Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization

The optimization of culture media is critical for improving the efficiency and cost of cellular production systems. Traditional approaches often rely on extensive experimental trials or statistical methods, which can be costly and time-consuming. Here, we present genome-scale Multiobjective Bayesian Optimization (gsMOBO) as a general and flexible computational approach for media design. Our method integrates genome-scale metabolic models into a top layer Bayesian optimization loop for efficient exploration and optimization of nutrient combinations across high-dimensional spaces. We show that gsMOBO finds optimal medium formulations along a Pareto front balancing growth, production, and cost of medium components. We illustrate the approach in models of Escherichia coli engineered to produce antibody fragments, as well as Bacillus subtilis strains that synthesize cyclic lipopeptides. Our results show that gsMOBO identifies media compositions and Pareto-optimal trade-offs consistent with prior experimental work. gsMOBO is a broadly applicable tool for the design of cost-effective and productive culture media, offering a route to accelerate medium development in biomanufacturing.