Smita Suchi, Lange Falko, Wolkenhauer Olaf, Köhling Rüdiger
Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany; Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
Biochim Biophys Acta. 2016 Nov;1860(11 Pt B):2706-15. doi: 10.1016/j.bbagen.2016.07.017. Epub 2016 Jul 25.
Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death.
The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors.
Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging.
In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
衰老被广泛认为是一个动态过程，它以时间依赖的方式积累不利的结构和功能变化，导致生物体生理完整性的逐渐丧失，最终引发与年龄相关的疾病并导致死亡。
大多数与衰老相关的研究都基于还原论方法，聚焦于单个基因/蛋白质或单个途径，而不考虑它们之间可能的相互作用。在过去几十年中，几个这样的基因/蛋白质被独立分析，并被认为与影响生物体寿命的作用有关。然而，由于多种因素的参与和整合，对基因和蛋白质的孤立分析在很大程度上无法解释复杂表型的机制。
技术进步使得生成高通量的时空数据成为可能，这些数据为使用基于计算机的方法提供了机会。这些技术使我们能够超越还原论方法，分析大规模网络，从而更深入地理解驱动衰老的过程。
在本综述中，我们专注于基于网络推理方法的系统生物学方法，以理解导致衰老表型的标志性过程的动态变化。我们还描述了用于解释和识别参与衰老相关过程机制联系的重要分子枢纽的计算方法。本文是名为“系统遗传学”的特刊的一部分，客座编辑：蔡宇东博士和黄涛博士。
