Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions
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.Journal Title:
Current Topics in Medicinal Chemistry