Assessment of the current genome-scale metabolic model of Chlorella as a synthetic design tool

Chlorella species are widely recognized for their rapid growth, high biomass productivity, and lipid accumulation, making them promising candidates not only for wastewater treatment but also for biomass recovery and reuse in applications such as biofuel production, animal feed, and biofertilizers. To enhance these biotechnological potentials, metabolic modeling provides a systematic framework for understanding and engineering cellular metabolism. Genome-scale metabolic models (GEMs) have therefore emerged as powerful tools for guiding strain design and metabolic engineering. However, selecting an appropriate model for a specific Chlorella strain remains challenging due to variations in biomass composition and metabolic constraints across published models. In this study, we performed a preliminary evaluation of existing Chlorella metabolic models to assess their suitability for model-guided strain design. Strainspecific data, including biomass equations and constraint parameters, were integrated into the models, and growth optimization simulations were conducted. Model performance was compared based on their ability to accurately predict strain-specific growth potential. The results indicate that models incorporating strainspecific biomass and constraint data provide improved predictive accuracy. These findings highlight the importance of careful model selection before applying GEMs to strain-specific designs. Overall, this evaluation provides a framework for identifying suitable Chlorella metabolic models for future strain engineering efforts. Moreover, this approach can accelerate the development of high-performance Chlorella strains for biotechnological and synthetic biology applications.