TY - JOUR
T1 - A network-based method for target selection in metabolic networks
AU - Guimerà, R.
AU - Sales-Pardo, M.
AU - Amaral, L. A.N.
N1 - Funding Information:
We thank V. Hatzimanikatis, W.M. Miller, A. Mongragón, D.B. Stouffer and L.K. Wing for useful comments and suggestions. We thank J. Reed, I. Thiele and the Systems Biology Research Group at UCSD for their help and for making their FBA reconstructions publicly available. R.G. and M.S.-P. gratefully acknowledge support from the Fulbright Program. L.A.N.A. gratefully acknowledges support from a NIH/NIGMS K-25 award, from the J.S. McDonnell Foundation, and from the W.M. Keck Foundation.
PY - 2007/7/1
Y1 - 2007/7/1
N2 - Motivation: The lack of new antimicrobials, combined with increasing microbial resistance to old ones, poses a serious threat to public health. With hundreds of genomes sequenced, systems biology promises to help in solving this problem by uncovering new drug targets. Results: Here, we propose an approach that is based on the mapping of the interactions between biochemical agents, such as proteins and metabolites, onto complex networks. We report that nodes and links in complex biochemical networks can be grouped into a small number of classes, based on their role in connecting different functional modules. Specifically, for metabolic networks, in which nodes represent metabolites and links represent enzymes, we demonstrate that some enzyme classes are more likely to be essential, some are more likely to be species-specific and some are likely to be both essential and specific. Our network-based enzyme classification scheme is thus a promising tool for the identification of drug targets.
AB - Motivation: The lack of new antimicrobials, combined with increasing microbial resistance to old ones, poses a serious threat to public health. With hundreds of genomes sequenced, systems biology promises to help in solving this problem by uncovering new drug targets. Results: Here, we propose an approach that is based on the mapping of the interactions between biochemical agents, such as proteins and metabolites, onto complex networks. We report that nodes and links in complex biochemical networks can be grouped into a small number of classes, based on their role in connecting different functional modules. Specifically, for metabolic networks, in which nodes represent metabolites and links represent enzymes, we demonstrate that some enzyme classes are more likely to be essential, some are more likely to be species-specific and some are likely to be both essential and specific. Our network-based enzyme classification scheme is thus a promising tool for the identification of drug targets.
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U2 - 10.1093/bioinformatics/btm150
DO - 10.1093/bioinformatics/btm150
M3 - Article
C2 - 17463022
AN - SCOPUS:34547830837
SN - 1367-4803
VL - 23
SP - 1616
EP - 1622
JO - Bioinformatics
JF - Bioinformatics
IS - 13
ER -
