Unlike black-box models, the CAS-based engine emphasizes transparency and traceability:
Each fitness decision is linked to interpretable sub-metrics (e.g., hydrogen bond disruptions, catalytic distance changes).
Evolutionary paths are tracked as mutation trees or interaction graphs, enabling hypothesis testing on: Sequence-function relationships. Structural bottlenecks. Mutational robustness vs. fragility.Â
The output is not merely a set of optimized sequences but a navigable landscape of evolutionary logic, capable of informing real-world bioengineering decisions.
4.B. Incorporation of Reinforcement Learning Agents as Mutation Drivers
As synthetic enzyme evolution increasingly demands adaptive, intelligent control over mutational exploration, we propose the integration of Reinforcement Learning (RL) agents as dynamic mutation drivers within the CAS-based simulation framework. This approach marries complex systems theory with agent-based AI, enabling mutations to emerge not merely from stochastic sampling but from learned policies shaped by evolutionary outcomes.
1. Rationale for Using RL in Molecular Evolution
Traditional evolutionary algorithms (EAs) apply mutations using static or probabilistically weighted strategies. While effective in low-dimensional optimization, such methods are often:
Blind to context (e.g., residue environment, folding strain, active site location)
Inefficient in rugged fitness landscapes, prone to local minima
Non-adaptive to emergent constraints over long generations
