Metabolic network analysis integrated with transcript verification for sequenced genomes

Iterative cycles of metabolic modeling and experimental open reading frame verification in Chlamydomonas reinhardtii lay the groundwork for more accurate gene annotation and provide resources for metabolic engineering. With sequencing of thousands of organisms completed or in progress, there is a gr...

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Published in	Nature methods Vol. 6; no. 8; pp. 589 - 592
Main Authors	Manichaikul, Ani, Ghamsari, Lila, Hom, Erik F Y, Lin, Chenwei, Murray, Ryan R, Chang, Roger L, Balaji, S, Hao, Tong, Shen, Yun, Chavali, Arvind K, Thiele, Ines, Yang, Xinping, Fan, Changyu, Mello, Elizabeth, Hill, David E, Vidal, Marc, Salehi-Ashtiani, Kourosh, Papin, Jason A
Format	Journal Article
Language	English
Published	New York Nature Publishing Group US 01.08.2009 Nature Publishing Group
Subjects	Animals Bioinformatics Biological Microscopy Biological Techniques Biomedical Engineering/Biotechnology brief-communication Chlamydomonas reinhardtii Chlamydomonas reinhardtii - enzymology Chlamydomonas reinhardtii - genetics Chlamydomonas reinhardtii - metabolism Computational Biology - methods Computer Simulation DNA sequencing Enzymes - genetics Enzymes - metabolism Genetic Engineering Genetic transcription Genome, Protozoan Genomics Life Sciences Metabolic engineering Metabolism Methods Models, Genetic Molecular biology Nucleotide sequencing Open reading frames Physiology Proteomics Protozoan Proteins - genetics Protozoan Proteins - metabolism Scientific method Transcription, Genetic United States
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ISSN	1548-7091 1548-7105 1548-7105
DOI	10.1038/nmeth.1348

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Abstract	Iterative cycles of metabolic modeling and experimental open reading frame verification in Chlamydomonas reinhardtii lay the groundwork for more accurate gene annotation and provide resources for metabolic engineering. With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.
AbstractList	With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering. With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering. [PUBLICATION ABSTRACT] With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering. With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering. Iterative cycles of metabolic modeling and experimental open reading frame verification in Chlamydomonas reinhardtii lay the groundwork for more accurate gene annotation and provide resources for metabolic engineering. With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.
Audience	Academic
Author	Salehi-Ashtiani, Kourosh Papin, Jason A Mello, Elizabeth Vidal, Marc Murray, Ryan R Chavali, Arvind K Hom, Erik F Y Manichaikul, Ani Chang, Roger L Balaji, S Shen, Yun Yang, Xinping Hill, David E Ghamsari, Lila Thiele, Ines Lin, Chenwei Hao, Tong Fan, Changyu
AuthorAffiliation	2 Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA 3 Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA 1 Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA 4 Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 These authors contributed equally to this work. Present address: Center for Systems Biology, University of Iceland, Reykjavik, Iceland.
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Snippet	Iterative cycles of metabolic modeling and experimental open reading frame verification in Chlamydomonas reinhardtii lay the groundwork for more accurate gene... With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using...
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SubjectTerms	Animals Bioinformatics Biological Microscopy Biological Techniques Biomedical Engineering/Biotechnology brief-communication Chlamydomonas reinhardtii Chlamydomonas reinhardtii - enzymology Chlamydomonas reinhardtii - genetics Chlamydomonas reinhardtii - metabolism Computational Biology - methods Computer Simulation DNA sequencing Enzymes - genetics Enzymes - metabolism Genetic Engineering Genetic transcription Genome, Protozoan Genomics Life Sciences Metabolic engineering Metabolism Methods Models, Genetic Molecular biology Nucleotide sequencing Open reading frames Physiology Proteomics Protozoan Proteins - genetics Protozoan Proteins - metabolism Scientific method Transcription, Genetic
Title	Metabolic network analysis integrated with transcript verification for sequenced genomes
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