Gene expression is intrinsically stochastic, leading to substantial cell-to-cell variability in mRNA and protein levels, now routinely quantified with single-cell technologies. In this talk, I will discuss extensions of the classical two-state telegraph model to incorporate salient features of single-cell biology, including cell division, DNA replication, mRNA maturation, gene dosage...
Chimeric Antigen Receptor (CAR)-T cell therapy is revolutionising immunotherapy, and has shown significant efficacy in cancers of the blood. Experimental collaborators have observed that patterns of clone-agnostic and clone-specific resistance change sharply with immunologic pressure. We have leveraged mathematical modelling to determine which mechanisms can give rise to the experimentally...
Imperfect molecular detection in single-cell experiments introduces technical noise that can distort the observed dynamics of gene regulatory networks, hence complicating the inference of the true kinetic parameters from single-cell data. We extend binomial capture models from simple gene-expression systems to general, possibly time-dependent, regulatory networks, using both chemical master...
The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability...
We consider the Schrรถdinger bridge problem (SBP) which, given ensemble measurements of the initial and final configurations of a stochastic dynamical system and some prior knowledge on the dynamics, aims to reconstruct the โmost likelyโ evolution of the system compatible with the data. Notably, this point of view has spurred a lot of activity in the single cell analysis community over the past...
Inferring biophysical models of gene regulation from single-cell omics data remains a significant challenge because of the high dimensionality, stochasticity, and cross-sectional nature of the measurements. We aim to bridge this gap by combining flow-based generative modeling with the biophysics of transcriptional regulation to infer interpretable models of cell fate decision-making from...
In single-cell biology, stochastic reaction networks (SRNs) model molecular production, degradation, and interactions, and inferring their structure and parameters from data is central to understanding underlying biological mechanisms. Approximate Bayesian computation (ABC) offers a flexible Bayesian approach with posterior uncertainty quantification, but its reliance on extensive stochastic...
Modern single-cell technologies measure gene expression with unprecedented resolution, yet this data reflect multiple overlapping sources of variation. Separating technical noise from biologically meaningful fluctuations to reveal the underlying transcriptional dynamics remains a central challenge.
Mechanistic models offer a framework for proposing hypotheses about the molecular processes...
