Editorial: Inference of Biological Networks

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

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Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190311125256 ◽

2019 ◽

Vol 19 (6) ◽

pp. 413-425 ◽

Cited By ~ 3

Author(s):

Athanasios Alexiou ◽

Stylianos Chatzichronis ◽

Asma Perveen ◽

Abdul Hafeez ◽

Ghulam Md. Ashraf

Keyword(s):

Protein Interactions ◽

Biological Networks ◽

Regulatory Networks ◽

Review Paper ◽

Boolean Networks ◽

Diagnostic Tools ◽

Complex Nature ◽

Cellular Interactions ◽

Protein Protein Interactions ◽

Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

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Networks

10.1093/oso/9780198805090.001.0001 ◽

2018 ◽

Cited By ~ 347

Author(s):

Mark Newman

Keyword(s):

Biological Networks ◽

Generative Models ◽

The Internet ◽

Network Data ◽

Computer Algorithms ◽

Graph Models ◽

Random Graph Models ◽

The Social ◽

Spectral Algorithms ◽

Wide Availability

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

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