TY - JOUR
T1 - Genome-scale microbial in silico models
T2 - The constraints-based approach
AU - Price, Nathan D.
AU - Papin, Jason A.
AU - Schilling, Christophe H.
AU - Palsson, Bernhard O.
N1 - Funding Information:
We acknowledge the support of the National Science Foundation (BES 01–20363), the National Institutes of Health (GM 57089), and the Whitaker Foundation (Graduate Research Fellowship to JP).
PY - 2003/4/1
Y1 - 2003/4/1
N2 - Genome sequencing and annotation has enabled the reconstruction of genome-scale metabolic networks. The phenotypic functions that these networks allow for can be defined and studied using constraints-based models and in silico simulation. Several useful predictions have been obtained from such in silico models, including substrate preference, consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and shifts in expression profiles. The success rate of these predictions is typically in the order of 70-90% depending on the organism studied and the type of prediction being made. These results are useful as a basis for iterative model building and for several practical applications.
AB - Genome sequencing and annotation has enabled the reconstruction of genome-scale metabolic networks. The phenotypic functions that these networks allow for can be defined and studied using constraints-based models and in silico simulation. Several useful predictions have been obtained from such in silico models, including substrate preference, consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and shifts in expression profiles. The success rate of these predictions is typically in the order of 70-90% depending on the organism studied and the type of prediction being made. These results are useful as a basis for iterative model building and for several practical applications.
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U2 - 10.1016/S0167-7799(03)00030-1
DO - 10.1016/S0167-7799(03)00030-1
M3 - Review article
C2 - 12679064
AN - SCOPUS:0037385718
SN - 0167-7799
VL - 21
SP - 162
EP - 169
JO - Trends in Biotechnology
JF - Trends in Biotechnology
IS - 4
ER -