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Probabilistic strain optimization under constraint uncertainty

Overview of attention for article published in BMC Systems Biology, March 2013
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1 tweeter

Citations

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33 Mendeley
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Title
Probabilistic strain optimization under constraint uncertainty
Published in
BMC Systems Biology, March 2013
DOI 10.1186/1752-0509-7-29
Pubmed ID
Authors

Mona Yousofshahi, Michael Orshansky, Kyongbum Lee, Soha Hassoun

Abstract

An important step in strain optimization is to identify reactions whose activities should be modified to achieve the desired cellular objective. Preferably, these reactions are identified systematically, as the number of possible combinations of reaction modifications could be very large. Over the last several years, a number of computational methods have been described for identifying combinations of reaction modifications. However, none of these methods explicitly address uncertainties in implementing the reaction activity modifications. In this work, we model the uncertainties as probability distributions in the flux carrying capacities of reactions. Based on this model, we develop an optimization method that identifies reactions for flux capacity modifications to predict outcomes with high statistical likelihood.

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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 3%
Latvia 1 3%
Singapore 1 3%
Denmark 1 3%
Thailand 1 3%
Unknown 28 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 39%
Student > Ph. D. Student 5 15%
Professor 3 9%
Student > Master 3 9%
Professor > Associate Professor 3 9%
Other 3 9%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 45%
Biochemistry, Genetics and Molecular Biology 5 15%
Computer Science 3 9%
Engineering 2 6%
Chemical Engineering 1 3%
Other 1 3%
Unknown 6 18%

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 29 March 2013.
All research outputs
#8,872,669
of 11,126,160 outputs
Outputs from BMC Systems Biology
#700
of 964 outputs
Outputs of similar age
#88,736
of 129,131 outputs
Outputs of similar age from BMC Systems Biology
#16
of 25 outputs
Altmetric has tracked 11,126,160 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 964 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.