(27be) Agent-Based Metabolic Modeling for the Rational Engineering of Chlamydomonas Reinhardtii

The economic cultivation of algae requires growth under natural outdoor light, but the diurnal cycle of sunlight poses a challenge to modeling efforts. We have developed a fully functional three-dimensional agent-based model to simulate Chlamydomonas reinhardtii growth under diurnal conditions. The model combines systems biology data with agent-based modeling and detailed tracking of nutrient and light conditions to accurately predict the growth of single-gene knockouts and identify potential targets for rational engineering efforts to increase productivity. The model was also used to investigate the role of the ATO1 gene in peroxisomal oxidation and oil accumulation and to predict productivity based on genetic knockouts. The development of the agent-based model addresses the limitations of traditional metabolic and bioreactor models for photosynthetic microorganisms, which are less applicable due to the transient nature of diurnal growth and self-shading. Here, we use CRISPR/Cas9-mediated genetic engineering to produce high-productivity mutant strains predicted by the model and characterize their growth rates, biomass composition, and metabolic footprint.