Cell lineage statistics is a powerful tool for inferring for example the population growth rate, the fitness landscape, or various cellular parameters, such as division rate and death 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?
To address this issue, we develop a model-independent theoretical framework.
We show how to quantify fitness landscape, survivor bias and selection for arbitrary cell traits from cell lineage statistics in the presence of death, and we test this method using an experimental data set in which a cell population is exposed to a drug that kills a large fraction of the population. This analysis reveals that failing to properly account for dead lineages can lead to misleading fitness estimations. For simple trait dynamics, we prove and illustrate numerically that the fitness landscape and the survivor bias can in addition be used for the non-parametric estimation of the division and death rates, using only lineage histories.
Our framework also provides universal bounds on the population growth rate, and a fluctuation-response relation which quantifies the reduction of population growth rate due to the variability in death rate.
In there is any time left, I will talk about consequences of cell death in the context of cell size control, where we show that the survivor bias can sometimes conceal the adder property, namely the constant increment of volume between birth and division observed in many cells.