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Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

Overview of attention for article published in BMC Bioinformatics, January 2017
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

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8 X users

Citations

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297 Dimensions

Readers on

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103 Mendeley
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1 CiteULike
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Title
Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy
Published in
BMC Bioinformatics, January 2017
DOI 10.1186/s12859-016-1452-4
Pubmed ID
Authors

David R. Penas, Patricia González, Jose A. Egea, Ramón Doallo, Julio R. Banga

Abstract

The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Russia 1 <1%
Unknown 101 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 27%
Researcher 20 19%
Student > Master 15 15%
Student > Doctoral Student 6 6%
Professor 5 5%
Other 13 13%
Unknown 16 16%
Readers by discipline Count As %
Engineering 16 16%
Agricultural and Biological Sciences 15 15%
Biochemistry, Genetics and Molecular Biology 13 13%
Computer Science 12 12%
Chemical Engineering 7 7%
Other 19 18%
Unknown 21 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 24 June 2019.
All research outputs
#6,213,112
of 23,511,526 outputs
Outputs from BMC Bioinformatics
#2,280
of 7,405 outputs
Outputs of similar age
#114,485
of 420,901 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 144 outputs
Altmetric has tracked 23,511,526 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,405 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 420,901 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 72% of its contemporaries.
We're also able to compare this research output to 144 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 74% of its contemporaries.