Accelerating cell culture media development using Bayesian optimization-based iterative experimental design
Harini Narayanan, Joshua A. Hinckley, Rachel Barry, Brendan Dang, Lenna A. Wolffe, Adel Atari, Yuen‐Yi Tseng, J. Christopher Love
Abstract
Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we apply a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show that this approach yields new compositions of media with cytokine supplementation to maintain the viability and distribution of human peripheral blood mononuclear cells in the culture. Second, we apply this framework to optimize the production of three recombinant proteins in cultivations of K.phaffii. We identified conditions with improved outcomes for both applications compared to the initial standard media using 3-30 times fewer experiments than that estimated for other methods such as the standard Design of Experiments. Subsequently, we also demonstrated the extensibility of our approach to efficiently account for additional design factors through transfer learning. These examples demonstrate how coupling data collection, modeling, and optimization in this iterative paradigm, while using an exploration-exploitation trade-off in each iteration, can reduce the time and resources for complex optimization tasks such as the one demonstrated here.