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Clustering reveals limits of parameter identifiability in multi-parameter models of biochemical dynamics

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

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

Mentioned by

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5 tweeters

Citations

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12 Dimensions

Readers on

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37 Mendeley
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1 CiteULike
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Title
Clustering reveals limits of parameter identifiability in multi-parameter models of biochemical dynamics
Published in
BMC Systems Biology, September 2015
DOI 10.1186/s12918-015-0205-8
Pubmed ID
Authors

Karol Nienałtowski, Michał Włodarczyk, Tomasz Lipniacki, Michał Komorowski

Abstract

Compared to engineering or physics problems, dynamical models in quantitative biology typically depend on a relatively large number of parameters. Progress in developing mathematics to manipulate such multi-parameter models and so enable their efficient interplay with experiments has been slow. Existing solutions are significantly limited by model size. In order to simplify analysis of multi-parameter models a method for clustering of model parameters is proposed. It is based on a derived statistically meaningful measure of similarity between groups of parameters. The measure quantifies to what extend changes in values of some parameters can be compensated by changes in values of other parameters. The proposed methodology provides a natural mathematical language to precisely communicate and visualise effects resulting from compensatory changes in values of parameters. As a results, a relevant insight into identifiability analysis and experimental planning can be obtained. Analysis of NF- κB and MAPK pathway models shows that highly compensative parameters constitute clusters consistent with the network topology. The method applied to examine an exceptionally rich set of published experiments on the NF- κB dynamics reveals that the experiments jointly ensure identifiability of only 60 % of model parameters. The method indicates which further experiments should be performed in order to increase the number of identifiable parameters. We currently lack methods that simplify broadly understood analysis of multi-parameter models. The introduced tools depict mutually compensative effects between parameters to provide insight regarding role of individual parameters, identifiability and experimental design. The method can also find applications in related methodological areas of model simplification and parameters estimation.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Spain 1 3%
United Kingdom 1 3%
Switzerland 1 3%
Unknown 32 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 32%
Student > Ph. D. Student 11 30%
Professor > Associate Professor 2 5%
Professor 2 5%
Lecturer 1 3%
Other 4 11%
Unknown 5 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 19%
Engineering 5 14%
Agricultural and Biological Sciences 4 11%
Computer Science 2 5%
Nursing and Health Professions 1 3%
Other 7 19%
Unknown 11 30%

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 05 October 2015.
All research outputs
#5,221,541
of 16,400,568 outputs
Outputs from BMC Systems Biology
#287
of 1,106 outputs
Outputs of similar age
#80,580
of 252,342 outputs
Outputs of similar age from BMC Systems Biology
#1
of 3 outputs
Altmetric has tracked 16,400,568 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,106 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 68% 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 252,342 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 63% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them