Using machine learning to optimise biofuel and industrial compound production in cyanobacteria

Cyanobacteria (oxygenic photosynthetic bacteria) are potential biotechnology platforms, since they can convert carbon dioxide into biofuels and industrial/pharmaceutical chemicals using energy derived from sunlight. This technology offers the opportunity to limit carbon emissions from industry while replacing petroleum derived compounds with renewable alternatives. However, commercialisation is dependent on improved understanding of cyanobacterial metabolism. In collaboration with Dr Dongda Zhang (University of Manchester) and Simon Moxon (UEA) the student will apply machine learning approaches to investigate cyanobacterial metabolism and identify optimal pathways and metabolic fluxes for compound production. The student will test these outcomes by generating appropriate mutants and measuring growth and compound production of these strains in small reactors. Commercially relevant mutants will then be tested in larger industrial reactors in collaboration with our industrial partner, Cyanetics Ltd, using carbon dioxide emissions from industrial plants. The ideal candidate for this project will have a background in chemical engineering, bioengineering, computer science, or molecular biology/microbiology with programming skills. The student will join a cyanobacterial biology laboratory with strong national and international links. Examples of recent publications from the investigator include: Saar et al (2018) Nature Energy 3 (1) 75; Lea-Smith et al (2016) Plant Phys. 172(3):1928-1940; Lea-Smith et al (2015) PNAS 112(44):13591-6; Lea-Smith et al (2014) Plant Phys. 165(2):705-714. This project offers the opportunity to develop skills in machine learning, molecular biology, microbiology and chemical engineering which will aid a future career in academia or industry.