Bayesian phylodynamics for developmental biology: incorporating age-dependence

As novel technologies for single-cell lineage tracing emerge, phylogenetic and phylodynamic tools are increasingly being used to study developmental processes. However, traditional phylodynamic methods, which were originally developed to study viral evolution, rely on assumptions that are difficult to justify in developmental contexts. Notably, due to cells dividing after characteristic generation times rather than after exponential waiting times -- as assumed by the traditionally used birth-death model -- empirical cell lineage trees deviate from birth-death phylogenies. Here, we present a non-trivial extension of the birth-death phylodynamic model that captures this characteristic feature of development. By applying our method to a public dataset of stem cell colonies, we show how previous estimates of the underlying population-dynamic parameters were biased by the choice of a birth-death tree prior. Beyond developmental biology, our framework provides an approach for analyzing systems where classical birth-death assumptions may be violated or where empirical tree shapes are poorly captured by those expected under standard phylodynamic models. Our method is available as a BEAST2 package.