Center for Translational Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Clin Transl Sci. 2012 Jun;5(3):285-8. doi: 10.1111/j.1752-8062.2011.00388.x.
Investigation into biological complexity, whether for a better understanding of disease or drug process, is a monumental task plaguing investigators. The lure of "omic" technologies for circumventing much of these challenges has led to widespread efforts and adoption. It is becoming clearer that a single "omic" approach (e.g., genomics) is often insufficient for completely defining the complexity in these biological systems. Hence, there is an increasing awareness that a "systems" approach will serve to increase resolution and confidence and provide a strong foundation for further hypothesis-driven investigation. Although certain metabolites are already considered clinically important, the profiling of metabolites via metabolomics (the profiling of metabolites to fully characterize metabolic pathways) is the most recent to mature of these "omic" technologies and has been only recently adopted as compared to genomic or proteomic approaches in systems inquiries. Recent reports suggest that this "omic" may well be a key data stream in systems investigations for endeavors in personalized medicine and biomarker identification, as it seems most closely relevant to the phenotype.
对生物复杂性的研究，无论是为了更好地了解疾病还是药物作用过程，都是一项艰巨的任务，困扰着研究人员。为了规避这些挑战，“组学”技术的吸引力导致了广泛的努力和采用。越来越明显的是，单一的“组学”方法（例如基因组学）通常不足以完全定义这些生物系统的复杂性。因此，人们越来越意识到，系统方法将有助于提高分辨率和置信度，并为进一步的假设驱动研究提供坚实的基础。虽然某些代谢物已经被认为具有临床重要性，但通过代谢组学（对代谢物进行分析以全面描述代谢途径）对代谢物进行分析是这些“组学”技术中最新成熟的技术，与基因组学或蛋白质组学方法相比，它在系统研究中最近才被采用。最近的报告表明，对于个性化医疗和生物标志物识别等领域的系统研究，这种“组学”很可能是一个关键的数据来源，因为它似乎与表型最为密切相关。
