ArticleComputational and structural biotechnology journal2026
Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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