Accelerating Cell Culture Media Development Using Bayesian Optimization-Based Iterative Experimental Design

Abstract

Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we have applied a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show this approach yields new compositions of media with cytokine supplementation to maintain the viability and distribution of PBMCs in culture. Second, we applied this framework to optimize the production of three recombinant proteins in K.phaffii cultivations. For both applications, we identified conditions with improved outcomes compared to the initial standard media using 3 to 30 times fewer experiments than other methods such as the 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 tradeoff in each iteration, can reduce the time and resources for these types of optimizations.