Unlike traditional models that map genotype to phenotype linearly, CAS assumes nonlinearity and emergence: small changes in high-weight nodes may lead to disproportionate shifts in output, while many low-impact mutations may collectively result in significant functional innovations.
Synthesis: Toward a Unified CAS Evolutionary Engine
Taken together, these six variables enable the construction of a computational landscape in which proteins are not statically optimized, but dynamically evolved across a field of probabilities, constraints, and structural interdependencies. The framework facilitates:
Simulating non-trivial mutational paths,
Identifying high-impact mutation clusters, and
Forecasting long-term functional emergence under synthetic selection pressures.
This conceptual and algorithmic foundation sets the stage for the development of predictive tools that go beyond conventional sequence-structure-function modeling, aiming instead to simulate and direct adaptive emergence in enzyme engineering.
2.B. Integration of Thermodynamic Principles and Probabilistic Mutation Logic
The evolution of synthetic enzymes, particularly those designed for high-performance catalysis of complex substrates such as plastics, cannot be adequately captured through deterministic pathways alone. Instead, it demands a modeling architecture that integrates thermodynamic constraints with probabilistic mutation dynamics---a synthesis well-suited to the Complex Adaptive Systems (CAS) framework.
This section lays out how classical thermodynamics (free energy landscapes, stability, enthalpic and entropic balances) can be mathematically and conceptually unified with stochastic models of mutation to simulate adaptive protein evolution more realistically and systematically.
1. Thermodynamics as an Evolutionary Landscape
