Speaker
Description
Cell lineage statistics is a powerful tool for inferring cellular parameters, such as division rate, death rate, or population growth rate. Yet, in practice such an analysis suffers from a basic problem: how should we treat incomplete lineages that do not survive until the end of the experiment? In this talk, I will introduce a model-independent theoretical framework to address this issue. This framework provides practical definitions of fitness landscape, survivor bias, and selection strength for arbitrary cell traits from cell lineage statistics in the presence of death. Analyses of experimental data where a cell population is exposed to a drug that kills a large fraction of the population reveals that failing to properly account for dead lineages can lead to misleading fitness estimations. For simple trait dynamics, the fitness landscape and the survivor bias can in addition be used for the nonparametric estimation of the division and death rates, using only lineage histories. Finally, this framework provides universal bounds on the population growth rate, and a fluctuation-response relation that quantifies the change in population growth rate due to the variability in death rate.
Bibliography
@article{genthon_cell_2023,
title = {Cell {Lineage} {Statistics} with {Incomplete} {Population} {Trees}},
volume = {1},
issn = {2835-8279},
url = {https://link.aps.org/doi/10.1103/PRXLife.1.013014},
doi = {10.1103/PRXLife.1.013014},
language = {en},
number = {1},
urldate = {2026-04-16},
journal = {PRX Life},
author = {Genthon, Arthur and Nozoe, Takashi and Peliti, Luca and Lacoste, David},
month = sep,
year = {2023},
pages = {013014},
}
