The assessment of essential genes in the stability of PPI networks using critical node detection problem

Document Type : Original Article

Authors

1 Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

2 Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic)

3 Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran

Abstract

Essential genes and proteins as their products encode the basic functions of a cell in a variety of conditions and are vital for the survival of a cell. Analyzing the characteristics of these proteins provides important biological information. An interesting analysis is to demonstrate the correlation between topological importance of a protein in protein-protein interaction networks and its essentiality. Different centrality criteria such as degree, betweenness, closeness, and eigenvector centralities are used to investigate such a correlation. Despite the remarkable results obtained by these methods, it is shown that the centrality criteria in scale-free networks show a high level of correlations which indicate that they share similar topological information of the networks. In this paper, we use a different approach for analyzing this correlation and use a well-known problem in the field of graph theory, Critical Node Detection Problem and solve it on the protein-protein interaction networks to obtain a subset of proteins called critical nodes which have the most effect on the network stability. Our results show that essential proteins have a more prominent presence in the set of critical nodes than what expected at random samples. Furthermore, the essential proteins represented in the set of critical nodes have a different distribution of topological properties compared to the essential proteins recovered by the centrality-based methods.
All the source codes and data are available at “http://bioinformatics.aut.ac.ir/CNDP_PPI_networks/”.

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AUT Journal of Mathematics and Computing
Volume 3, Issue 1
February 2022
Pages 59-76

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