基于单目标与多目标局部搜索的组合CAR T细胞回路设计
Combinatorial CAR T-cell Circuit Design using Single- and Multi-Objective Local Search
AI总结：
针对CAR T细胞回路设计的疗效与安全性权衡问题，提出单目标多起点局部搜索及多目标局部搜索方法，实验显示其性能优于SOTA方法，多目标方法在单目标约束问题上也能产生更优解。
AI中文摘要：
CAR T细胞回路设计可在靶向肿瘤细胞的同时不损伤健康细胞，是癌症治疗的关键。设计能最大化肿瘤细胞清除数量（即疗效）且满足大量健康细胞不受损伤这一安全性约束的CAR T细胞回路是一项极具挑战性的任务。针对该问题，我们提出了一种新的多起点局部搜索方法，在单细胞癌症图谱数据集上的实验显示，该方法的性能优于当前的SOTA方法。此外，我们还引入了一种多目标局部搜索方法，该方法能为治疗的疗效与安全性之间的权衡提供洞见。实验还表明，即便针对受约束的单目标优化问题，该多目标方法也能产生更优的解决方案。
英文摘要：
CAR T-cell circuit design enables targeting tumour cells while keeping healthy cells unaffected to a cancer treatment. Designing CAR T-cell circuits that maximize the number of tumour cells eliminated (called efficacy) while meeting the safety constraint of keeping a large number of healthy cells unaffected is a challenging task. We provide a new multistart local search approach for this problem and show that it outperforms the current SOTA approach on a single-cell cancer atlas. Furthermore, we introduce a multi-objective local search approach which provides insights into the trade-offs of efficacy and safety of the treatment. Our experiments also demonstrate that the multi-objective approach yields improved solutions even for the constrained single-objective optimisation problem.
