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Biochemical fluctuations, optimisation and the linear noise approximation

Overview of attention for article published in BMC Systems Biology, July 2012
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1 X user

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Title
Biochemical fluctuations, optimisation and the linear noise approximation
Published in
BMC Systems Biology, July 2012
DOI 10.1186/1752-0509-6-86
Pubmed ID
Authors

Jürgen Pahle, Joseph D Challenger, Pedro Mendes, Alan J McKane

Abstract

Stochastic fluctuations in molecular numbers have been in many cases shown to be crucial for the understanding of biochemical systems. However, the systematic study of these fluctuations is severely hindered by the high computational demand of stochastic simulation algorithms. This is particularly problematic when, as is often the case, some or many model parameters are not well known. Here, we propose a solution to this problem, namely a combination of the linear noise approximation with optimisation methods. The linear noise approximation is used to efficiently estimate the covariances of particle numbers in the system. Combining it with optimisation methods in a closed-loop to find extrema of covariances within a possibly high-dimensional parameter space allows us to answer various questions. Examples are, what is the lowest amplitude of stochastic fluctuations possible within given parameter ranges? Or, which specific changes of parameter values lead to the increase of the correlation between certain chemical species? Unlike stochastic simulation methods, this has no requirement for small numbers of molecules and thus can be applied to cases where stochastic simulation is prohibitive.

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The data shown below were collected from the profile of 1 X user 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 5%
Poland 1 3%
Japan 1 3%
Unknown 35 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 33%
Researcher 13 33%
Lecturer 2 5%
Other 2 5%
Professor > Associate Professor 2 5%
Other 5 13%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 33%
Physics and Astronomy 8 21%
Biochemistry, Genetics and Molecular Biology 5 13%
Engineering 3 8%
Computer Science 3 8%
Other 4 10%
Unknown 3 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 September 2012.
All research outputs
#17,283,763
of 25,371,288 outputs
Outputs from BMC Systems Biology
#651
of 1,132 outputs
Outputs of similar age
#108,294
of 163,838 outputs
Outputs of similar age from BMC Systems Biology
#30
of 46 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.