Evidence map›Paper›PMID 22107236›Full record

ArticlePhysical review letters2011

Effects of input noise on a simple biochemical switch.

Bo Hu, David A Kessler, Wouter-Jan Rappel, Herbert Levine

Abstract read
In one paragraph

Article in Physical review letters, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

8 citing papers in PubMed, 31 citations in OpenAlex.

  1. Volumetric compression develops noise-driven single-cell heterogeneity.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  2. Article
  3. How input noise limits biochemical sensing in ultrasensitive systems.Physical review. E, Statistical, nonlinear, and soft matter physics · 2014
    Article
  4. Article
  5. Article
  6. Article
  7. How input fluctuations reshape the dynamics of a biological switching system.Physical review. E, Statistical, nonlinear, and soft matter physics · 2012
    Article
  8. Article
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

4 authors at 3 institutions in 2 countries.

Bo HuCenter for Theoretical Biological Physics, University of California San Diego, La Jolla, California 92093-0319, USA.
David A Kessler
Wouter-Jan Rappel
Herbert Levine
Center for Theoretical Biological Physics · USBar-Ilan University · ILUniversity of California, San Diego · US

Funding

Project 5P01GM078586 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI RAPPEL, WOUTER-JAN · 2007 to 2016
$11.2M
NIGMS NIH HHS P01 GM078586
6 · The paper itself

Abstract

Many biological processes are controlled by biomolecular switches which themselves are regulated by various upstream chemical molecules (the input). Understanding how input noise affects the output stochastic switching process is of significant interest in various biophysical systems like gene regulation, chemosensing, and cell motility. Here, we propose an exactly solvable model where the noisy input signal arises from a simple birth-death process and directly regulates the transition rates of a downstream switch. We solve the joint master equations to analyze the statistical properties of the output switching process. Our results suggest that the conventional wisdom of an additive input-output noise rule fails to describe signaling systems containing a single molecular switch, and, instead, the most important effect of input noise is to effectively reduce the on rate of the switch.

Indexed as

BiochemistryModels, TheoreticalMarkov Chains

Identifiers

PMID22107236
PMCPMC4465233
OpenAlexW1990029052

What OpenQuestion holds

Textmetadata
LicenceTDM
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.