Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism.

TitleMetabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism.
Publication TypeJournal Article
Year of Publication2011
AuthorsChang RL, Ghamsari L, Manichaikul A, Hom EFY, Balaji S, Fu W, Shen Y, Hao T, Palsson BØ, Salehi-Ashtiani K, Papin JA
JournalMol Syst Biol
Volume7
Pagination518
Date Published2011
ISSN1744-4292
KeywordsChlamydomonas reinhardtii, Computer Simulation, Databases, Genetic, Genetic Engineering, Lipid Metabolism, Metabolic Networks and Pathways, Models, Biological, Phenotype, Photobioreactors, Photosynthesis, Reverse Transcriptase Polymerase Chain Reaction, Sequence Analysis, RNA, Systems Biology
Abstract

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.

DOI10.1038/msb.2011.52
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/21811229?dopt=Abstract
PMCPMC3202792
Alternate TitleMol. Syst. Biol.
PubMed ID21811229