Machine learning boosts spirulina bioproduction by up to 100 percent

Collaborating with Google, Lumen Bioscience applied Bayesian black-box optimisation, a machine learning approach, to increase spirulina biomanufacturing productivity. A new paper shows that applying machine learning (ML) to bioproduction can significantly increase recombinant protein production and thus advance the scalability of spirulina-based biologic drugs. Under a research collaboration funded in part by the Bill & Melinda Gates Foundation, Lumen Bioscience worked with Google Accelerated Science to apply ML to increase the productivity of Arthrospira platensis (spirulina) using Bayesian black-box optimisation. The results of the collaboration, which are pending peer-review, have been published in pre-print on the bioRxiv server. The paper describes how the ML approach of Bayesian black-box optimisation was used to guide experiments in 96 photobioreactors, exploring the relationship between production outcomes and 17 environmental variables, including pH, temperature and light intensity.