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From biodiversity to design: a phylogeny-aware, data-driven framework for biological model selection in bio-inspired design
Summary
Scientists are building better tools to design technology inspired by nature—like filters that mimic how animals such as clams or barnacles naturally strain particles from water. This new framework helps engineers systematically pick the best animal or organism to copy from, using a scoring system based on available research, uniqueness, and evolutionary traits, rather than relying on guesswork. The researchers tested it specifically for designing microplastic filters, which could eventually help reduce the tiny plastic particles found in our water and food supply.
Selecting suitable biological models remains one of the most challenging and least formalised steps in biomimetic design. Existing tools support searching for biological strategies and transferring principles into engineering concepts, but provide limited guidance on which organism to prioritise once multiple plausible candidates are identified. Here we present a phylogeny-informed, data-driven framework that structures model selection as an explicit comparison. The framework computes four module scores per candidate model: data sufficiency, innovativeness, phylogenetic characteristics, and an open-ended contextual module for project-specific constraints. Users assign weights to the modules to reflect resources, timelines, and design requirements, yielding an overall compatible score and a ranked shortlist. We demonstrate the framework in a microplastic-filtration case study using 35 suspension-feeding taxa and three archetypal user scenarios. Rankings reveal a compact set of candidates that remains competitive across scenarios, while a smaller subset shifts in response to changes in priorities, thereby distinguishing robust starting points from context-dependent opportunities. Overall, the framework fills an under-supported gap in current biomimetic workflows by providing a structured decision-support layer for biological model selection.