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 both costly and time-consuming. Here, we present gsMOBO, a computational approach for media design that integrates genome-scale metabolic models with multiobjective optimization. Our method employs Flux Balance Analysis coupled with a top-layer Bayesian optimization routine for efficient exploration and optimization of nutrient combinations across high-dimensional spaces. We show that our method 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 Paretooptimal trade-offs consistent with prior experimental work. Our method provides a broadly applicable tool for the design of cost-effective and productive culture media, and offers a route to accelerate medium development in biomanufacturing.