系统分析有望为治疗药物靶点的确定提供帮助，但同样可以通过营养干预以预防或治疗的方式针对已识别的关键网络节点。因此，在营养科学领域，可以预见的是，对正常和营养干扰信号网络的系统分析与对潜在遗传多态性的了解相结合，将引领未来通过预测和预防营养来改善个体健康。尽管高通量转录组微阵列数据最初最容易获得且适合系统分析，但 MS 技术的最新技术和方法进展促进了蛋白质组学研究的线性增加。现在，结合蛋白质组学技术生成复杂、多方面数据集已经很常见，这些将成为未来系统生物学研究的基石。本文将定义系统生物学，概述当前的蛋白质组学方法，强调蛋白质组学在营养研究中的成功应用，并讨论系统生物学方法在营养科学中未来应用的挑战。
