Computational Approaches for Microalgal Biofuel Optimization
Researchers from New York University Abu Dhabi presented a review on major computational tools and approaches developed, and used in the optimization of biofuel producing algae strains. Various approaches are employed in reconstruction and alteration of metabolic network models, integrating different types of biological data sets to potentially identify target genes, pathways and reactions of particular interest to biofuel production in algae.
Metabolic models help contextualizing high throughput experimental data, for example, integrating gene expression data with metabolic pathways under different growth conditions. With the availability of large and diverse biological data sets, metabolic network models can provide a framework to integrate omics data and testing of downstream hypotheses. It allows for cross- species metabolic comparison which benefits in the identification of differentially activated metabolically pathways.
Some of the tools used are
- Metabolic network model reconstruction
- Pathway visualization
- Model refinement and gap filling
- Constraint-based modeling, FBA and integration of expression data
- Omics data integration tools
