Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries

Pan, Shu; Reed, Jennifer L.

doi:10.1016/j.copbio.2017.12.012

Title: Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries

Full Record
Other Related Research

Authors:: Pan, Shu; Reed, Jennifer L.

Publication Date:: 2018-06-01

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:: 1548876

Grant/Contract Number:: FC02-07ER64494

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Current Opinion in Biotechnology

Additional Journal Information:: Journal Name: Current Opinion in Biotechnology Journal Volume: 51 Journal Issue: C; Journal ID: ISSN 0958-1669

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Pan, Shu, and Reed, Jennifer L. Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries.  United Kingdom: N. p., 2018. 
Web.  doi:10.1016/j.copbio.2017.12.012.

Copy to clipboard


                    Pan, Shu, & Reed, Jennifer L. Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries.  United Kingdom.  https://doi.org/10.1016/j.copbio.2017.12.012

Copy to clipboard


                    Pan, Shu, and Reed, Jennifer L. Fri .  
"Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries".  United Kingdom.  https://doi.org/10.1016/j.copbio.2017.12.012.

Copy to clipboard


                    
@article{osti_1548876,

  title        = {Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries},

  author       = {Pan, Shu and Reed, Jennifer L.},

  abstractNote = {},

  doi          = {10.1016/j.copbio.2017.12.012},

  journal      = {Current Opinion in Biotechnology},

  number       = C,

  volume       = 51,

  place        = {United Kingdom},

  year         = {2018},

  month        = {6}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Publisher's Version of Record
https://doi.org/10.1016/j.copbio.2017.12.012

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 30 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries

Journal Article Pan, Shu ; Reed, Jennifer L. - Current Opinion in Biotechnology

Not provided.
https://doi.org/10.1016/j.copbio.2017.12.012
Model-driven analysis of mutant fitness experiments improves genome-scale metabolic models of Zymomonas mobilis ZM4

Journal Article Ong, Wai Kit ; Courtney, Dylan K. ; Pan, Shu ; ... - PLoS Computational Biology (Online)

Genome-scale metabolic models have been utilized extensively in the study and engineering of the organisms they describe. Here we present the analysis of a published dataset from pooled transposon mutant fitness experiments as an approach for improving the accuracy and gene-reaction associations of a metabolic model for Zymomonas mobilis ZM4, an industrially relevant ethanologenic organism with extremely high glycolytic flux and low biomass yield. Gene essentiality predictions made by the draft model were compared to data from individual pooled mutant experiments to identify areas of the model requiring deeper validation. Subsequent experiments showed that some of the discrepancies between themore »« less
Cited by 6
https://doi.org/10.1371/journal.pcbi.1008137
Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

Journal Article Benedict, Matthew N. ; Mundy, Michael B. ; Henry, Christopher S. ; ... - PLoS Computational Biology (Online)

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore »« less
Cited by 46
https://doi.org/10.1371/journal.pcbi.1003882

Full Text Available
Genome –Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A

Journal Article Vinay-Lara, Elena ; Hamilton, Joshua J. ; Stahl, Buffy ; ... - PLoS ONE

Not provided.
https://doi.org/10.1371/journal.pone.0110785

Full Text Available
Integrated Genome-Scale Analysis Identifies Novel Genes and Networks Underlying Senescence in Maize

Journal Article Sekhon, Rajandeep S. ; Saski, Christopher ; Kumar, Rohit ; ... - Plant Cell

Premature senescence in annual crops reduces yield, while delayed senescence, termed stay-green, imposes positive and negative impacts on yield and nutrition quality. Despite its importance, scant information is available on the genetic architecture of senescence in maize (Zea mays) and other cereals. We combined a systematic characterization of natural diversity for senescence in maize and coexpression networks derived from transcriptome analysis of normally senescing and stay-green lines. Sixty-four candidate genes were identified by genome-wide association study (GWAS), and 14 of these genes are supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport and signaling, and sinkmore »« less
Cited by 43
https://doi.org/10.1105/tpc.18.00930

Similar Records