Studying stochastic systems biology of the cell with single-cell genomics data

G Gorin, JJ Vastola, L Pachter - Cell Systems, 2023 - cell.com
Recent experimental developments in genome-wide RNA quantification hold considerable
promise for systems biology. However, rigorously probing the biology of living cells requires …

RNA velocity unraveled

G Gorin, M Fang, T Chari, L Pachter - PLOS Computational …, 2022 - journals.plos.org
We perform a thorough analysis of RNA velocity methods, with a view towards
understanding the suitability of the various assumptions underlying popular …

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

Q Jiang, X Fu, S Yan, R Li, W Du, Z Cao, F Qian… - Nature …, 2021 - nature.com
Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time
delays to effectively model large numbers of intermediate biochemical processes. Analysis …

Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data

M Carilli, G Gorin, Y Choi, T Chari, L Pachter - Nature Methods, 2024 - nature.com
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 …

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

S Persson, S Shashkova, L Österberg… - FEMS Yeast …, 2022 - academic.oup.com
Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in
cellular adaptation to changing environments. The SNF1 pathway regulates energy …

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments

G Gorin, JJ Vastola, M Fang, L Pachter - Nature Communications, 2022 - nature.com
The question of how cell-to-cell differences in transcription rate affect RNA count
distributions is fundamental for understanding biological processes underlying transcription …

Extrinsic noise and heavy-tailed laws in gene expression

L Ham, RD Brackston, MPH Stumpf - Physical review letters, 2020 - APS
Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we
derive analytical solutions to a set of models describing the molecular mechanisms …

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

S Luo, Z Wang, Z Zhang, T Zhou… - Nucleic Acids …, 2023 - academic.oup.com
Gene expression in mammalian cells is highly variable and episodic, resulting in a series of
discontinuous bursts of mRNAs. A challenge is to understand how static promoter …

Monte Carlo samplers for efficient network inference

Z Kilic, M Schweiger, C Moyer… - PLoS computational …, 2023 - journals.plos.org
Accessing information on an underlying network driving a biological process often involves
interrupting the process and collecting snapshot data. When snapshot data are stochastic …

Gene expression model inference from snapshot RNA data using Bayesian non-parametrics

Z Kilic, M Schweiger, C Moyer, D Shepherd… - Nature computational …, 2023 - nature.com
Gene expression models, which are key towards understanding cellular regulatory
response, underlie observations of single-cell transcriptional dynamics. Although RNA …