Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism
Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. In contrast, genome-scale metabolic models capture species-specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here we combine chlorophyll fluorescence parameters defining photosynthetic and non-photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Additionally, our simulations recapitulate the link between mitochondrial dissipation of photosynthetically-derived electrons and the redox state of the photosynthetic electron transport chain. We use this framework to assess engineering strategies for rerouting cellular resources toward bioproducts. Overall, we present a methodology for incorporating a common, informative data type into computational models of light-driven metabolism for characterization, monitoring and engineering of photosynthetic organisms.