Cellular reprogramming: Mathematics meets medicine
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 02, 2020
- Source ID
- 10.1002/wsbm.1515
Entities
People
- Can Chen
- Charles W Ryan
- Gabrielle A Dotson
- Indika Rajapakse
- Lindsey Muir
Organizations
- Air Force Office of Scientific Research
- University of Michigan