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Constructing stochastic models from deterministic process equations by propensity adjustment

Overview of attention for article published in BMC Systems Biology, November 2011
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1 tweeter

Citations

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Readers on

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50 Mendeley
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Title
Constructing stochastic models from deterministic process equations by propensity adjustment
Published in
BMC Systems Biology, November 2011
DOI 10.1186/1752-0509-5-187
Pubmed ID
Authors

Jialiang Wu, Brani Vidakovic, Eberhard O Voit

Abstract

Gillespie's stochastic simulation algorithm (SSA) for chemical reactions admits three kinds of elementary processes, namely, mass action reactions of 0th, 1st or 2nd order. All other types of reaction processes, for instance those containing non-integer kinetic orders or following other types of kinetic laws, are assumed to be convertible to one of the three elementary kinds, so that SSA can validly be applied. However, the conversion to elementary reactions is often difficult, if not impossible. Within deterministic contexts, a strategy of model reduction is often used. Such a reduction simplifies the actual system of reactions by merging or approximating intermediate steps and omitting reactants such as transient complexes. It would be valuable to adopt a similar reduction strategy to stochastic modelling. Indeed, efforts have been devoted to manipulating the chemical master equation (CME) in order to achieve a proper propensity function for a reduced stochastic system. However, manipulations of CME are almost always complicated, and successes have been limited to relative simple cases.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 6%
United Kingdom 1 2%
China 1 2%
Italy 1 2%
Unknown 44 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 42%
Researcher 9 18%
Student > Master 4 8%
Professor > Associate Professor 3 6%
Other 2 4%
Other 7 14%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 22%
Engineering 8 16%
Computer Science 6 12%
Mathematics 5 10%
Biochemistry, Genetics and Molecular Biology 5 10%
Other 10 20%
Unknown 5 10%

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 08 November 2011.
All research outputs
#3,094,896
of 4,506,935 outputs
Outputs from BMC Systems Biology
#417
of 670 outputs
Outputs of similar age
#44,609
of 73,580 outputs
Outputs of similar age from BMC Systems Biology
#28
of 47 outputs
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So far Altmetric has tracked 670 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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