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Title: Advances in metabolic modeling of oleaginous microalgae

Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specifc information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. As a result, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.
Authors:
 [1] ;  [2] ;  [1] ;  [2]
  1. Univ. Nacional de Colombia (Colombia)
  2. Univ. of California, San Diego, La Jolla, CA (United States)
Publication Date:
Grant/Contract Number:
SC0012658
Type:
Accepted Manuscript
Journal Name:
Biotechnology for Biofuels
Additional Journal Information:
Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 1754-6834
Publisher:
BioMed Central
Research Org:
Johns Hopkins Univ., Baltimore, MD (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Oleaginous phototrophs; Lipid production; Constraint‑based metabolic modeling; Central carbon metabolism
OSTI Identifier:
1485595

Tibocha-Bonilla, Juan D., Zuñiga, Cristal, Godoy-Silva, Rubén D., and Zengler, Karsten. Advances in metabolic modeling of oleaginous microalgae. United States: N. p., Web. doi:10.1186/s13068-018-1244-3.
Tibocha-Bonilla, Juan D., Zuñiga, Cristal, Godoy-Silva, Rubén D., & Zengler, Karsten. Advances in metabolic modeling of oleaginous microalgae. United States. doi:10.1186/s13068-018-1244-3.
Tibocha-Bonilla, Juan D., Zuñiga, Cristal, Godoy-Silva, Rubén D., and Zengler, Karsten. 2018. "Advances in metabolic modeling of oleaginous microalgae". United States. doi:10.1186/s13068-018-1244-3. https://www.osti.gov/servlets/purl/1485595.
@article{osti_1485595,
title = {Advances in metabolic modeling of oleaginous microalgae},
author = {Tibocha-Bonilla, Juan D. and Zuñiga, Cristal and Godoy-Silva, Rubén D. and Zengler, Karsten},
abstractNote = {Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specifc information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. As a result, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.},
doi = {10.1186/s13068-018-1244-3},
journal = {Biotechnology for Biofuels},
number = 1,
volume = 11,
place = {United States},
year = {2018},
month = {9}
}

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