Host-Aware Control of Gene Expression using Data-Enabled Predictive Control
For researchers in synthetic biology and biomanufacturing, DeePC offers a sample-efficient, model-free control method that adapts to system variations without retraining.
This work applies Data-Enabled Predictive Control (DeePC) to regulate gene expression and host growth rate in bacteria, achieving robust performance with minimal data compared to other control strategies.
Cybergenetic gene expression control in bacteria enables applications in engineering biology, drug development, and biomanufacturing. AI-based controllers offer new possibilities for real-time, single-cell-level regulation but typically require large datasets and re-training for new systems. Data-enabled Predictive Control (DeePC) offers better sample efficiency without prior modelling. We apply DeePC to a system with two inputs (optogenetic control and media concentration) and two outputs (expression of gene of interest and host growth rate). Using basis functions to address nonlinearities, we demonstrate that DeePC remains robust to parameter variations and performs among the best control strategies while using the least data.