What is it about?
We propose the use of Machine Learning approaches to perform a data-driven optimization of an mRNA in vitro translation reaction. A Bayesian optimization method and model interpretability techniques were used to automate experiment design, providing a feedback loop. IVT reaction conditions were found under 60 optimization runs that produced 12 g/L in solely two hours. The results obtained outperform published industry standards and data reported in literature in terms of both achievable reaction yield and reduction of production time. Furthermore, this shows the potential of Bayesian optimization as a cost-effective optimisation tool within (bio)chemical applications.
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Why is it important?
mRNA vaccines are a new alternative to conventional vaccines with a prominent role in infectious disease control. These vaccines are produced in in vitro transcription (IVT) reactions, catalyzed by RNA polymerase in cascade reactions. To ensure an efficient and cost-effective manufacturing process, essential for a large-scale production and effective vaccine supply chain, the IVT reaction needs to be optimised.
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This page is a summary of: Maximizing mRNA vaccine production with bayesian optimization, Biotechnology and Bioengineering, August 2022, Wiley,
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