Predicting the metabolic capabilities of Synechococcus elongatus PCC 7942 adapted to different light regimes.
Metab Eng
; 52: 42-56, 2019 03.
Article
de En
| MEDLINE
| ID: mdl-30439494
ABSTRACT
There is great interest in engineering photoautotrophic metabolism to generate bioproducts of societal importance. Despite the success in employing genome-scale modeling coupled with flux balance analysis to engineer heterotrophic metabolism, the lack of proper constraints necessary to generate biologically realistic predictions has hindered broad application of this methodology to phototrophic metabolism. Here we describe a methodology for constraining genome-scale models of photoautotrophy in the cyanobacteria Synechococcus elongatus PCC 7942. Experimental photophysiology parameters coupled to genome-scale flux balance analysis resulted in accurate predictions of growth rates and metabolic reaction fluxes at low and high light conditions. Additionally, by constraining photon uptake fluxes, we characterized the metabolic cost of excess excitation energy. The predicted energy fluxes were consistent with known light-adapted phenotypes in cyanobacteria. Finally, we leveraged the modeling framework to characterize existing photoautotrophic and photomixtotrophic engineering strategies for 2,3-butanediol production in S. elongatus. This methodology, applicable to genome-scale modeling of all phototrophic microorganisms, can facilitate the use of flux balance analysis in the engineering of light-driven metabolism.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Synechococcus
/
Lumière
Type d'étude:
Prognostic_studies
/
Risk_factors_studies
Langue:
En
Journal:
Metab Eng
Sujet du journal:
ENGENHARIA BIOMEDICA
/
METABOLISMO
Année:
2019
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique