Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea.
Curr Opin Biotechnol. 2010 Feb;21(1):78-84. doi: 10.1016/j.copbio.2010.01.003. Epub 2010 Feb 6.
During the past decade, bottom-up and top-down approaches of network reconstruction have greatly facilitated integration and analysis of biological networks, including transcriptional, protein interaction, and metabolic networks. As increasing amounts of multidimensional high-throughput data become available, biological networks have also been upgraded, allowing more accurate understanding of whole cellular characteristics. The network size is constantly expanding as larger volume of information and omics data are further integrated into the biological networks previously built upon a single type of data. Such effort more recently led to the modeling of human metabolic network and prediction of its tissue-specific metabolism, reconstruction of consensus yeast metabolic network, and simulation of mutual interactions among multiple microorganisms. It is expected that this trend will continue, the outcomes of which will allow development of more sophisticated networks integrating diverse omics data, and enhance our understanding of biological systems.
在过去的十年中，网络重建的自下而上和自上而下的方法极大地促进了生物网络的整合和分析，包括转录、蛋白质相互作用和代谢网络。随着越来越多的多维高通量数据的出现，生物网络也得到了升级，从而能够更准确地了解整个细胞的特征。随着更多的信息和组学数据进一步整合到以前基于单一类型数据构建的生物网络中，网络的规模不断扩大。最近，这一努力导致了人类代谢网络的建模和其组织特异性代谢的预测、共识酵母代谢网络的重建以及多个微生物之间相互作用的模拟。预计这种趋势将继续下去，其结果将允许开发更多复杂的网络，整合各种组学数据，并增强我们对生物系统的理解。
