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Title: Identifying the metabolic differences of a fast-growth phenotype in Synechococcus UTEX 2973

The photosynthetic capabilities of cyanobacteria make them interesting candidates for industrial bioproduction. One obstacle to large-scale implementation of cyanobacteria is their limited growth rates as compared to industrial mainstays. Synechococcus UTEX 2973, a strain closely related to Synechococcus PCC 7942, was recently identified as having the fastest measured growth rate among cyanobacteria. To facilitate the development of 2973 as a model organism we developed in this study the genome-scale metabolic model iSyu683. Experimental data were used to define CO 2 uptake rates as well as the biomass compositions for each strain. The inclusion of constraints based on experimental measurements of CO 2 uptake resulted in a ratio of the growth rates of Synechococcus 2973 to Synechococcus 7942 of 2.03, which nearly recapitulates the in vivo growth rate ratio of 2.13. This identified the difference in carbon uptake rate as the main factor contributing to the divergent growth rates. Additionally four SNPs were identified as possible contributors to modified kinetic parameters of metabolic enzymes and candidates for further study. As a result, comparisons against more established cyanobacterial strains identified a number of differences between the strains along with a correlation between the number of cytochrome c oxidase operons and heterotrophic ormore » diazotrophic capabilities.« less
Authors:
 [1] ;  [2] ;  [2] ;  [1]
  1. Pennsylvania State Univ., University Park, PA (United States)
  2. Washington Univ., St. Louis, MO (United States)
Publication Date:
Grant/Contract Number:
SC0012722
Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Research Org:
Washington Univ., St. Louis, MO (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; bacteria; biochemical reaction networks; computational methods
OSTI Identifier:
1347426