Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for unknown or uncertain dynamics. When inferred from...
Single-cell technologies have unearthed vast heterogeneities in gene expression across cell populations. Understanding these cell-to-cell differences is essential for determining how DNA sequence specifies cellular function and drives phenotypic diversity. Recent advances in machine learning and AI have enabled the development of DNA sequence-to-expression prediction models. These models are...
Recent live-cell microscopy techniques allow the simultaneous tracking of distal genomic elements and transcription activity, offering new ways to study chromatin dynamics and gene regulation. However, drawing robust conclusions from such data is statistically challenging due to substantial technical noise, intrinsic fluctuations and limited time resolution. In this talk, I will present a...
Single-cell RNA sequencing (scRNA-seq) enables inference of cellular trajectories from snapshots of differentiating cells. However, in many cases the inferred "pseudotime" does not have a clear mechanistic interpretation. We developed a principled trajectory inference framework based on biophysical models that can estimate interpretable parameters including process time and transcriptional...
Quantifying transcriptional bursting from live-cell imaging data is critical for understanding stochastic gene regulation. Here, we present DART (Deep learning for the Analysis and Reconstruction of Transcriptional dynamics) \cite{m}, a deep learning framework that infers promoter-state trajectories from fluorescence intensity traces, enabling the estimation of activation and inactivation...
Gene expression is inherently stochastic, leading to fluctuations in protein levels that can influence cellular function. Negative autoregulatory feedback is a common regulatory motif that can suppress these fluctuations and stabilize gene expression, but its effects can depend strongly on additional cellular processes. We investigate how cell-cycle dynamics influence the noise-reduction...
Biological functions emerge from biomolecular interactions that are inherently stochastic, heterogeneous across cells, and only partially observable. This has motivated the development of stochastic models in mathematical biology that link mechanistic hypotheses to data and enable principled inference and prediction. In the context of gene expression, modern single-cell and live-cell...
