Dual-Objective Mechanobiological Growth Optimization for Heterogenous Lattice Structures

Abstract

Computational design is growing in necessity for advancing biomedical technologies, particularly for complex systems with numerous trade-offs. For instance, in tissue scaffolds constructed from repeating unit cells, the structure's porosity and topology affect biological tissue and vasculature growth. Here, we adapt curvature-based tissue growth and agent-based vasculature models for predicting scaffold mechanobiological growth. A non-dominated sorting genetic algorithm (NSGA II) is used for dual-objective optimization of scaffold tissue and blood vessel growth with heterogeneous unit cell placement. Design inputs consist of unit cells of two different topologies, void unit cells, and beam diameters from 64 to 313 μm. Findings demonstrate a design heuristic for optimizing scaffolds by placing two selected unit cells, one that favors high tissue growth density and one that favors blood vessel growth, throughout the scaffold. The pareto front of solutions demonstrates that scaffolds with large porous areas termed Channel Voids or Small Voids improve vasculature growth while lattices with no larger void areas result in higher tissue growth. Results demonstrate the merit in computational investigations for characterizing tissue scaffold design trade-offs, and provide a foundation for future design multi-objective optimization for complex biomedical systems.