Data integration and analysis of biological networks.
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.
Kim
Tae Yong
TY
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.
Kim
Hyun Uk
HU
Lee
Sang Yup
SY
D000465:Algorithms; D003198:Computer Simulation; D003628:Database Management Systems; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome