Machine Learning-Guided Optimization of p -Coumaric Acid Production in Yeast
Problem
Optimizing microbial cell factories (p-coumaric acid production in yeast) through DBTL cycles is slow without effective links between phases.
Why it matters
Efficient microbial production is required for the transition to a bio-based economy.
What the AI did
Machine learning models, with feature importance and SHAP values, linked DBTL cycle phases and guided expansion of the design space.
Results
68% increased production of p-coumaric acid within two DBTL cycles
Impact
Demonstrated ML-guided DBTL optimization achieving a concrete production improvement in yeast.
Record
| Venue | ACS Synthetic Biology |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Sara Moreno Paz, Rianne van der Hoek, Elif Eliana, Priscilla Zwartjens, S. Gosiewska, Vítor A. P. Martins dos Santos, Joep P. J. Schmitz, Maria Suarez Diez |
| Citations | 40 (OpenAlex, at import time) |
| References | 37 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1021/acssynbio.4c00035 |
| Open access | hybrid |
| Keywords | machine learning, p-coumaric acid, yeast, DBTL, SHAP, pathway optimization |
Importance rating
significant — thousands to millions affected
Abstract states microbial cell factories are required for the transition to a bio-based economy; ML-guided DBTL optimization benefits industrial biotechnology, though demonstrated on a single p-coumaric acid case study.