New machine learning model describes dynamics of cell development
Tuesday, April 2, 2019 - 09:10
in Biology & Nature
From their birth through to their death, cells lead an eventful existence. Thanks to single-cell genomics, their destiny in large cell populations can now be analyzed. However, this method destroys the cell, which makes it difficult to draw conclusions about the dynamics of cell development. In order to address this problem, researchers at the Helmholtz Zentrum München and the University of Massachusetts use pseudodynamics, a mathematical model that estimates developmental processes from single-cell time series observations. Their report has been published in Nature Biotechnology.