Agent-Based Algal Modeling for the Rational Engineering of Chlamydomonas reinhardtii
Sandra Gomez Romero, Alex J. Metcalf, and Nanette R. Boyle*
Colorado School of Mines
The overall research objective is to develop an experimentally validated multiparadigm multiscale modeling framework that will enable the most advanced and predictive metabolic modeling of diurnally grown photosynthetic organisms to date. The genome-scale metabolic model of Chromochloris zofingiensis will be embedded into an agent-based modeling framework to allow modeling of diurnal growth; the model will also be able to simulate intracellular fluxes, cell-to-cell interactions, cell-to-environment interactions, metabolite diffusion, and spatial distribution. This modeling approach will allow simulation of metabolic shifts that occur due to diel cycles and generation of rational engineering strategies to design production strains that are not impacted negatively by this natural phenomenon.
Economical algae production requires growth under outdoor light, but the diel nature of sunlight complicates modeling efforts. Researchers have developed a solution for that: a fully functional 3D agent-based model, capable of simulating algal growth under diurnal conditions. By combining systems biology data from Chlamydomonas reinhardtii grown in diurnal light (Strenkert et al. 2019) with agent-based modeling and detailed tracking of nutrient and light conditions, this model performs better than traditional steady-state metabolic models (Metcalf et al. 2022). In order to develop a model of growth during diurnal light, the team needed to decouple the standard biomass formation equation to allow different components of biomass to be synthesized at different times of the day. The model was able to more accurately predict qualitative phenotypical outcomes of the starchless mutant, sta6. The model then predicted growth of single-gene knockouts, and potential targets were identified for rational engineering efforts to increase productivity. The team will discuss recent advances in characterizing these mutants and further improvement of the model by including light and nutrient tacking. This model enables evaluation of the impact of genetic and environmental changes on the growth, biomass composition, and intracellular fluxes for diurnal growth.
Metcalf, A. J., et al. 2022. “Rhythm of the Night (and Day): Predictive Metabolic Modeling of Diurnal Growth in Chlamydomonas,” mSystems 7(4), e00176-00122.
Strenkert, D., et al. 2019. “Multiomics Resolution of Molecular Events During a Day in the Life of Chlamydomonas,” Proceedings of the National Academy of Sciences of the United States of America 116, 2374–83.