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Automatic validation of computational models using pseudo-3D spatio-temporal model checking

Overview of attention for article published in BMC Systems Biology, December 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
8 tweeters
reddit
1 Redditor
video
1 video uploader

Citations

dimensions_citation
9 Dimensions

Readers on

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30 Mendeley
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1 CiteULike
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Title
Automatic validation of computational models using pseudo-3D spatio-temporal model checking
Published in
BMC Systems Biology, December 2014
DOI 10.1186/s12918-014-0124-0
Pubmed ID
Authors

Ovidiu Pârvu, David Gilbert

Abstract

BackgroundComputational models play an increasingly important role in systems biology for generating predictions and in synthetic biology as executable prototypes/designs. For real life (clinical) applications there is a need to scale up and build more complex spatio-temporal multiscale models; these could enable investigating how changes at small scales reflect at large scales and viceversa. Results generated by computational models can be applied to real life applications only if the models have been validated first. Traditional in silico model checking techniques only capture how non-dimensional properties (e.g. concentrations) evolve over time and are suitable for small scale systems (e.g. metabolic pathways). The validation of larger scale systems (e.g. multicellular populations) additionally requires capturing how spatial patterns and their properties change over time, which are not considered by traditional non-spatial approaches.ResultsWe developed and implemented a methodology for the automatic validation of computational models with respect to both their spatial and temporal properties. Stochastic biological systems are represented by abstract models which assume a linear structure of time and a pseudo-3D representation of space (2D space plus a density measure). Time series data generated by such models is provided as input to parameterised image processing modules which automatically detect and analyse spatial patterns (e.g. cell) and clusters of such patterns (e.g. cellular population). For capturing how spatial and numeric properties change over time the Probabilistic Bounded Linear Spatial Temporal Logic is introduced. Given a collection of time series data and a formal spatio-temporal specification the model checker Mudi (http://mudi.modelchecking.org) determines probabilistically if the formal specification holds for the computational model or not. Mudi is an approximate probabilistic model checking platform which enables users to choose between frequentist and Bayesian, estimate and statistical hypothesis testing based validation approaches. We illustrate the expressivity and efficiency of our approach based on two biological case studies namely phase variation patterning in bacterial colony growth and the chemotactic aggregation of cells.ConclusionsThe formal methodology implemented in Mudi enables the validation of computational models against spatio-temporal logic properties and is a precursor to the development and validation of more complex multidimensional and multiscale models.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 3%
Brazil 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 27%
Student > Master 5 17%
Student > Bachelor 4 13%
Professor > Associate Professor 3 10%
Student > Ph. D. Student 2 7%
Other 4 13%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 33%
Computer Science 8 27%
Engineering 3 10%
Biochemistry, Genetics and Molecular Biology 1 3%
Environmental Science 1 3%
Other 2 7%
Unknown 5 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 January 2015.
All research outputs
#4,712,437
of 16,266,822 outputs
Outputs from BMC Systems Biology
#241
of 1,107 outputs
Outputs of similar age
#80,460
of 309,204 outputs
Outputs of similar age from BMC Systems Biology
#12
of 73 outputs
Altmetric has tracked 16,266,822 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,107 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 77% 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 309,204 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 73% of its contemporaries.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.