Evidence map›Paper›PMID 36862689›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Competing constraints shape the nonequilibrium limits of cellular decision-making.

Nicholas C Lammers, Avi I Flamholz, Hernan G Garcia

Open access · hybridFull text read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Invariant nonequilibrium dynamics in gene regulation optimize information flow.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  3. Article
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  11. Chromatin enables precise and scalable gene regulation with factors of limited specificity.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  12. Flexibility and sensitivity in gene regulation out of equilibrium.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  13. Article
  14. Energy Aware Technology Mapping of Genetic Logic Circuits.bioRxiv : the preprint server for biology · 2024
    Article
  15. Article
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Nicholas C LammersBiophysics Graduate Group, University of California, Berkeley, CA 904720.ORCID 0000-0001-6832-6152
Avi I FlamholzDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125.ORCID 0000-0002-9278-5479
Hernan G GarciaBiophysics Graduate Group, University of California, Berkeley, CA 904720.
California Institute of Technology · USQB3 · USUniversity of California, Berkeley · US

Funding

Lighting Up the Central Dogma in Embryonic DevelopmentDP2HD094655 · NICHD · UNIVERSITY OF CALIFORNIA BERKELEY · PI GARCIA, HERNAN GUSTAVO · 2017 to 2017
$2.4M
Predictive understanding of the temporal control of transcription in Drosophila developmentR01GM139913 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI GARCIA, HERNAN GUSTAVO · 2021 to 2024
$1.2M
DOD | Defense Advanced Research Projects Agency (DARPA) 66001-20-2-4033Gordon and Betty Moore Foundation (GBMF) 2919.02HHS | National Institutes of Health (NIH) 5T32HG000047-1HHS | National Institutes of Health (NIH) DP2 OD024541National Science Foundation (NSF) 1652236National Science Foundation (NSF) PHY-1748958NICHD NIH HHS DP2 HD094655NIGMS NIH HHS R01 GM139913
6 · The paper itself

Abstract

Gene regulation is central to cellular function. Yet, despite decades of work, we lack quantitative models that can predict how transcriptional control emerges from molecular interactions at the gene locus. Thermodynamic models of transcription, which assume that gene circuits operate at equilibrium, have previously been employed with considerable success in the context of bacterial systems. However, the presence of ATP-dependent processes within the eukaryotic transcriptional cycle suggests that equilibrium models may be insufficient to capture how eukaryotic gene circuits sense and respond to input transcription factor concentrations. Here, we employ simple kinetic models of transcription to investigate how energy dissipation within the transcriptional cycle impacts the rate at which genes transmit information and drive cellular decisions. We find that biologically plausible levels of energy input can lead to significant gains in how rapidly gene loci transmit information but discover that the regulatory mechanisms underlying these gains change depending on the level of interference from noncognate activator binding. When interference is low, information is maximized by harnessing energy to push the sensitivity of the transcriptional response to input transcription factors beyond its equilibrium limits. Conversely, when interference is high, conditions favor genes that harness energy to increase transcriptional specificity by proofreading activator identity. Our analysis further reveals that equilibrium gene regulatory mechanisms break down as transcriptional interference increases, suggesting that energy dissipation may be indispensable in systems where noncognate factor interference is sufficiently large.

Indexed as

EukaryotaEukaryotic CellsGene Regulatory NetworksKineticsThermodynamicsTranscription FactorsTranscription Factorscell signalingdecision theorygene regulationnonequilibriumtranscriptional dynamics

Identifiers

PMID36862689
PMCPMC10013869
OpenAlexW4322757207

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read41
Read underepoch 390

Registered trials

None linked

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.