Abstract
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-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, 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.
Highlights
Photosynthetic microorganisms have been recognized as one of the oldest life forms on Earth [1]
In this review we compare various computational methods used for the rational design of strains and culture media, including flux balance analysis (FBA), dynamic flux balance analysis, 13C metabolic flux analysis (13C MFA), and elementary modes (EM) analysis
We focus in particular on the latest insights into central carbon metabolism of oleaginous microalgae obtained by computational modeling as it is most relevant for production of biofuels and fuel precursors
Summary
Photosynthetic microorganisms have been recognized as one of the oldest life forms on Earth [1]. We focus in particular on the latest insights into central carbon metabolism (tricarboxylic acid cycle, the Calvin cycle, the glyoxylate shunt, glycolysis/gluconeogenesis, and the pentose-phosphate pathway) of oleaginous microalgae obtained by computational modeling as it is most relevant for production of biofuels and fuel precursors. We discuss the impact of time course modeling as well as the importance of incorporating compartmentalization into genome-scale models for microalgae and highlight the complexity of modeling lipid metabolism to increase biofuel productivity.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
