Dotson Gabrielle A, Ryan Charles W, Chen Can, Muir Lindsey, Rajapakse Indika
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Program in Cellular and Molecular Biology, University of Michigan, Ann Arbor, Michigan, USA.
Wiley Interdiscip Rev Syst Biol Med. 2020 Dec 2:e1515. doi: 10.1002/wsbm.1515.
Generating needed cell types using cellular reprogramming is a promising strategy for restoring tissue function in injury or disease. A common method for reprogramming is addition of one or more transcription factors that confer a new function or identity. Advancements in transcription factor selection and delivery have culminated in successful grafting of autologous reprogrammed cells, an early demonstration of their clinical utility. Though cellular reprogramming has been successful in a number of settings, identification of appropriate transcription factors for a particular transformation has been challenging. Computational methods enable more sophisticated prediction of relevant transcription factors for reprogramming by leveraging gene expression data of initial and target cell types, and are built on mathematical frameworks ranging from information theory to control theory. This review highlights the utility and impact of these mathematical frameworks in the field of cellular reprogramming. This article is categorized under: Reproductive System Diseases > Reproductive System Diseases>Genetics/Genomics/Epigenetics Reproductive System Diseases > Reproductive System Diseases>Stem Cells and Development Reproductive System Diseases > Reproductive System Diseases>Computational Models.
利用细胞重编程生成所需的细胞类型是恢复损伤或疾病组织功能的一种有前景的策略。重编程的一种常见方法是添加一种或多种赋予新功能或特性的转录因子。转录因子选择和递送方面的进展最终促成了自体重编程细胞的成功移植，这是其临床应用的早期例证。尽管细胞重编程在许多情况下都取得了成功，但为特定转化鉴定合适的转录因子一直具有挑战性。计算方法通过利用初始细胞类型和靶细胞类型的基因表达数据，能够更精确地预测用于重编程的相关转录因子，并且建立在从信息论到控制论等数学框架之上。本综述强调了这些数学框架在细胞重编程领域的实用性和影响。本文分类如下：生殖系统疾病>生殖系统疾病>遗传学/基因组学/表观遗传学；生殖系统疾病>生殖系统疾病>干细胞与发育；生殖系统疾病>生殖系统疾病>计算模型 。
