#38 · Epigenomics

Quantitative models probe kinetics, memory capacity and microbial dynamics

The dominant thread is quantitative, model-based description of biological dynamics, spanning epigenetic state, microbial population kinetics and macromolecular binding. The methylation preprint applies Shannon information theory to estimate the capacity of the DNA methylome as a memory system and its mutual information with cell identity under biological constraints. A minimal stochastic model of one microbe consuming one resource, analyzed analytically and by Monte Carlo, recovers Monod growth kinetics. An extended Tonks-McGhee-von Hippel framework incorporating ligand crowding and binding-mode transitions reproduces SSB-ssDNA binding kinetics for HmtSSB and EcoSSB. Separately, MMTKPred uses machine learning to predict transporter Vmax and Km for metabolic modelling of yeasts. What is new is the explicit parameterization of these processes, with the methylation work framing epigenetic memory in information-theoretic terms.