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With the advance of experimental techniques such as time-lapse fluorescencemicroscopy, the availability of single-cell trajectory data has vastlyincreased, and so has the demand for computational methods suitable forparameter inference with this type of data. However, most of the currentlyavailable methods treat single-cell trajectories independently, ignoring themother-daughter relationships and the information provided by populationstructure. This information is however essential if a process of interesthappens at cell division, or if it evolves slowly compared to the duration ofthe cell cycle. In this work, we highlight the importance of tracking celllineage trees and propose a Bayesian framework for parameter inference ontree-structured data. Our method relies on a combination of Sequential MonteCarlo for likelihood approximation and Markov Chain Monte Carlo for parametersampling. We demonstrate the capabilities of our inference framework on twosimple examples in which the lineage tree information is necessary: one inwhich the cell phenotype can only switch at cell division and another where thecell type fluctuates randomly over timescales that extend well beyond thelifetime of a single cell.

Parameter inference method for stochastic single-cell dynamics from tree-structured data