Evolution's Solutions Are Being Lost Before We Can Decode Them

Over billions of years, organisms have evolved solutions to challenges humans face today — corrosion resistance, energy-efficient locomotion, drought tolerance, pathogen defense, carbon fixation, self-healing materials — but most of these evolved innovations remain undiscovered and poorly understood. Despite explosive growth in genomic resources and computational tools, the functional mechanisms underlying most organisms' adaptive traits are unknown. Simultaneously, high extinction rates are permanently destroying these biological innovations before they can be studied. A fundamental disciplinary gap separates molecular biologists who could decode mechanisms from evolutionary biologists who know where to look, and neither group routinely connects with engineers who could translate discoveries into applications.

The bioeconomy — industrial processes, pharmaceuticals, agriculture, energy production, nature-based climate solutions — depends on understanding biological innovations. Executive Order 14081 directed federal agencies to leverage biological discoveries for a sustainable, safe, and secure American bioeconomy. Convergent evolution, where unrelated species independently evolve similar solutions to the same problem, provides a natural experimental framework for understanding why certain solutions work — but this comparative approach is vastly underutilized. Every species extinction permanently closes a door on potential solutions to current and future challenges.

Biomimicry efforts typically study a single organism's trait in isolation and try to replicate it, missing the deeper mechanistic understanding that comparative evolutionary approaches provide. Genomic sequencing alone is insufficient: having a genome does not mean understanding the functional significance of the innovations it encodes. AI-driven protein structure prediction (e.g., AlphaFold) is a powerful tool but is disconnected from ecological and evolutionary context — we can predict protein structure but cannot systematically identify which evolved proteins represent solutions to engineering-relevant challenges. Biological collections (museums, herbaria, biobanks) contain vast untapped resources but lack the metadata and computational infrastructure to enable systematic discovery. The intellectual and cultural silos between molecular biology, evolutionary biology, and engineering have resisted integration despite decades of calls for interdisciplinary work.

Computational pipelines that systematically scan genomes across the tree of life for convergent functional innovations relevant to specific engineering challenges; integration of generative AI protein modeling with evolutionary and ecological context; scalable methods to functionally characterize evolved innovations from genomic data without needing to culture the organism; and partnership frameworks that connect fundamental biologists with bioeconomy translators in engineering and industry.

A student team could select a specific engineering challenge (e.g., anti-fouling surfaces, freeze tolerance, or UV resistance) and use publicly available genomic databases (NCBI, UniProt) and phylogenetic tools to identify convergent evolution events across distantly related species, then characterize the candidate genes and proteins computationally. This is feasible as a bioinformatics project using existing databases and open-source tools. A team with wet-lab access could experimentally characterize a candidate protein identified through this pipeline.