Toward Organic Data Structures: A Manifesto for Bio-Inspired Computing Primitives

Classical data structures are designed with mechanical metaphors: stacks, queues, trees, heaps. They are inert objects with fixed performance profiles that cannot adapt to changing workloads, cannot self-tune under shifting access patterns, and cannot gracefully degrade when pushed beyond design parameters. We argue that biological systems offer a richer vocabulary of organizational patterns that can yield data structures with emergent properties: self-healing, adaptive performance, lifecycle-driven optimization, and graceful degradation. This paper outlines the Mutuus research program. We introduce the organic/inorganic taxonomy, a three-tier framework spanning data structures (Organics), operational strategies (Metabolics), and coordination patterns (Ecologics), and a rigorous evaluation methodology that rejects hand-wavy biological metaphors in favor of formal specification, complexity analysis, and reproducible benchmarking. We present three implemented exemplars (Nacre Array, Diatom Bitmap, and Mycelial Cache) and propose the Metabolic Cost Principle: that nature-inspired primitives should be evaluated on lifecycle economics, not snapshot benchmarks.