From biodiversity to design: a phylogeny-aware, data-driven framework for biological model selection in bio-inspired...

Area of Science:

• Biomimetics and Bio-inspired Engineering
• Evolutionary Biology
• Data Science in Design

Background:

• Selecting appropriate biological models is a critical yet informal step in biomimetic design.
• Current tools lack guidance for prioritizing among multiple viable biological candidates.
• This gap hinders efficient and effective transfer of biological principles to engineering solutions.

Purpose of the Study:

• To develop and present a structured, data-driven framework for biological model selection in biomimetic design.
• To provide a quantitative method for comparing and ranking potential biological models.
• To address the under-supported decision-making process in choosing biological models.

Main Methods:

• Developed a phylogeny-informed, data-driven framework for explicit comparison of biological models.
• Implemented four scoring modules: data sufficiency, innovativeness, phylogenetic characteristics, and contextual constraints.
• Utilized user-defined weights for modules to tailor rankings to specific project requirements.

Main Results:

• Demonstrated the framework using a microplastic-filtration case study with 35 suspension-feeding taxa.
• Generated ranked shortlists of biological models based on different user scenarios and priorities.
• Identified both robust, consistently competitive candidates and context-dependent opportunities.

Conclusions:

• The framework provides a structured decision-support layer for biological model selection in biomimetic workflows.
• It enables explicit comparison and prioritization, moving beyond informal selection processes.
• This approach enhances the efficiency and effectiveness of identifying suitable biological models for engineering innovation.