# Phylodynamics of Somatic Evolution: A Likelihood-Based Approach for Cellular Reproduction

## TL;DR

This paper introduces a new method to study how cells evolve by linking mutations to cell divisions, improving analysis of single-cell data.

## Contribution

A novel likelihood-based framework that models mutations tied to cell divisions, enabling efficient analysis of single-cell phylogenies.

## Key findings

- The framework uses a compound Poisson process to model mutations tied to cell divisions.
- A dynamic programming algorithm efficiently computes likelihoods over phylogenetic trees with mutations.
- The method was validated on simulated data and hematopoietic stem cell phylogenies.

## Abstract

Understanding the evolutionary dynamics of cell populations requires models that link observed phylogenetic patterns to the underlying processes of cell division, death, and mutation. Classical phylodynamic inference methods—developed primarily for macroevolutionary settings—assume that mutations accrue in calendar time and often rely on a molecular clock. Here, we introduce a framework that ties mutations to discrete birth (division) events. In this setting, mutations accumulate via a compound Poisson process, capturing both visible and hidden cell divisions within the reconstructed phylogenetic tree. We present a computationally efficient dynamic programming algorithm to compute the likelihood based on tree topologies with associated mutations, integrating over latent variables such as branch durations and unobserved cell divisions. Our method is applicable to large-scale single-cell datasets, and we demonstrate its utility on simulated data and on single-cell phylogenies of hematopoietic stem cells.