Network Classification Based on Reducibility With Respect to the Stability of Canalizing Power of Genes in a Gene Regulatory Network - A Boolean Network Modeling Perspective

A key objective of studying biological systems is to design therapeutic intervention strategies for beneficially altering cell dynamics. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, it is critical to reduce the netwo...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 19; no. 1; pp. 558 - 568
Main Authors: Kim, Eunji, Ivanov, Ivan, Dougherty, Edward R.
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Boolean
Boolean algebra
Boolean functions
Canalizing power
Complexity theory
Computational modeling
Electric power distribution
External stimuli
gene regulatory network
Gene Regulatory Networks - genetics
Genes
Irrigation
Models, Genetic
network classification
network reduction
Networks
Perturbation
Perturbation methods
Robustness
Stability analysis
ISSN:1545-5963, 1557-9964, 1557-9964
Online Access:Get full text
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Summary:A key objective of studying biological systems is to design therapeutic intervention strategies for beneficially altering cell dynamics. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, it is critical to reduce the network complexity and the paper aims to address this issue by focusing on the distribution of the canalizing power (CP) of the genes in the model. Canalizing genes enforce broad corrective actions on cellular processes and play a crucial role in producing optimal reactions to external stimuli. Therefore, it is critical to reduce the network while preserving the canalizing power of genes. We reduce Boolean networks with perturbation by removing genes with the smallest canalizing power consecutively, and evaluate the stability of canalizing power. A systematic empirical study demonstrates that there are two classes of networks, reducible and irreducible with respect to the preservation of canalizing power of the genes. Based on these observations, we introduce the definition of reducible networks and proceed with the problem of selecting the relevant network features that allow for discriminating networks from the two different classes. We demonstrate the efficacy of the selected features on synthetic and real gene regulatory networks.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2020.3005313