By grounding our evaluation of synthetic enzyme evolution in these systemic and emergent metrics, we move beyond brute-force enumeration toward principled, interpretable, and predictive frameworks, suitable for integration with machine learning models and high-throughput experimental pipelines.
Section 4. Synthetic Evolution Simulation Architecture
A. Designing an in silico Evolution Engine Using CAS Principles
To faithfully simulate the evolution of synthetic enzymes capable of biodegrading complex polymers (e.g., plastics), we propose an in silico evolution engine that is explicitly grounded in the principles of Complex Adaptive Systems (CAS). This engine is not a linear pipeline but a dynamic, self-modulating system that mirrors the co-adaptive, probabilistic, and emergent behaviors observed in biological evolution.
This section outlines the core architectural philosophy, components, and operational flow of such a simulation system.
1. Philosophical Premise: Beyond Deterministic Simulation
Traditional protein evolution models rely on sequence-based fitness predictions, which often neglect the multilevel interdependencies present in folding dynamics, energy landscapes, and adaptive pressures. CAS principles help bridge this gap by introducing a multi-agent, probabilistic, and feedback-rich environment in which artificial protein lineages evolve through simulated pressures.
Key theoretical underpinnings include:
Emergence: New functions may arise from combinations of residues or mutations not individually beneficial.
Nonlinearity: Mutation impacts are not additive; small changes can cause folding bifurcations or systemic failures.
Distributed adaptation: Mutational information is not stored centrally but emerges from iterative feedback between structural modules.
