Horizontal gene transfer mediated by bacteriophages is a critical mechanism for bacterial genome plasticity, among others, responsible for the diffusion of antibiotic resistance. We develop a stochastic Chemical Reaction Network (CRN) model capturing phage-mediated communication between engineered Sender and Receiver E. coli populations, where M13 bacteriophages transport genetic sequences,...
Modern experimental and imaging technologies provide high-resolution time-series measurements of biological systems, revealing dynamic behaviours that are difficult to analyze statistically. The underlying biological processes are typically noisy, partially observed, and high-dimensional, making Bayesian state-space models a natural framework for their analysis. However, exact Bayesian...
Gene expression models often treat the cell cycle as mere background, overlooking the transient gene-dosage shifts introduced by DNA replication. We ask how these dosage changes reshape the time it takes single cells to cross regulatory thresholds—a key currency for decision-making in transcriptional networks. Using a general stochastic framework that captures cell-to-cell variability without...
Inferring gene regulatory interactions from transcriptomic data is crucial for understanding gene networks. It is made difficult by technical noise, cell size and other confounding factors which introduce spurious correlations and inference errors. Focusing on regulation via coding and non-coding RNA interactions, we generalise correlation tests in the presence of technical noise to allow...
Biochemical processes inside cells are fundamentally stochastic. Inferring parameters of stochastic models describing these processes from collected single-cell data poses mathematical and computational problems. This minisymposium brings together international speakers who will present recent methodological advances on modeling and inference with different types of single-cell data. Juan...
