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

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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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Keywords: BioAmbients calculus; Biological networks; COPASI software; Evolutionary algorithms genetic algorithms; Gene regulatory networks; Petri nets; Protein-protein interactions

Document Type: Review Article

Publication date: March 1, 2019

This article was made available online on March 25, 2019 as a Fast Track article with title: "Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions".

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