Computational Approaches for Microalgal Biofuel Optimization: A ReviewReport as inadecuate

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BioMed Research International - Volume 2014 2014, Article ID 649453, 12 pages -

Review ArticleDivision of Science and Math and Center for Genomics and Systems Biology CGSB, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi, UAE

Received 6 June 2014; Revised 28 August 2014; Accepted 1 September 2014; Published 21 September 2014

Academic Editor: Meisam Tabatabaei

Copyright © 2014 Joseph Koussa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

Author: Joseph Koussa, Amphun Chaiboonchoe, and Kourosh Salehi-Ashtiani



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