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

Roger L. Chang, Lila Ghamsari, Ani Manichaikul, Erik F.Y. Hom, Santhanam Balaji, Weiqi Fu, Yun Shen, Tong Hao, Bernhard Palsson, Kourosh Salehi-Ashtiani, Jason A. Papin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number518
JournalMolecular systems biology
StatePublished - 2011


  • Chlamydomonas reinhardtii
  • lipid metabolism
  • metabolic engineering
  • photobioreactor

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Information Systems
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Computational Theory and Mathematics


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