Epigenetic Clocks: Modelling Biomarkers Measuring and Predicting Biological Age

There is a broad consensus on nine hallmarks of human aging, such as DNA damage and telomere shortening. These were believed to be related but weakly connected symptoms. However, recent studies suggest that epigenetic changes are upstream drivers of these hallmarks. The best biomarker to track such changes is epigenetic clocks that detect shifts in DNA methylation levels.

Yet, there is little understanding of what causes these shifts and how to revert them if deviating too much.

This gap makes epigenetic clocks lagging indicators of abnormal aging or disease states that prevent researchers from using them to predict how the biological age may accelerate from current conditions or respond to drugs. This proposal is about unraveling the mechanics of epigenetic clocks to allow such predictions.

We will develop a stochastic model for DNA methylation resting on sound physical principles.

Using novel datasets from the participating researchers, we will fit the model´s parameters using Bayesian inference and explore cases with Monte Carlo simulations that are hard to access in experiments.

Together, we have the infrastructure to analyze datasets from patients with aging and disease states, make predictions, and, if needed, do new experiments.

This proposal puts us in a favorable position to develop epigenetic clocks as a novel biomarker for age and advance our understanding of the molecular drivers causing aging.