https://www.sciencedaily.com/releases/2022/02/220201115139.htm

Weissman, Qiu, and collaborators Jianhua Xing, professor of computational and systems biology at the University of Pittsburgh School of Medicine, and Xing lab graduate student Yan Zhang have built a machine learning framework that can define the mathematical equations describing a cell's trajectory from one state to another, such as its development from a stem cell into one of several different types of mature cell. The framework, called dynamo,can also be used to figure out the underlying mechanisms -- the specific cocktail of gene activity -- driving changes in the cell. Researchers could potentially use these insights to manipulate cells into taking one path instead of another, a common goal in biomedical research and regenerative medicine.

The researchers describe dynamo in a paper published in the journal Cell on February 1. They explain the framework's many analytical capabilities and use it to help understand mechanisms of human blood cell production, such as why one type of blood cell forms first (appears more rapidly than others).