Speaker
Description
Genetic mutations can lead to the formation of genetically distinct subpopulations of cells, called clones, which can vary in size over time. Due to the difficulty of obtaining meaningful longitudinal genetic data, one of the main challenges lies in the complexity of inferring the dynamics that govern the emergence and evolution of clones from snapshot data. In this context, DNA methylation can provide useful information: DNA methylation marks can oscillate back and forth between methylated and unmethylated states, leading to substantial differences in intercellular methylation patterns that can be used to identify clonal cell populations and infer their history \cite{Scherer2025, Hay2023, Fu2026, Larson2019, Utsey2020}.
We propose stochastic models of DNA methylation fluctuation and inheritance to study how methylation patterns evolve in clonal cell populations, considering mono- versus polyclonal evolution and competition dynamics. By developing simple and identifiable models, we reconstruct tissue evolution and identify which biological parameters drive methylation patterns indicative of clonal cell populations and how these differ from the methylation patterns that arise from well-mixed tissues. Finally, we compare the identifiability of discrete agent-based models, coarse-grained models, and continuum models to determine in which cases simple models accurately capture clonal tissue dynamics and in which cases more complex models need to, and can, be learned from data.
Bibliography
@article{Scherer2025,
title={Clonal tracing with somatic epimutations reveals dynamics of blood ageing},
author={Scherer, Michael and Singh, Indranil and Braun, Martina Maria and Szu-Tu, Chelsea and Sanchez Sanchez, Pedro and Lindenhofer, Dominik and Jakobsen, Niels Asger and K{\"o}rber, Verena and Kardorff, Michael and Nitsch, Lena and others},
journal={Nature},
volume={643},
number={8071},
pages={478--487},
year={2025},
publisher={Nature Publishing Group UK London}
}
@article{Hay2023,
title={Epigenetic inheritance is unfaithful at intermediately methylated CpG sites},
author={Hay, Amir D and Kessler, Noah J and Gebert, Daniel and Takahashi, Nozomi and Tavares, Hugo and Teixeira, Felipe K and Ferguson-Smith, Anne C},
journal={Nature Communications},
volume={14},
number={1},
pages={5336},
year={2023},
publisher={Nature Publishing Group UK London}
}
@article{Fu2026,
title={DNA methylation meets lineage tracing: History, recent progress, and future directions},
author={Fu, Ruijiang and Chen, Mengyang and Wang, Shou-Wen},
journal={Quantitative Biology},
volume={14},
number={1},
pages={e70017},
year={2026},
publisher={Wiley Online Library}
}
@article{Larson2019,
title={Data-driven selection and parameter estimation for DNA methylation mathematical models},
author={Larson, Karen and Zagkos, Loukas and Mc Auley, Mark and Roberts, Jason and Kavallaris, Nikos I and Matzavinos, Anastasios},
journal={Journal of Theoretical Biology},
volume={467},
pages={87--99},
year={2019},
publisher={Elsevier}
}
@article{Utsey2020,
title={A mathematical model for inheritance of DNA methylation patterns in somatic cells},
author={Utsey, Kiersten and Keener, James P},
journal={Bulletin of Mathematical Biology},
volume={82},
number={7},
pages={84},
year={2020},
publisher={Springer}
}
