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 an...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2006-11, Vol.103 (46), p.17480-17484
Main Authors: Reed, Jennifer L., Patel, Trina R., Chen, Keri H., Joyce, Andrew R., Applebee, Margaret K., Herring, Christopher D., Bui, Olivia T., Knight, Eric M., Fong, Stephen S., Palsson, Bernhard O.
Format: Article
Language:English
Subjects:
Algorithms
Biological Sciences
Biological Transport
Carbon
Carbon - metabolism
Cell Proliferation
Complementary DNA
Computational Biology
Computer Simulation
E coli
Escherichia coli
Escherichia coli - cytology
Escherichia coli - genetics
Escherichia coli - metabolism
Genetic screening
Genome, Bacterial - genetics
Genomes
Genomics
Malates - metabolism
Metabolism
Modeling
Open Reading Frames - genetics
Phenotypes
Propionates
Reverse transcriptase polymerase chain reaction
Studies
Sugar Acids - metabolism
Thymidine - metabolism
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0603364103