IEEE Trans Biomed Eng. 2019 Mar;66(3):727-739. doi: 10.1109/TBME.2018.2855404. Epub 2018 Jul 19.
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, first, maximising neotissue growth and, second, minimising operating cost. We make novel extensions to Bayesian multiobjective optimisation in the case of one analytical objective function and one black-box, i.e. simulation based and 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 multiobjective optimisation method determines the tradeoff between neotissue growth and operating cost. Our method exhibits better data efficiency than genetic algorithms, i.e. the most common approach in the literature, on both the tissue engineering example and standard test functions. The multiobjective optimisation method applies to real-world problems combining black-box models with easy-to-quantify objectives such as cost.
组织工程和再生医学着眼于改善或恢复人类和动物的生物组织功能。我们考虑在动态灌注生物反应器培养过程中，在骨组织工程的背景下，优化三维支架中的新组织生长。目标是选择设计变量，以优化两个相互冲突的目标，首先是最大化新组织生长，其次是最小化运营成本。我们对贝叶斯多目标优化进行了新颖的扩展，在一种分析目标函数和一种黑盒（即基于模拟和目标函数）的情况下。分析目标表示运营成本，而黑盒新组织生长目标来自于模拟偏微分方程组系统。所得到的多目标优化方法确定了新组织生长和运营成本之间的权衡。我们的方法在组织工程实例和标准测试函数上都比遗传算法（文献中最常见的方法）具有更好的数据效率。多目标优化方法适用于将黑盒模型与易于量化的目标（如成本）相结合的实际问题。
