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Manatee invariants reveal functional pathways in signaling networks

Overview of attention for article published in BMC Systems Biology, July 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

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7 Dimensions

Readers on

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14 Mendeley
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Title
Manatee invariants reveal functional pathways in signaling networks
Published in
BMC Systems Biology, July 2017
DOI 10.1186/s12918-017-0448-7
Pubmed ID
Authors

Leonie Amstein, Jörg Ackermann, Jennifer Scheidel, Simone Fulda, Ivan Dikic, Ina Koch

Abstract

Signal transduction pathways are important cellular processes to maintain the cell's integrity. Their imbalance can cause severe pathologies. As signal transduction pathways feature complex regulations, they form intertwined networks. Mathematical models aim to capture their regulatory logic and allow an unbiased analysis of robustness and vulnerability of the signaling network. Pathway detection is yet a challenge for the analysis of signaling networks in the field of systems biology. A rigorous mathematical formalism is lacking to identify all possible signal flows in a network model. In this paper, we introduce the concept of Manatee invariants for the analysis of signal transduction networks. We present an algorithm for the characterization of the combinatorial diversity of signal flows, e.g., from signal reception to cellular response. We demonstrate the concept for a small model of the TNFR1-mediated NF- κB signaling pathway. Manatee invariants reveal all possible signal flows in the network. Further, we show the application of Manatee invariants for in silico knockout experiments. Here, we illustrate the biological relevance of the concept. The proposed mathematical framework reveals the entire variety of signal flows in models of signaling systems, including cyclic regulations. Thereby, Manatee invariants allow for the analysis of robustness and vulnerability of signaling networks. The application to further analyses such as for in silico knockout was shown. The new framework of Manatee invariants contributes to an advanced examination of signaling systems.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 21%
Student > Ph. D. Student 3 21%
Researcher 3 21%
Student > Master 2 14%
Professor 1 7%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 29%
Computer Science 2 14%
Medicine and Dentistry 2 14%
Agricultural and Biological Sciences 1 7%
Veterinary Science and Veterinary Medicine 1 7%
Other 2 14%
Unknown 2 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 March 2020.
All research outputs
#5,607,547
of 17,358,590 outputs
Outputs from BMC Systems Biology
#295
of 1,115 outputs
Outputs of similar age
#122,606
of 329,272 outputs
Outputs of similar age from BMC Systems Biology
#29
of 87 outputs
Altmetric has tracked 17,358,590 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,115 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,272 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 87 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.