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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks

Overview of attention for article published in BMC Systems Biology, August 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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4 X users

Citations

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22 Dimensions

Readers on

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27 Mendeley
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1 CiteULike
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Title
Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
Published in
BMC Systems Biology, August 2015
DOI 10.1186/s12918-015-0193-8
Pubmed ID
Authors

Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda Klipp

Abstract

Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely - and often arbitrarily - reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical reaction detail in a quantitative or semi quantitative manner. Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 26%
Student > Bachelor 6 22%
Researcher 6 22%
Professor 3 11%
Student > Postgraduate 2 7%
Other 2 7%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 30%
Agricultural and Biological Sciences 6 22%
Computer Science 5 19%
Medicine and Dentistry 2 7%
Mathematics 1 4%
Other 2 7%
Unknown 3 11%
Attention Score in Context

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 12 August 2015.
All research outputs
#13,082,030
of 22,821,814 outputs
Outputs from BMC Systems Biology
#448
of 1,142 outputs
Outputs of similar age
#118,989
of 264,425 outputs
Outputs of similar age from BMC Systems Biology
#12
of 31 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 60% 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 264,425 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 54% of its contemporaries.
We're also able to compare this research output to 31 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 61% of its contemporaries.