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A Dynamical Model Reveals Gene Co-Localizations in Nucleus

Overview of attention for article published in PLoS Computational Biology, July 2011
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Title
A Dynamical Model Reveals Gene Co-Localizations in Nucleus
Published in
PLoS Computational Biology, July 2011
DOI 10.1371/journal.pcbi.1002094
Pubmed ID
Authors

Jing Kang, Bing Xu, Ye Yao, Wei Lin, Conor Hennessy, Peter Fraser, Jianfeng Feng

Abstract

Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency- or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
United Kingdom 2 2%
Czechia 1 1%
Germany 1 1%
Russia 1 1%
France 1 1%
Unknown 88 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 31%
Researcher 22 23%
Professor 8 8%
Professor > Associate Professor 6 6%
Student > Bachelor 5 5%
Other 16 16%
Unknown 10 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 52%
Biochemistry, Genetics and Molecular Biology 13 13%
Computer Science 5 5%
Physics and Astronomy 4 4%
Mathematics 2 2%
Other 10 10%
Unknown 13 13%
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 06 March 2012.
All research outputs
#23,065,269
of 25,707,225 outputs
Outputs from PLoS Computational Biology
#8,637
of 9,024 outputs
Outputs of similar age
#119,195
of 128,455 outputs
Outputs of similar age from PLoS Computational Biology
#62
of 65 outputs
Altmetric has tracked 25,707,225 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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