2024-03-29T16:40:12Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:000952052024-03-29T05:26:34Z01164:05352:07133:07264
Complementary elementary mode analysis for large-scale metabolic networksComplementary elementary mode analysis for large-scale metabolic networksenghttp://id.nii.ac.jp/1001/00095186/Technical Reporthttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=95205&item_no=1&attribute_id=1&file_no=1Copyright (c) 2013 by the Information Processing Society of JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of TechnologyDepartment of Bioscience and Bioinformatics, Kyushu Institute of TechnologyDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology/Biomedical Informatics R&D Center, Kyushu Institute of TechnologyMd.BahadurBadshaRyo, TsuboiHiroyuki, KurataMetabolic pathway analysis facilitates understanding a complex metabolic system and enables prediction of steady-state metabolic flux distributions through elementary mode (EM) analysis. The principal drawback of the ordinary EM analysis is that the number of EMs suffers from a combinatorial explosion, and the use of complete sets of EMs gives rise to problems with scalability when applied to large-scale network models. The current problem is that many organisms still do not provide any specific objective biological function to predict the unknown metabolic fluxes. Since EMs can be described by many scalar products of each EM, the predicted fluxes should be consistent with respect to all of them. To overcome the existing problem, we proposed a fast and efficient EM algorithm named the complementary EMs (cEM). This study opens a new framework for a large-scale metabolic network, which neither requires the initial generation of a full set of EMs nor any objective biological function.Metabolic pathway analysis facilitates understanding a complex metabolic system and enables prediction of steady-state metabolic flux distributions through elementary mode (EM) analysis. The principal drawback of the ordinary EM analysis is that the number of EMs suffers from a combinatorial explosion, and the use of complete sets of EMs gives rise to problems with scalability when applied to large-scale network models. The current problem is that many organisms still do not provide any specific objective biological function to predict the unknown metabolic fluxes. Since EMs can be described by many scalar products of each EM, the predicted fluxes should be consistent with respect to all of them. To overcome the existing problem, we proposed a fast and efficient EM algorithm named the complementary EMs (cEM). This study opens a new framework for a large-scale metabolic network, which neither requires the initial generation of a full set of EMs nor any objective biological function.AA12055912研究報告バイオ情報学(BIO)2013-BIO-355122013-09-122013-09-05