Deep mutational scanning (DMS), mapping large fitness landscapes through barcoded libraries and next-generation sequencing
Fluorescence-activated droplet sorting (FADS) for activity-based enrichment
When aligned with CAS-based mutational trajectories, these HTP techniques serve not just as validation tools, but as empirical guidance systems---identifying regions of high functional density in the mutational landscape and feeding that insight back into the simulation.
3. Closed-Loop Optimization: Evolution as Engineering
The integration between CAS simulation and wet-lab validation is best conceptualized as a closed-loop architecture, where:
Simulations generate candidates based on emergent systemic criteria.
High-throughput assays validate and quantify real-world function.
Empirical results are fed back into the model, refining probabilistic mutation logic, network weightings, or fitness scoring functions.
This cyclical design process transforms synthetic evolution into an engineering discipline, where evolutionary trajectories can be steered---not through deterministic design, but through adaptive convergence between digital and physical biology.
Furthermore, reinforcement learning agents can be rewarded using empirical data, such as catalytic yield or degradation half-life, creating a hybrid AI-biowet model that continuously improves itself.
4. Beyond Validation: Emergent Discovery
