Optimization of muscle cell culture media using nonlinear design of experiments

Optimizing media for biological processes, such as those used in tissue engineering and cultivated meat production, is difficult due to the extensive experimentation required, number of media components, nonlinear and interactive responses, and the number of conflicting design objectives. Here we demonstrate the capacity of a nonlinear design‐of‐experiments (DOE) method to predict optimal media conditions in fewer experiments than a traditional DOE. The approach is based on a hybridization of a coordinate search for local optimization with dynamically adjusted search spaces and a global search method utilizing a truncated genetic algorithm using radial basis functions to store and model prior knowledge. Using this method, we were able to reduce the cost of muscle cell proliferation media while maintaining cell growth 48 h after seeding using 30 common components of typical commercial growth medium in fewer experiments than a traditional DOE (70 vs. 103). While we clearly demonstrated that the experimental optimization algorithm significantly outperforms conventional DOE, due to the choice of a 48 h growth assay weighted by medium cost as an objective function, these findings were limited to performance at a single passage, and did not generalize to growth over multiple passages. This underscores the importance of choosing objective functions that align well with process goals. Graphical and Lay Summary Layman's Summary of Work: In order to improve quality of muscle cell growth media, the effect of various metabolites were modeled using data collected in lab. The data is then used to suggest improvements to the medium using an algorithm called HND (hybrid nonlinear designer). This was done over multiple iterations to continually improve the media and did so using fewer experiments than traditional methods (70 vs. 103 experiments respectively).