Choudhury Mahima, Deans Annika J, Candland Daniel R, Deans Tara L
Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA.
Curr Opin Biomed Eng. 2025 Jun;34. doi: 10.1016/j.cobme.2025.100580. Epub 2025 Feb 3.
Artificial intelligence provides an exciting avenue to improve approaches in cell therapies by learning and predicting dynamic gene expression patterns from large datasets of stem cell differentiation. The integration of synthetic biology provides genetic tools that mimic the spatial and temporal expression patterns during differentiation, enhancing the potential to significantly improve differentiation outcomes and further our understanding of the mechanisms involved during cell fate decisions.
人工智能提供了一条令人兴奋的途径,可通过从干细胞分化的大型数据集中学习和预测动态基因表达模式来改进细胞治疗方法。合成生物学的整合提供了能够模拟分化过程中空间和时间表达模式的遗传工具,增强了显著改善分化结果以及深化我们对细胞命运决定过程中所涉及机制理解的潜力。