bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/717199

Multi-scale bursting in stochastic gene expression

Cao, Z.·Filatova, T.·Oyarzun, D.·Grima, R.
View original source ↗Export metadata ↓

Abstract

Transcriptional bursting is a major source of noise in gene expression. The telegraph model of gene expression, whereby transcription switches between "on" and "off" states, is the dominant model for bursting. Recently it was shown that the telegraph model cannot explain a number of experimental observations from perturbation data. Here we study an alternative model that is consistent with the data and which explicitly describes RNA polymerase recruitment and polymerase pause release, two steps necessary for mRNA production. We derive the exact steady-state distribution of mRNA numbers and an approximate steady-state distribution of protein numbers which are given by generalized hypergeometric functions. The theory is used to calculate the relative sensitivity of the coefficient of variation of mRNA fluctuations for thousands of genes in mouse fibroblasts. This indicates that the size of fluctuations is mostly sensitive to the rate of burst initiation and the mRNA degradation rate. Furthermore we show that (i) the time-dependent distribution of mRNA numbers is accurately approximated by a modified telegraph model with a Michaelis-Menten like dependence of the effective transcription rate on RNA polymerase abundance. (ii) the model predicts that if the polymerase recruitment rate is comparable or less than the pause release rate, then upon gene replication the mean number of RNA per cell remains approximately constant. This gene dosage compensation property has been experimentally observed and cannot be explained by the telegraph model with constant rates. Statement of SignificanceThe random nature of gene expression is well established experimentally. Mathematical modelling provides a means of understanding the factors leading to the observed stochasticity. There is evidence that the classical two-state model of stochastic mRNA dynamics (the telegraph model) cannot describe perturbation experiments and a new model that includes polymerase dynamics has been proposed. In this paper, we present the first detailed study of this model, deriving an exact solution for the mRNA distribution in steady-state conditions, an approximate time-dependent solution and showing the model can explain gene dosage compensation. As well, we use the theory together with transcriptomic data, to deduce which parameters when perturbed lead to a maximal change in the size of mRNA fluctuations.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cao, Z., Filatova, T., Oyarzun, D., Grima, R.. 2019-07-28. Multi-scale bursting in stochastic gene expression. https://doi.org/10.1101/717199

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

A three-factor model can explain symmetry breaking and the n-1 rule during X-chromosome inactivation

X-chromosome inactivation (XCI) is a mammalian gene regulatory process that via chromosome-scale transcriptional silencing, leaves active a single X chromosome per diploid genome complement. The mechanisms that enable cells to select the appropriate number of X chromosomes to inactivate while leaving others active remain unclear. We demonstrate that all known chromosomal counting scenarios during XCI can be accounted for by a three-factor mathematical model that includes (1) Xist, the long noncoding RNA (lncRNA) responsible for gene silencing during XCI in eutherians, (2) an X-linked transcriptional activator of Xist, and (3) the autosomally-encoded SPEN, an Xist RNA-binding protein that is essential for gene silencing during XCI. In our model, symmetry between active X chromosomes is broken by stochastic differences in the number of Xist molecules that associate with the X chromosomes at the onset of XCI. Subsequently, integrated positive and negative feedback loops amplify then propagate superstoichiometric assemblies of SPEN complexed with Xist on the inactive chromosome(s), and select for levels of X-linked activator that enable only one X chromosome per diploid genome complement to remain active. Thus, asymmetric propagation of the SPEN-Xist ribonucleoprotein complex may play an essential role in symmetry breaking and chromosomal counting during XCI, independently of or in concert with cis-acting networks of DNA regulatory elements. Our results also have conceptual implications for gene regulatory circuits on autosomes.

systems biology↗
bioRxiv2026

stem.MAP: Identification of cell-type-specific protein complexes in mammalian embryonic stem cells.

Protein complexes carry out critical functions during development. Mapping cell-type-specific protein complexes in early embryogenesis is essential for uncovering the molecular mechanisms of embryonic development. We unfortunately currently lack a catalog of protein complexes specific for embryogenesis including embryonic stem cells. Here we develop a proteomics-based machine learning workflow to predict protein complexes present in mouse embryonic stem cells (mESC). Our method integrates data from a cell-type agnostic protein complex resource with co-fractionation mass spectrometry (CF-MS) experiments on mESC. Our resource, stem.MAP, identifies 3,986 complexes encompassing ~133,000 unique interactions, and 8,616 proteins. stem.MAP captures variant complexes critical for stem cell function including the PRC2, BAF and NuRD complexes. stem.MAP also discriminates mutually exclusive interactions specific to embryonic stem cells (e.g. Eif4ebp2 - Eif4e). Finally we observe 1,388 high confidence stem.MAP interactions found only in mESC CF-MS data, many with support from AlphaFold modeling and a gastruloid coregulation network. We make stem.MAP protein complexes available through our web resource: stem.proteincomplexes.org. We anticipate stem.MAP will be beneficial to the study of early development and we expect our proteomics-based machine learning workflow to be applicable to the creation of any context-specific protein interaction network.

systems biology↗
bioRxiv2026

Coarse-graining genome-scale metabolic models for dynamic, multi-scale simulations of Pseudomonas aeruginosa in gel environments

Microbial communities are shaped by coupled interactions between microenvironment conditions and the intracellular adaptations that emerge in response external metabolic cues. For pathogenic microbes such as Pseudomonas aeruginosa, these interactions can rapidly generate heterogeneous sub-populations that exhibit adaptations for sustaining host infection and resisting antibiotic treatment. Technological strides in -omics data collection, combined with robust computational tools like genome-scale metabolic models (GEM) for predicting cellular metabolism, have yielded promising multi-scale depictions of microbial population development in simulated spatial environments. These multi-scale model simulations help establish and evaluate mechanistic contexts for how infection-enhancing behaviors might emerge in the population or be influenced by variable perturbations in the biological system. However, the number and rigorous tuning of parameters influencing the connections between intracellular-extracellular events pose barriers when shaping simulations to different in vitro systems of interest. In this work, we present a genome-scale, metabolism-informed continuum model (GEM-CM), a computational framework for coupling a reaction-diffusion model to a surrogate GEM representation that generates metabolic rate predictions informed by flux balance analysis outputs. A key feature of GEM-CM is its application of random forest regression, or ensembles, to coarse-grain the interface connecting the GEM and reaction-diffusion models down to a small set of input metabolites methodically chosen through the random forests evaluation of feature importance. To demonstrate GEM-CMs utility, we evaluated simulation predictions against P. aeruginosa PA14 populations radially propagating in low weight percentage agar gels. In addition to biomass growth, we simulated PA14 populations producing the cytotoxic secondary metabolite pyocyanin (PCN). GEM-CM could capture PA14 growth dynamics in agar gels when ensembles were provided with three carbon source inputs describing the simulated growth media. Bacterial motility parameters and the random forest ensemble allowed GEM-CM simulations to accurately predict spatial biomass distributions for PA14 populations over time and probe their PCN production dynamics. Ensembles also demonstrated robust performance for single gene knockout conditions with GEM-CM simulation accurately capturing the partial or total biomass and PCN inhibition observed in transposon mutant screenings. When evaluating transposon mutant population development, the GEM-CM framework additionally suggested that metH may be a promising target to inhibit the spatial propagation of PA14. This works proof-of-concept applications illustrated GEM-CM utility as an early stage multi-scale modeling tool for interrogating in vitro metabolic behavior in microbial populations.

systems biology↗