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Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer

Overview of attention for article published in BMC Systems Biology, April 2017
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Title
Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer
Published in
BMC Systems Biology, April 2017
DOI 10.1186/s12918-017-0424-2
Pubmed ID
Authors

Yunseong Kim, Sea Choi, Dongkwan Shin, Kwang-Hyun Cho

Abstract

Cancer reversion, converting the phenotypes of a cancer cell into those of a normal cell, has been sporadically observed throughout history. However, no systematic analysis has been attempted so far. To investigate this from a systems biological perspective, we have constructed a logical network model of colorectal tumorigenesis by integrating key regulatory molecules and their interactions from previous experimental data. We identified molecular targets that can reverse cancerous cellular states to a normal state by systematically perturbing each molecular activity in the network and evaluating the resulting changes of the attractor landscape with respect to uncontrolled proliferation, EMT, and stemness. Intriguingly, many of the identified targets were well in accord with previous studies. We further revealed that the identified targets constitute stable network motifs that contribute to enhancing the robustness of attractors in cancerous cellular states against diverse regulatory signals. The proposed framework for systems analysis is applicable to the study of tumorigenesis and reversion of other types of cancer.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 19%
Student > Master 6 16%
Student > Ph. D. Student 4 11%
Professor 3 8%
Researcher 2 5%
Other 5 14%
Unknown 10 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 24%
Engineering 4 11%
Agricultural and Biological Sciences 3 8%
Computer Science 2 5%
Medicine and Dentistry 2 5%
Other 6 16%
Unknown 11 30%
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 07 October 2017.
All research outputs
#20,412,387
of 22,962,258 outputs
Outputs from BMC Systems Biology
#1,011
of 1,144 outputs
Outputs of similar age
#269,833
of 309,562 outputs
Outputs of similar age from BMC Systems Biology
#27
of 35 outputs
Altmetric has tracked 22,962,258 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.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.