@article {Ru{\'e}010835, author = {Pau Ru{\'e} and Yung Hae Kim and Hjalte List Larsen and Anne Grapin-Botton and Alfonso Martinez Arias}, title = {A framework for the analysis of symmetric and asymmetric divisions in developmental processes}, elocation-id = {010835}, year = {2014}, doi = {10.1101/010835}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Animal tissue development relies on precise generation and deployment of specific cell types into tissue sub-structures. Understanding how this process is regulated remains a major challenge of biology. In many tissues, development progresses through a sequence of dividing progenitor cells, each with decreasing potency, that balance their growth and differentiation. Dividing progenitor cells thus face a decision on whether their offspring shall differentiate or self-renew. This results in three possible modes of division (symmetric self-renewing, symmetric differentiating, and asymmetric) all of which have been observed in developing animal tissues. In some instances, the frequencies of occurrence of these division modes are incompatible with the possibility that sibling cells take the decision to differentiate independently of each other. Rather, an excess of symmetric divisions, both proliferating and differentiating, is usually observed in so far no general mechanism by which this fate entanglement takes place has been put forward. Here we propose a simple model of progenitor priming that provides a rationale on how the fate of sibling cells might be linked. Analysis of the model suggests that commitment to the cycle completion of cells primed for differentiation might be the cause of the observed excess of symmetric divisions. The model presented is applicable to a broad range of developmental systems and provides a testing framework to explain the dynamics of cell division and differentiation are related.}, URL = {https://www.biorxiv.org/content/early/2014/10/28/010835}, eprint = {https://www.biorxiv.org/content/early/2014/10/28/010835.full.pdf}, journal = {bioRxiv} }