Integrating Machine Learning and Transcriptomics to Enhance β-Carotene Production in Yeast

In the second DBTL cycle, fine-tuning MVA gene expression resulted in a 139% improvement in β-carotene titer. We then supplemented β-carotene production with iron, guided by transcriptional insights in alternations of genes related to iron uptake in β-carotene-producing yeast, resulting in an 70.54% improvement in β-carotene titer at 48 hours. Moreover, integrating the fine-tuned MVA cassette with iron supplementation yielded up to 72.07 mg/L of β-carotene at 72 hours, representing a 67.79% increase compared to that of the parent strain without MVA gene adjustments and iron supplementation.

Our study highlights the potential of combining machine learning and omics approaches with synthetic biology to enhance non-native biochemical production in yeast.