(338d) Integrating Circadian Gene Expression Data into Metabolic Models to Improve Dynamic Flux Modeling

Photosynthetic microorganisms have the potential to become economical and sustainable sources of fuels, as the energy required for the cell to grow can be sourced from natural sunlight alone; however, we have yet to harness their full power due to a general lack of tools for engineering their metabolism. Metabolic models have been shown to drastically reduce the development time for commercial production strains of heterotrophic bacteria, but these models are less applicable to photosynthetic systems due to the transient nature of diurnal (day/night) growth. Current metabolic models are not capable of accurately predicting growth rates in day/night growth cycles, let alone genetic changes which would lead to increased yields. We are building the next generation of metabolic models, specifically for photosynthetic microorganisms, in order to more accurately predict changes in carbon fluxes during diel light. We have developed a pipeline to convert transient transcript expression data into time-dependent constraints for a flux balance analysis model. Here, we will present our work on the model green alga, Chlamydomonas reinhardtii. We will discuss how the carbon is redirected as the alga prepares for the transition from day to night and vice versa. We will also discuss the differences in essential genes in our circadian model versus a normal FBA model.