Evidence map›Paper›PMID 31160574›Full record

ArticleNature communications2019

Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise.

Kun Xiong, Alex K Lancaster, Mark L Siegal, Joanna Masel

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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.

Kun XiongDepartment of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, 85721, USA.ORCID http://orcid.org/0000-0003-1431-6586
Alex K LancasterRonin Institute, Montclair, NJ, 07043, USA.ORCID http://orcid.org/0000-0002-0002-9263
Mark L SiegalCenter for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA.ORCID http://orcid.org/0000-0001-6930-2988
Joanna MaselDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA. masel@email.arizona.edu.ORCID http://orcid.org/0000-0002-7398-2127

Funding

Genetic and Nongenetic Variation in Complex TraitsR35GM118170 · NIGMS · NEW YORK UNIVERSITY · PI SIEGAL, MARK L · 2016 to 2021
$1.7M
Towards a general theory of evolutionary capacitanceR01GM076041 · NIGMS · UNIVERSITY OF ARIZONA · PI MASEL, JOANNA · 2006 to 2009
$565k
John Templeton Foundation (JTF) 39667NIGMS NIH HHS R35 GM118170U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R01GM076041U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM118170
6 · The paper itself

Abstract

In transcriptional regulatory networks (TRNs), a canonical 3-node feed-forward loop (FFL) is hypothesized to evolve to filter out short spurious signals. We test this adaptive hypothesis against a novel null evolutionary model. Our mutational model captures the intrinsically high prevalence of weak affinity transcription factor binding sites. We also capture stochasticity and delays in gene expression that distort external signals and intrinsically generate noise. Functional FFLs evolve readily under selection for the hypothesized function but not in negative controls. Interestingly, a 4-node "diamond" motif also emerges as a short spurious signal filter. The diamond uses expression dynamics rather than path length to provide fast and slow pathways. When there is no idealized external spurious signal to filter out, but only internally generated noise, only the diamond and not the FFL evolves. While our results support the adaptive hypothesis, we also show that non-adaptive factors, including the intrinsic expression dynamics, matter.

Indexed as

Adaptation, PhysiologicalFeedback, PhysiologicalGene Expression RegulationGene Regulatory NetworksModels, GeneticModels, TheoreticalRNA, MessengerSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsRNA, MessengerSaccharomyces cerevisiae Proteins

Identifiers

PMID31160574
PMCPMC6546794

What OpenQuestion holds

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LicenceCC BY
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.