Evidence map›Paper›PMID 31149348›Full record

ArticleNPJ systems biology and applications2019

Mapping connections in signaling networks with ambiguous modularity.

Daniel Lill, Oleksii S Rukhlenko, Anthony James Mc Elwee, Eugene Kashdan, Jens Timmer, Boris N Kholodenko

Abstract read
In one paragraph

Article in NPJ systems biology and applications, 2019. 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
–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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Network inference from perturbation time course data.NPJ systems biology and applications · 2022
    Article
  7. 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

6 authors.

Daniel Lill *1Institute of Physics, University of Freiburg, Freiburg, Germany.
Oleksii S Rukhlenko *2Systems Biology Ireland, University College Dublin, Dublin, Ireland.ORCID 0000-0003-1863-4987
Anthony James Mc Elwee2Systems Biology Ireland, University College Dublin, Dublin, Ireland.
Eugene Kashdan2Systems Biology Ireland, University College Dublin, Dublin, Ireland.
Jens Timmer1Institute of Physics, University of Freiburg, Freiburg, Germany.
Boris N Kholodenko2Systems Biology Ireland, University College Dublin, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modular Response Analysis (MRA) is a suite of methods that under certain assumptions permits the precise reconstruction of both the directions and strengths of connections between network modules from network responses to perturbations. Standard MRA assumes that modules are insulated, thereby neglecting the existence of inter-modular protein complexes. Such complexes sequester proteins from different modules and propagate perturbations to the protein abundance of a downstream module retroactively to an upstream module. MRA-based network reconstruction detects retroactive, sequestration-induced connections when an enzyme from one module is substantially sequestered by its substrate that belongs to a different module. Moreover, inferred networks may surprisingly depend on the choice of protein abundances that are experimentally perturbed, and also some inferred connections might be false. Here, we extend MRA by introducing a combined computational and experimental approach, which allows for a computational restoration of modular insulation, unmistakable network reconstruction and discrimination between solely regulatory and sequestration-induced connections for a range of signaling pathways. Although not universal, our approach extends MRA methods to signaling networks with retroactive interactions between modules arising from enzyme sequestration effects.

Indexed as

Computational BiologyComputer SimulationGene Regulatory NetworksModels, BiologicalProtein Interaction MapsProteinsSignal TransductionProteinsApplied mathematicsBiochemical networksComputer modelling

Identifiers

PMID31149348
PMCPMC6533310

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LicenceCC BY
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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.