Machine-Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: A p-Coumaric Acid Case Study

Combinatorial pathway optimization is a powerful approach in metabolic engineering to improve strain performance. While machine learning (ML) has shown promise in guiding the Design–Build–Test–Learn (DBTL) cycle, most applications have been limited to small design spaces, thereby restricting the potential of predictive and exploration-exploitation strategies. In this work, we applied two DBTL cycles to optimize p-coumaric acid production in Saccharomyces cerevisiae. The first cycle involved constructing a large combinatorial library of 18 genes and 20 promoters (170 million possible designs). In the second cycle, we employed a gradient bandit-based machine learning recommendation strategy, tuned to balance exploration and exploitation. Our results show that this balanced strategy outperforms greedy, feature importance-based approaches, leading to greater diversity in strain performance and improved top-producer identification. Notably, applying the same strategy to an alternative parent strain yielded the highest p-coumaric acid titer (1.23 g/L), a 2.37-fold improvement over the original. These findings highlight the value of ML-guided exploration in large design spaces and demonstrate that balancing exploration and exploitation is critical for successful strain optimization.