ReviewBiophysical journal2013
Analytic approaches to stochastic gene expression in multicellular systems.
Review in Biophysical journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Role of intercellular interactions on single cell and population level responses: considerations for multicellular bioreporter design.Frontiers in molecular biosciences · 2025Article
- Target gene responses differ when transcription factor levels are acutely decreased by nuclear export versus degradation.Development (Cambridge, England) · 2024Article
- A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021Article
- TATA and paused promoters active in differentiated tissues have distinct expression characteristics.Molecular systems biology · 2021Article
- Stochastic allelic expression as trigger for contractile imbalance in hypertrophic cardiomyopathy.Biophysical reviews · 2020Review
- Order Through Disorder: The Characteristic Variability of Systems.Frontiers in cell and developmental biology · 2020Review
- RNA synthesis is associated with multiple TBP-chromatin binding events.Scientific reports · 2017Article
- Probing the stochastic property of endoreduplication in cell size determination of Arabidopsis thaliana leaf epidermal tissue.PloS one · 2017Article
- Effects of cell-cycle-dependent expression on random fluctuations in protein levels.Royal Society open science · 2016Article
- Sex Bias and Maternal Contribution to Gene Expression Divergence in Drosophila Blastoderm Embryos.PLoS genetics · 2015Article
- The Independent Probabilistic Firing of Transcription Factors: A Paradigm for Clonal Variability in the Zebrafish Retina.Developmental cell · 2015Article
- Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms.Biophysical journal · 2015Article
- Shaped 3D singular spectrum analysis for quantifying gene expression, with application to the early zebrafish embryo.BioMed research international · 2015Article
- The embryo as a laboratory: quantifying transcription in Drosophila.Trends in genetics : TIG · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deterministic thermodynamic models of the complex systems, which control gene expression in metazoa, are helping researchers identify fundamental themes in the regulation of transcription. However, quantitative single cell studies are increasingly identifying regulatory mechanisms that control variability in expression. Such behaviors cannot be captured by deterministic models and are poorly suited to contemporary stochastic approaches that rely on continuum approximations, such as Langevin methods. Fortunately, theoretical advances in the modeling of transcription have assembled some general results that can be readily applied to systems being explored only through a deterministic approach. Here, I review some of the recent experimental evidence for the importance of genetically regulating stochastic effects during embryonic development and discuss key results from Markov theory that can be used to model this regulation. I then discuss several pairs of regulatory mechanisms recently investigated through a Markov approach. In each case, a deterministic treatment predicts no difference between the mechanisms, but the statistical treatment reveals the potential for substantially different distributions of transcriptional activity. In this light, features of gene regulation that seemed needlessly complex evolutionary baggage may be appreciated for their key contributions to reliability and precision of gene expression.
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.