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Analysis of a dynamic model of guard cell signaling reveals the stability of signal propagation

Overview of attention for article published in BMC Systems Biology, August 2016
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Title
Analysis of a dynamic model of guard cell signaling reveals the stability of signal propagation
Published in
BMC Systems Biology, August 2016
DOI 10.1186/s12918-016-0327-7
Pubmed ID
Authors

Xiao Gan, Réka Albert

Abstract

Analyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. In this paper we identify the allowed long-term behaviors of a multi-level, 70-node dynamic model of the stomatal opening process in plants. We start by reducing the model's huge state space. We first reduce unregulated nodes and simple mediator nodes, then simplify the regulatory functions of selected nodes while keeping the model consistent with experimental observations. We perform attractor analysis on the resulting 32-node reduced model by two methods: 1. converting it into a Boolean model, then applying two attractor-finding algorithms; 2. theoretical analysis of the regulatory functions. We further demonstrate the robustness of signal propagation by showing that a large percentage of single-node knockouts does not affect the stomatal opening level. Combining both methods with analysis of perturbation scenarios, we conclude that all nodes except two in the reduced model have a single attractor; and only two nodes can admit oscillations. The multistability or oscillations of these four nodes do not affect the stomatal opening level in any situation. This conclusion applies to the original model as well in all the biologically meaningful cases. In addition, the stomatal opening level is resilient against single-node knockouts. Thus, we conclude that the complex structure of this signal transduction network provides multiple information propagation pathways while not allowing extensive multistability or oscillations, resulting in robust signal propagation. Our innovative combination of methods offers a promising way to analyze multi-level models.

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

Geographical breakdown

Country Count As %
France 1 5%
Argentina 1 5%
Unknown 19 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 33%
Researcher 5 24%
Student > Bachelor 3 14%
Student > Master 2 10%
Student > Doctoral Student 2 10%
Other 0 0%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 29%
Mathematics 3 14%
Biochemistry, Genetics and Molecular Biology 2 10%
Medicine and Dentistry 2 10%
Physics and Astronomy 2 10%
Other 4 19%
Unknown 2 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 22 August 2016.
All research outputs
#6,267,904
of 8,261,086 outputs
Outputs from BMC Systems Biology
#621
of 850 outputs
Outputs of similar age
#180,599
of 253,845 outputs
Outputs of similar age from BMC Systems Biology
#27
of 40 outputs
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