Dummy dataset with simulated mutational trajectories
Comparison with AlphaFold and other predictive models
Analysis of emergent structural motifs across evolutionary cycles
6. Implications and Future Applications
CAS-based predictive design for bioremediation enzymes
Ethical and ecological considerations in synthetic enzyme deployment
Integrating this model with wet-lab validation and high-throughput screening
Section 2. Theoretical Foundations
2.A. Definition of the Six CAS Variables in the Context of Protein Evolution
The Complex Adaptive Systems (CAS) framework offers a powerful lens through which the evolution of biomolecules---particularly enzymes---can be conceptualized not merely as a sequence of mutational events, but as a dynamic process of emergent functionality shaped by interacting variables. In the context of synthetic protein evolution, CAS theory permits the construction of models that are neither wholly deterministic nor entirely stochastic, but adaptive and systemically coherent.
Here, we define six foundational variables that comprise the CAS framework adapted to protein evolution modeling. These variables serve as the core ontological and computational scaffolding for simulating and predicting enzyme development in response to artificial selection pressures, substrate interactions, and mutational perturbations.
