These metrics form multi-scalar functions over the mutation space,
Their interdependence is visualized as contour manifolds over the adaptive topology,
Pareto frontiers can be constructed to identify optimal trade-offs between competing metrics (e.g., binding vs. stability, or specificity vs. activity).
Additionally, emergent function can be characterized by:
Functional robustness: The persistence of performance metrics under stochastic perturbations (simulated by Gaussian noise or random edge rewiring),
Evolvability potential: The local topology's capacity to generate novel high-function mutants through accessible mutational pathways.
4. Implications for Predictive Evolutionary Design
Incorporating these metrics within our modeling pipeline allows for:
Selection of candidate mutants not solely on static structural similarity but on projected functional advantage,
Evolutionary trajectory mapping based on adaptive fitness gradients,
Constraint-aware generative design, where synthetic enzymes are optimized within known thermodynamic and kinetic bounds.
