Evidence map›Paper›PMID 24676102›Full record

ArticlePLoS computational biology2014

Loads bias genetic and signaling switches in synthetic and natural systems.

Samanthe M Lyons, Wenlong Xu, June Medford, Ashok Prasad

Abstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. A Practical Step-by-Step Guide for Quantifying Retroactivity in Gene Networks.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  8. Article
  9. Article
  10. 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.

Samanthe M LyonsSchool of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, United States of America.
Wenlong XuChemical and Biological Engineering Department, Colorado State University, Fort Collins, Colorado, United States of America.
June MedfordDepartment of Biology, Colorado State University, Fort Collins, Colorado, United States of America.
Ashok PrasadSchool of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, United States of America; Chemical and Biological Engineering Department, Colorado State University, Fort Collins, Colorado, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms.

Indexed as

Gene Regulatory NetworksAlgorithmsAnimalsBacterial ProteinsComputer SimulationFeedback, PhysiologicalHumansModels, GeneticProbabilityras ProteinsSignal TransductionStochastic ProcessesSynthetic BiologyBacterial Proteinsras Proteins

Identifiers

PMID24676102
PMCPMC3967935

What OpenQuestion holds

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LicenceCC BY
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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.