ArticlePLoS computational biology2016
What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- Macrophage response specificity to ligand mixtures is improved by signaling pathway antagonism.Molecular systems biology · 2026Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- Gradient matching accelerates mixed-effects inference for biochemical networks.Bioinformatics (Oxford, England) · 2025Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Exploring carbon source related localization and phosphorylation in the Snf1/Mig1 network using population and single cell-based approaches.Microbial cell (Graz, Austria) · 2024Article
- Article
- Optogenetic control of beta-carotene bioproduction in yeast across multiple lab-scales.Frontiers in bioengineering and biotechnology · 2023Article
- Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Resolving Crosstalk Between Signaling Pathways Using Mathematical Modeling and Time-Resolved Single Cell Data.Methods in molecular biology (Clifton, N.J.) · 2023Article
- CyberSco.Py an open-source software for event-based, conditional microscopy.Scientific reports · 2022Article
- Surface-enhanced Raman scattering: An emerging tool for sensing cellular function.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2022Review
- Scalable and flexible inference framework for stochastic dynamic single-cell models.PLoS computational biology · 2022Article
- Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level.PLoS computational biology · 2022Article
- One Cell at a Time: Advances in Single-Cell Methods and Instrumentation for Discovery in Aquatic Microbiology.Frontiers in microbiology · 2022Article
- Two-level modeling approach to identify the regulatory dynamics capturing drug response heterogeneity in single-cells.Scientific reports · 2021Article
- Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter.Journal of the Royal Society, Interface · 2021Article
- The circadian oscillator analysed at the single-transcript level.Molecular systems biology · 2021Article
- Modeling and characterization of inter-individual variability in CD8 T cell responses in mice.In silico biology · 2021Article
- Random Parametric Perturbations of Gene Regulatory Circuit Uncover State Transitions in Cell Cycle.iScience · 2020Article
- A systematic approach to decipher crosstalk in the p53 signaling pathway using single cell dynamics.PLoS computational biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression. We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships) is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation). Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.