#40 · Epigenomics

Stochastic simulation and inference of local rules driving population-level dynamics

The common thread is computational treatment of stochastic models in which local reaction rules generate population-level behavior. Three preprints approach this from complementary angles: a Python toolkit providing mean-field simulation and Bayesian parameter inference for k-mer motif dynamics in prebiotic RNA reactors; simulation and inference methods for non-Markovian stochastic reaction networks with state-dependent delays; and a comparison of SSA, exact CME, and mean-field CME for stochastic collaborative DNA methylation, centered on scaling costs and accuracy. The shared methodological emphasis is the trade-off between exact stochastic description and tractable approximation, together with inferring rule parameters from observed dynamics. What is new relative to prior framing is that the methylation work is organized around computational tradeoffs rather than biological mechanism, while the other two items focus on inference tooling and delay-generalized simulation algorithms.