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• W3113302967 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." @default.
• W3113302967 created "2020-12-21" @default.
• W3113302967 creator A5010948592 @default.
• W3113302967 creator A5030273254 @default.
• W3113302967 creator A5048203479 @default.
• W3113302967 creator A5055577517 @default.
• W3113302967 creator A5077461186 @default.
• W3113302967 date "2020-12-02" @default.
• W3113302967 modified "2023-10-16" @default.
• W3113302967 title "Cellular reprogramming: Mathematics meets medicine" @default.
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• W3113302967 doi "https://doi.org/10.1002/wsbm.1515" @default.
• W3113302967 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33289324" @default.

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