Evidence map›Paper›PMID 22475952›Full record

ArticlePhysical biology2012

Gain control in molecular information processing: lessons from neuroscience.

Ilya Nemenman

Abstract read
In one paragraph

Article in Physical biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

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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

1 author.

Ilya NemenmanDepartments of Physics and Biology, Computational and Life Sciences Initiative, Emory University, Atlanta, GA 30322, USA. ilya.nemenman@emory.edu

Funding

Differential metabolic network analysis of tumor progressionR01CA132629 · NCI · UNIVERSITY OF NEW MEXICO · PI FREYER, JAMES P · 2007 to 2011
$1.9M
NCI NIH HHS 7R01CA132629-04NCI NIH HHS R01 CA132629
6 · The paper itself

Abstract

Statistical properties of environments experienced by biological signaling systems in the real world change, which necessitates adaptive responses to achieve high fidelity information transmission. One form of such adaptive response is gain control. Here, we argue that a certain simple mechanism of gain control, understood well in the context of systems neuroscience, also works for molecular signaling. The mechanism allows us to transmit more than 1 bit (on or off) of information about the signal independent of the signal variance. It does not require additional molecular circuitry beyond that already present in many molecular systems, and in particular, it does not depend on existence of feedback loops. The mechanism provides a potential explanation for abundance of ultrasensitive response curves in biological regulatory networks.

Indexed as

Models, BiologicalNeurobiologySignal TransductionAnimalsDipteraFeedbackNeuronsSystems Biology

Identifiers

PMID22475952
PMCPMC3355474

What OpenQuestion holds

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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.