ArticleScientific reports2019
Intrinsic and extrinsic noise of gene expression in lineage trees.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.ACS omega · 2026Article
- Modeling of gene regulatory networks: an annotated glossary.Trends in plant science · 2026Review
- Impact of variability in cell generation times on cell-to-cell variability of protein concentrations.bioRxiv : the preprint server for biology · 2026Article
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.bioRxiv : the preprint server for biology · 2026Article
- Article
- Cyclo-stationary distributions of mRNA and Protein counts for random cell division times.bioRxiv : the preprint server for biology · 2025Article
- Positioning of negative feedback loops within immune signaling pathways influences gene expression noise.bioRxiv : the preprint server for biology · 2025Article
- Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives.PLoS computational biology · 2025Article
- Stress-Induced Constraint on Expression Noise of Essential Genes in E. coli.Journal of molecular evolution · 2024Article
- Stable developmental patterns of gene expression without morphogen gradients.PLoS computational biology · 2024Article
- Translation regulation by RNA stem-loops can reduce gene expression noise.BMC bioinformatics · 2024Article
- Non-genetic heterogeneity and immune subtyping in breast cancer: Implications for immunotherapy and targeted therapeutics.Translational oncology · 2024Article
- Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives.bioRxiv : the preprint server for biology · 2024Article
- Feedback between stochastic gene networks and population dynamics enables cellular decision-making.Science advances · 2024Article
- Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise.Physical biology · 2023Article
- Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.Bioinformatics (Oxford, England) · 2023Article
- PyEcoLib: a python library for simulating stochastic cell size dynamics.Physical biology · 2023Article
- Inference on autoregulation in gene expression with variance-to-mean ratio.Journal of mathematical biology · 2023Article
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.BMC bioinformatics · 2022Article
- Single cell variability of CRISPR-Cas interference and adaptation.Molecular systems biology · 2022Article
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Abstract
Cell-to-cell heterogeneity is driven by stochasticity in intracellular reactions and the population dynamics. While these sources are usually studied separately, we develop an agent-based framework that accounts for both factors while tracking every single cell of a growing population. Apart from the common intrinsic variability, the framework also predicts extrinsic noise without the need to introduce fluctuating rate constants. Instead, extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age. We provide explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics in two-colour experiments. We find that these statistics differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) population snapshots with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots with unknown cell ages as measured from static images or flow cytometry. Applying the method to models of stochastic gene expression and feedback regulation elucidates that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.
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