Systems approach to refining genome annotation.
Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.
Reed
Jennifer L
JL
Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA.
Patel
Trina R
TR
Chen
Keri H
KH
Joyce
Andrew R
AR
Applebee
Margaret K
MK
Herring
Christopher D
CD
Bui
Olivia T
OT
Knight
Eric M
EM
Fong
Stephen S
SS
Palsson
Bernhard O
BO
D001692:Biological Transport; D002244:Carbon / Q000378:metabolism; D049109:Cell Proliferation; D019295:Computational Biology; D003198:Computer Simulation; D004926:Escherichia coli / Q000166:cytology / Q000235:genetics / Q000378:metabolism; D016680:Genome, Bacterial / Q000235:genetics*; D008293:Malates / Q000378:metabolism; D016366:Open Reading Frames / Q000235:genetics; D013400:Sugar Acids / Q000378:metabolism; D013936:Thymidine / Q000378:metabolism
D008293:Malates; D013400:Sugar Acids; C012991:galactonic acid; D002244:Carbon; C030298:malic acid; D013936:Thymidine