Microscopic algae are a promising future platform for the sustainable production of biofuels. These organisms use sunlight, atmospheric carbon dioxide, and nutrients such as nitrogen and phosphorous dissolved in liquid medium to make lipids which can be processed into liquid transportation fuel. Most algal biofuel processes require two stages. In the first stage, the algae consume nutrients and grow. In the second stage, the lipids used to make biofuel accumulate within the biomass, but only when all the nutrients are consumed so that the biomass does not grow any more. In order to improve the economic viability of algae-based biofuels, it is necessary to develop strains of algae that can generate lipids for biofuel while producing more biomass that sustains the process. The goal of this project is re-program the gene networks in a model strain of algae named Chlamydomonas reinhardtii to enhance both biofuel and biomass production at the same time. The research will attempt to make the systems biology platform more generic that it can be extended to other organisms. The educational activities associated with the project will develop high school curricular materials for renewable green biotechnology topics.
A critical challenge with algal biofuel production is that nutrient starvation is required to induce lipid accumulation. The proposed research will develop a systems biology strategy to predictably manipulate regulatory and metabolic networks using the model photosynthetic green microalga Chlamydomonas reinhardtii in an effort to significantly enhance lipid accumulation without growth arrest. A predictive Environment and Gene Regulatory Influence Network (EGRIN) model will be developed to rationally identify gene targets for systems level re-engineering. The EGRIN model will be built upon a compendium of transcriptomes from cultures of C. reinhardtii grown in a diverse set of nutritional and environmental conditions. Accuracy of the EGRIN model will be improved by incorporating experimentally mapped open chromatin structure. Further, the EGRIN model will be integrated with a metabolic network model to identify gene targets for engineering. Finally, model-guided genome engineering of algae using CRISPR/Cas9 technology will be used to enhance biomass or lipid production. The proposed research will generate a fundamental, model-guided strategy for predictably manipulating regulatory and metabolic networks in algae through a generalized approach which can be customized to perform similar strain engineering objectives other organisms.
