A Complex Adaptive Systems Framework for Predicting the Synthetic Evolution of Anti-Plastic Enzymes
Abstract
The exponential accumulation of synthetic polymers in the biosphere presents an escalating environmental crisis, demanding biotechnological innovations beyond conventional degradation strategies. While recent discoveries such as PETase and MHETase offer biological blueprints for plastic biodegradation, the evolutionary refinement of these enzymes remains largely stochastic and inefficient. This paper proposes a novel theoretical and computational framework grounded in Complex Adaptive Systems (CAS) to model and predict the evolutionary trajectories of synthetic anti-plastic enzymes. By encoding six core CAS variables---interaction levels, structural permutations, probabilistic affinity, interaction weights, systemic stability, and emergent functionality---our model offers a mesoscale approach that bridges molecular dynamics and evolutionary selection. We argue that such a framework not only enhances predictive design in synthetic biology but also enables the emergence of adaptive enzyme variants tailored for diverse polymeric substrates. This work lays the foundation for a systems-level evolutionary design tool, integrating bioinformatics, thermodynamic modeling, and machine learning, aimed at accelerating the discovery of next-generation bio-remediating enzymes.
1. Empirical and Technical Background
The global challenge posed by synthetic plastic pollution---particularly polyethylene terephthalate (PET), polyurethane, and polystyrene---has stimulated a surge of interest in enzymatic plastic degradation. Enzymes such as PETase (from Ideonella sakaiensis) and its engineered variants have demonstrated the capacity to hydrolyze high-molecular-weight polymers under mild conditions. However, directed evolution approaches remain time-intensive, with limited predictive power regarding mutation effects, folding outcomes, or substrate specificity.
Current computational strategies---ranging from AlphaFold's structural prediction to Rosetta-based docking and thermodynamic simulations---excel in local accuracy but often neglect the systemic interdependencies and emergent behaviors crucial in enzyme function. These limitations point toward a need for a more integrated theoretical architecture---one that views molecular systems not as static structures, but as adaptive agents within complex evolutionary landscapes.
The Complex Adaptive Systems (CAS) framework, originally developed in ecological, economic, and neural network studies, provides such a paradigm. In CAS, systems evolve through nonlinear interactions among multiple components, leading to emergent global behavior that cannot be reduced to the properties of individual elements. Translating this perspective into enzyme evolution allows us to reframe mutations, folding states, and environmental constraints as interlinked variables within a dynamic adaptive network.
This work introduces a theoretical architecture and modeling strategy that integrates CAS theory with protein engineering. Our goal is to construct a mesoscale simulation model that predicts the emergence of synthetic enzyme variants with enhanced plastic-degrading capabilities. This approach opens pathways to pre-screening evolutionary trajectories, identifying mutation clusters that maximize functional emergence, and understanding long-term fitness landscapes shaped by both thermodynamic and ecological constraints.
Outline
2. Theoretical Foundations
Definition of the six CAS variables in the context of protein evolution
