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Analysis of gene network robustness based on saturated fixed point attractors

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, March 2014
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Title
Analysis of gene network robustness based on saturated fixed point attractors
Published in
EURASIP Journal on Bioinformatics & Systems Biology, March 2014
DOI 10.1186/1687-4153-2014-4
Pubmed ID
Authors

Genyuan Li, Herschel Rabitz

Abstract

The analysis of gene network robustness to noise and mutation is important for fundamental and practical reasons. Robustness refers to the stability of the equilibrium expression state of a gene network to variations of the initial expression state and network topology. Numerical simulation of these variations is commonly used for the assessment of robustness. Since there exists a great number of possible gene network topologies and initial states, even millions of simulations may be still too small to give reliable results. When the initial and equilibrium expression states are restricted to being saturated (i.e., their elements can only take values 1 or -1 corresponding to maximum activation and maximum repression of genes), an analytical gene network robustness assessment is possible. We present this analytical treatment based on determination of the saturated fixed point attractors for sigmoidal function models. The analysis can determine (a) for a given network, which and how many saturated equilibrium states exist and which and how many saturated initial states converge to each of these saturated equilibrium states and (b) for a given saturated equilibrium state or a given pair of saturated equilibrium and initial states, which and how many gene networks, referred to as viable, share this saturated equilibrium state or the pair of saturated equilibrium and initial states. We also show that the viable networks sharing a given saturated equilibrium state must follow certain patterns. These capabilities of the analytical treatment make it possible to properly define and accurately determine robustness to noise and mutation for gene networks. Previous network research conclusions drawn from performing millions of simulations follow directly from the results of our analytical treatment. Furthermore, the analytical results provide criteria for the identification of model validity and suggest modified models of gene network dynamics. The yeast cell-cycle network is used as an illustration of the practical application of this analytical treatment.

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Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 29%
Student > Ph. D. Student 1 14%
Other 1 14%
Student > Master 1 14%
Unknown 2 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 14%
Nursing and Health Professions 1 14%
Agricultural and Biological Sciences 1 14%
Computer Science 1 14%
Chemistry 1 14%
Other 0 0%
Unknown 2 29%
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 22 March 2014.
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#20,657,128
of 25,374,917 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#33
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Outputs of similar age
#174,732
of 237,006 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
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