We perform a thorough analysis of RNA velocity methods, with a view towards
understanding the suitability of the various assumptions underlying popular …

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-
omic with clinical data is crucial to our understanding of disease pathogenesis and …

Single-cell RNA sequencing (scRNA-seq) technologies facilitate the characterization of
transcriptomic landscapes in diverse species, tissues, and cell types with unprecedented …

Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time
delays to effectively model large numbers of intermediate biochemical processes. Analysis …

In eukaryotes, the cell volume is observed to be strongly correlated with the nuclear volume.
The slope of this correlation depends on the cell type, growth condition, and the physical …

Here we present biVI, which combines the variational autoencoder framework of scVI with
biophysical models describing the transcription and splicing kinetics of RNA molecules. We …

The question of how cell-to-cell differences in transcription rate affect RNA count
distributions is fundamental for understanding biological processes underlying transcription …

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers
measured using smFISH (single molecule fluorescence in situ hybridization) with the …

Intracellular reaction rates depend on concentrations and hence their levels are often
regulated. However classical models of stochastic gene expression lack a cell size …

Many models of gene expression do not explicitly incorporate a cell cycle description. Here,
we derive a theory describing how messenger RNA (mRNA) fluctuations for constitutive and …