Gabrielle A Dotson, Charles W Ryan, Can Chen, Lindsey Muir, Indika Rajapakse
{"title":"Cellular reprogramming: Mathematics meets medicine.","authors":"Gabrielle A Dotson, Charles W Ryan, Can Chen, Lindsey Muir, Indika Rajapakse","doi":"10.1002/wsbm.1515","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49254,"journal":{"name":"Wiley Interdisciplinary Reviews-Systems Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":7.9000,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867497/pdf/nihms-1669173.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews-Systems Biology and Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/wsbm.1515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Journal Name:Wiley Interdisciplinary Reviews-Systems Biology and Medicine
Focus:
Strong interdisciplinary focus
Serves as an encyclopedic reference for systems biology research
Conceptual Framework:
Systems biology asserts the study of organisms as hierarchical systems or networks
Individual biological components interact in complex ways within these systems
Article Coverage:
Discusses biology, methods, and models
Spans systems from a few molecules to whole species
Topical Coverage:
Developmental Biology
Physiology
Biological Mechanisms
Models of Systems, Properties, and Processes
Laboratory Methods and Technologies
Translational, Genomic, and Systems Medicine