Capponi Sara, Daniels Kyle G
Department of Functional Genomics and Cellular Engineering, IBM Almaden Research Center, San Jose, California, USA.
Center for Cellular Construction, San Francisco, California, USA.
Immunol Rev. 2023 Nov;320(1):147-165. doi: 10.1111/imr.13236. Epub 2023 Jul 6.
Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.
细胞疗法是强大的技术，其中人类细胞被重新编程用于治疗应用，如杀死癌细胞或替换有缺陷的细胞。细胞疗法的基础技术在有效性和复杂性方面不断提高，使得细胞疗法的合理工程设计更加困难。创造下一代细胞疗法将需要改进的实验方法和预测模型。人工智能（AI）和机器学习（ML）方法已经彻底改变了生物学的几个领域，包括基因组注释、蛋白质结构预测和酶设计。在这篇综述中，我们讨论了将实验文库筛选与人工智能相结合，为模块化细胞治疗技术的开发建立预测模型的潜力。DNA合成和高通量筛选技术的进步使得模块化细胞治疗构建体文库的构建和筛选成为可能。基于这种筛选数据训练的人工智能和机器学习模型可以通过生成预测模型、设计规则和改进设计来加速细胞疗法的开发。
