Bayesian multi-objective optimisation with mixed analytical and black-box functions: application to tissue engineering - Olofsson,SCW; Mehrian,M; Calandra,R; Geris,L; Deisenroth,MP; Misener,R (IEEE Transactions on Biomedical Engineering, 2019)
Tissue engineering and regenerative medicine looks at improving or restoring biological tissue function in humans and animals. We consider optimising neotissue growth in a three-dimensional scaffold during dynamic perfusion bioreactor culture, in the context of bone tissue engineering. The goal is to choose design variables that optimise two conflicting objectives: (i) maximising neotissue growth and (ii) minimising operating cost. We make novel extensions to Bayesian multi-objective optimisation in the case of one analytical objective function and one black-box, i.e. simulation-based, objective function. The analytical objective represents operating cost while the black-box neotissue growth objective comes from simulating a system of partial differential equations. The resulting multi-objective optimisation method determines the trade-off in the variables between neotissue growth and operating cost. Our method outperforms the most common approach in literature, genetic algorithms, in terms of data efficiency, on both the tissue engineering example and standard test functions. The resulting method is highly applicable to real-world problems combining black-box models with easy-to-quantify objectives like cost.