Abstract Tissue engineering is a fast progressing domain where solutions are provided for organ failure or tissue damage. In this domain, computer models can facilitate the design of optimal production process conditions leading to robust and economically viable products. In this study, we use a previously published computationally efficient model, describing the neotissue growth (cells + their extracellular matrix) inside 3D scaffolds in a perfusion bioreactor. In order to find the most cost-effective medium refreshment strategy for the bioreactor culture, a multi-objective optimization strategy was developed aimed at maximizing the neotissue growth while minimizing the total cost of the experiment. Four evolutionary optimization algorithms (NSGAII, MOPSO, MOEA/D and GDEIII) were applied to the problem and the Pareto frontier was computed in all methods. All algorithms led to a similar outcome, albeit with different convergence speeds. The simulation results indicated that, given the actual cost of the labor compared to the medium cost, the most cost-efficient way of refreshing the medium was obtained by minimizing the refreshment frequency and maximizing the refreshment amount.

中文翻译：

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在培养和劳动力成本方面优化灌注生物反应器内的新组织生长：使用进化算法的多目标优化研究

摘要 组织工程是一个快速发展的领域，为器官衰竭或组织损伤提供解决方案。在这个领域，计算机模型可以促进最佳生产工艺条件的设计，从而生产出稳健且经济可行的产品。在这项研究中，我们使用了先前发表的计算效率高的模型，描述了灌注生物反应器中 3D 支架内的新组织生长（细胞 + 其细胞外基质）。为了为生物反应器培养找到最具成本效益的培养基更新策略，开发了一种多目标优化策略，旨在最大化新组织生长，同时最小化实验总成本。四种进化优化算法（NSGAII、MOPSO、MOEA/D 和 GDEIII) 应用于该问题，并在所有方法中计算帕累托边界。尽管收敛速度不同，但所有算法都产生了相似的结果。仿真结果表明，在人工成本与介质成本相比的情况下，通过最小化刷新频率和最大化刷新量来获得最具成本效益的介质刷新方式。