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From biodiversity to design: a phylogeny-aware, data-driven framework for biological model selection in bio-inspired design
Summary
Scientists have created a smart tool that helps engineers pick the best animal or organism to copy when designing new technology—like filters that remove harmful microplastics from water. As a case study, they tested it on 35 filter-feeding sea creatures (like clams and sponges) to find which ones offer the best blueprints for microplastic-catching devices, which could eventually help reduce our exposure to these tiny pollutants in drinking water and food. This is an early-stage design framework, not a finished filtration product, but it could speed up the development of nature-inspired solutions to the microplastics problem.
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.