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Identification of control targets in Boolean molecular network models via computational algebra

Overview of attention for article published in BMC Systems Biology, September 2016
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Title
Identification of control targets in Boolean molecular network models via computational algebra
Published in
BMC Systems Biology, September 2016
DOI 10.1186/s12918-016-0332-x
Pubmed ID
Authors

David Murrugarra, Alan Veliz-Cuba, Boris Aguilar, Reinhard Laubenbacher

Abstract

Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Hungary 1 2%
Mexico 1 2%
Unknown 39 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 28%
Researcher 8 19%
Professor 5 12%
Student > Master 5 12%
Student > Bachelor 3 7%
Other 7 16%
Unknown 3 7%
Readers by discipline Count As %
Mathematics 9 21%
Agricultural and Biological Sciences 8 19%
Biochemistry, Genetics and Molecular Biology 5 12%
Computer Science 4 9%
Engineering 4 9%
Other 8 19%
Unknown 5 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 04 October 2016.
All research outputs
#16,728,430
of 25,391,066 outputs
Outputs from BMC Systems Biology
#614
of 1,132 outputs
Outputs of similar age
#204,540
of 329,613 outputs
Outputs of similar age from BMC Systems Biology
#14
of 30 outputs
Altmetric has tracked 25,391,066 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% 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 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,613 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.