ArticleBiophysical journal2013
Translational cross talk in gene networks.
Article in Biophysical journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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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.
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Who cites it
33 citing papers in PubMed.
- A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Predictive genetic circuit design for phenotype reprogramming in plants.Nature communications · 2025Article
- Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction.Mathematical biosciences · 2024Article
- Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophysical journal · 2024Review
- A mathematical framework for analysing particle flow in a network with multiple pools.Royal Society open science · 2024Article
- The effect of microRNA on protein variability and gene expression fidelity.Biophysical journal · 2023Article
- Bacterial degrons in synthetic circuits.Open biology · 2022Review
- Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware.ACS synthetic biology · 2021Article
- Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity.International microbiology : the official journal of the Spanish Society for Microbiology · 2021Article
- Prediction of Cellular Burden with Host-Circuit Models.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Whole cell biophysical modeling of codon-tRNA competition reveals novel insights related to translation dynamics.PLoS computational biology · 2020Article
- Heterogeneity coordinates bacterial multi-gene expression in single cells.PLoS computational biology · 2020Article
- Competing endogenous RNA crosstalk at system level.PLoS computational biology · 2019Article
- Modelling and measuring intracellular competition for finite resources during gene expression.Journal of the Royal Society, Interface · 2019Article
- Networks of ribosome flow models for modeling and analyzing intracellular traffic.Scientific reports · 2019Article
- Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology.Processes (Basel, Switzerland) · 2018Article
- Combining a Toggle Switch and a Repressilator within the AC-DC Circuit Generates Distinct Dynamical Behaviors.Cell systems · 2018Article
- Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources.PLoS computational biology · 2018Article
- Metabolic division of labor in microbial systems.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- mTOR referees memory and disease through mRNA repression and competition.FEBS letters · 2017Review
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4 authors.
Funding
Abstract
It has been shown experimentally that competition for limited translational resources by upstream mRNAs can lead to an anticorrelation between protein counts. Here, we investigate a stochastic model for this phenomenon, in which gene transcripts of different types compete for a finite pool of ribosomes. Throughout, we utilize concepts from the theory of multiclass queues to describe a qualitative shift in protein count statistics as the system transitions from being underloaded (ribosomes exceed transcripts in number) to being overloaded (transcripts exceed ribosomes in number). The exact analytical solution of a simplified stochastic model, in which the numbers of competing mRNAs and ribosomes are fixed, exhibits weak positive correlations between steady-state protein counts when total transcript count slightly exceeds ribosome count, whereas the solution can exhibit strong negative correlations when total transcript count significantly exceeds ribosome count. Extending this analysis, we find approximate but reasonably accurate solutions for a more realistic model, in which abundances of mRNAs and ribosomes are allowed to fluctuate randomly. Here, ribosomal fluctuations contribute positively and mRNA fluctuations contribute negatively to correlations, and when mRNA fluctuations dominate ribosomal fluctuations, a strong anticorrelation extremum reliably occurs near the transition from the underloaded to the overloaded regime.
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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.