(27cb) Development of a Genome-Scale Metabolic Model for Auxenochlorella Protothecoides to Enable Rational Engineering

With increasing energy demands across the globe and the need to mitigate climate change, it is imperative that we develop renewable and/or carbon negative fuels rapidly. Algae have the potential to serve as a source of sustainable fuels and chemicals, but more focused efforts to engineer metabolism is needed. One approach to aide in rational engineering is the use of genome-scale metabolic models, which allow the investigation of different environmental or genetic background on productivity. Here, we will describe the development and use of a genome scale metabolic of an oleaginous green alga, Auxenochlorella protothecoides (A. pro.) to predict increased carbon fluxes toward biofuel precursors. A. pro. can accumulate up to 60% dry weight as triacylglycerols using photosynthesis [1]. These triacylglycerols can be used to synthesize cyclopropane fatty acids, which are core components of sustainable aviation fuels (SAFs). The initial draft network was reconstructed using RAPS, an automated algorithm developed by our lab to generate first draft metabolic networks of algae [2]. We will present our experimental data on how different growth conditions impact biomass compositions, simulations of carbon fluxes for these growth conditions and how they compare to ¹³C-MFA studies.

References

[1] Matsuka and E. Hase, “The Role of Respiration and Photosynthesis in the Chloroplast Regeneration in the ‘Glucose-bleached’ Cells of Chlorella Protothecoides,” Plant and Cell Physiology, vol. 7, no. 1, pp. 149–162, Mar. 1966, doi: 10.1093/oxfordjournals.pcp.a079161.

[2] J. Metcalf, A. Nagygyor, and N. R. Boyle, “Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes,” Algal Research, vol. 50, p. 101967, Sep. 2020, doi: 10.1016/j.algal.2020.101967.