Genome-scale Metabolic Network Reconstruction of Chlamydomonas for Light-driven Algal Metabolism Prediction

In recent years, algae have been one of the emerging feedstocks for biofuels production, due to the presence of large amounts of potentially useful compounds (embedded within the algae) which can be processed into biofuels. However, little is known about the metabolic processes which drive the production of these useful compounds in algae. A deeper knowledge of the algal metabolism could help in increasing the efficiency of biofuel production from algal harvests.

A team of researchers from the University of California (USA),Dana-Farber Cancer Institute (USA), Harvard Medical School (USA), University of Virginia (USA), Harvard University (USA), University of Iceland (Iceland), New York University (UAE) and New York University (USA) recently attempted to reconstruct the metabolic network of an algae for the prediction of its growth rate at a given light source. In their paper, they first presented a genome-scale reconstruction of the central metabolism of a model algae, Chlamydomonas reinhardtii'. This was done in a "bottom-up manner", according to current standards on a pathway-by-pathway basis, drawing biochemical, genomic, and physiological evidence from over 250 publications. Then, they functionally annotated the gene models from the genome-scale reconstruction of the algae. Subsequently, the researchers performed growth simulations using Flux Balance Analysis (i.e. a standard simulation method in the systems biology field with a long history of success) and Flux Variability Analysis. Verification experiments were done by cultivating the model algae at different average photon fluxes and sequencing the transcripts for proof. Finally, spectral bandwidths that effectively drive each photon-utilizing reaction in the reconstructed metabolism network were determined from published experimental activity spectral data, verified and integrated to the prediction to account for different light intensity conditions.

The network can offer insights into algal metabolism, which could be useful for improved algal-biofuel production, in terms of better algal strains, and more efficient light source design. The study is published in the journal, Molecular Systems Biology (URL above).