scholarly journals Discrimination of singleton and periodic attractors in Boolean networks

Automatica ◽  
2017 ◽  
Vol 84 ◽  
pp. 205-213 ◽  
Author(s):  
Xiaoqing Cheng ◽  
Takeyuki Tamura ◽  
Wai-Ki Ching ◽  
Tatsuya Akutsu
Keyword(s):  
Boolean Networks ◽  
Periodic Attractors

Singleton and 2-periodic attractors of sign-definite Boolean networks

2012 ◽  
Vol 112 (1-2) ◽  
pp. 35-38 ◽  
Author(s):  
Tatsuya Akutsu ◽  
Avraham A. Melkman ◽  
Takeyuki Tamura
Keyword(s):  
Boolean Networks ◽  
Periodic Attractors

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

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
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.

Optimal output tracking of switched Boolean networks

2021 ◽  
Author(s):  
Yuzhi Chen ◽  
Pengfei Sun ◽  
Tao Sun ◽  
Madini O. Alassafi ◽  
Adil M. Ahmad
Keyword(s):  
Boolean Networks ◽  
Output Tracking ◽  
Optimal Output

Controllability of Boolean networks with intermittent disturbances

2020 ◽  
Vol 34 (28) ◽  
pp. 2050309
Author(s):  
Tao You ◽  
Hailun Zhang ◽  
Mingyu Yang ◽  
Xiao Wang ◽  
Yangming Guo
Keyword(s):  
Gene Expression ◽  
Boolean Network ◽  
Boolean Networks ◽  
Genetic Mutations ◽  
Intermittent Control ◽  
Mathematical Tool ◽  
Pulse Control ◽  
Expression Of Genes ◽  
Complicated Process ◽  
Living Being

In biological systems, gene expression is an important subject. In order to clarify the specific process of gene expression, mathematical tools are needed to simulate the process. The Boolean network (BN) is a good mathematical tool. In this paper, we study a Boolean network with intermittent perturbations. This is of great significance for studying genetic mutations in bioengineering. The expression of genes in the internal system of a living being is a very complicated process, and it is clear that the process is trans-ageal for humans. Through the intermittent control and pulse control of the BN, we can obtain the trajectory of gene expression better and faster, which will provide a very important theoretical basis for our next research.

scholarly journals Hybrid Boolean Networks as Physically Unclonable Functions

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Noeloikeau Charlot ◽  
Daniel Canaday ◽  
Andrew Pomerance ◽  
Daniel J. Gauthier
Keyword(s):  
Boolean Networks ◽  
Physically Unclonable Functions
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