4. Feedback Loop Between Simulation and Wet-Lab Evolution
Perhaps most significantly, CAS models enable a closed-loop design-test-learn paradigm, wherein:
Simulated motifs guide wet-lab mutagenesis
Experimental results feed back into the CAS simulation as updated constraints or priors
The system continuously refines its evolutionary heuristics, improving with each iteration
This approach bridges in silico evolution and synthetic biology, enabling faster convergence toward highly functional, environmentally deployable enzymes.
5. Toward a Predictive Ecology of Biocatalysts
In the long term, CAS modeling can contribute to a predictive ecology of biocatalysts, where we not only design enzymes for degradation but also:
Model their evolutionary stability in open ecosystems
Predict their interaction with native microbial communities
Forecast potential horizontal gene transfer risks
