DocumentCode :
2377630
Title :
Predicting metabolic pathways from metabolic networks with limited biological knowledge
Author :
Leung, S.Y. ; Leung, Henry C M ; Xiang, Carlos L. ; Yiu, S.M. ; Chin, Francis Y L
Author_Institution :
Dept. of Comput. Sci., Univ. of Hong Kong, Hong Kong, China
fYear :
2010
fDate :
18-18 Dec. 2010
Firstpage :
7
Lastpage :
12
Abstract :
Understanding the metabolism of new species (e.g. endophytic fungi that produce fuel) have tremendous impact on human lives. Based on predicted proteins and existing reaction databases, one can construct the metabolic network for the species. Next is to identify critical metabolic pathways from the network. Existing computational techniques identify conserved pathways based on multiple networks of related species, but have the following drawbacks. Some do not rely on additional information, so only locate short (of length at most 5), but not necessarily interesting, conserved paths. The others require extensive information (the complete pathway on one species). In reality, researchers usually know only partial information of a metabolic pathway and may not have a conserved pathway in a related species. The Conserved Metabolic Pathway (CMP) problem is to find conserved pathways from the networks with partial information on the initial substrates and final products of the target pathways. Experimental results show that our algorithm CMPFinder can predict useful metabolic pathways with acceptable accuracy.
Keywords :
biochemistry; bioinformatics; cellular biophysics; molecular biophysics; proteins; CMPFinder; biological knowledge; computational technique; conserved metabolic pathway problem; human lives; metabolic networks; partial information; predicting metabolic pathways; proteins; reaction database; Building Block; Conserved Metabolic Pathways; Metabolic Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on
Conference_Location :
Hong, Kong
Print_ISBN :
978-1-4244-8303-7
Electronic_ISBN :
978-1-4244-8304-4
Type :
conf
DOI :
10.1109/BIBMW.2010.5703765
Filename :
5703765
Link To Document :
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