4. Disconnect across scales.
Statistical treatments typically analyze proteomic data in isolation, without embedding them in population dynamics, ecological contexts, or evolutionary landscapes. This limits their ability to bridge molecular signatures with broader evolutionary processes. The consequence is a persistent explanatory gap: we know that genetic codes and proteins are correlated, but we lack a formal, reproducible framework for understanding why such correlations arise and how they stabilize. Addressing this gap requires a paradigm shift---from descriptive statistics to Complex Adaptive Systems modeling, where interdependence, feedback, and emergent attractors can be formalized mathematically.
III. Theoretical Foundations
A. Principles of Complex Adaptive Systems
Complex Adaptive Systems (CAS) are systems composed of multiple interacting components, or agents, whose local interactions generate global patterns that cannot be reduced to the properties of individual parts. CAS theory has been applied across domains ranging from ecology to economics, and it provides a unifying framework for understanding how feedback, nonlinearity, and adaptation produce emergent order.
Key principles of CAS directly relevant to RNA--protein coevolution include:
1. Decentralized interactions.
No central controller dictates outcomes; rather, system-level organization emerges from the collective dynamics of many local interactions. In molecular evolution, RNA motifs and protein domains interact locally (through binding affinities, codon assignments, or folding constraints), yet produce global structures such as the ribosome.
2. Feedback loops.
CAS are characterized by both positive and negative feedback. For RNA--protein systems, RNA influences the production and structure of proteins, while proteins stabilize, modify, or translate RNA. These reciprocal feedbacks are the basis for synchronized adaptation.
3. Nonlinear genotype--phenotype mapping.
