Asynchronous random Boolean network model based on elementary cellular automata rule 126

2005 ◽  
Vol 71 (2) ◽  
Author(s):  
Mihaela T. Matache ◽  
Jack Heidel
Keyword(s):  
Cellular Automata ◽  
Network Model ◽  
Boolean Network ◽  
Model Based ◽  
Elementary Cellular Automata ◽  
Boolean Network Model

ASYNCHRONOUS RANDOM BOOLEAN NETWORK MODEL WITH VARIABLE NUMBER OF PARENTS BASED ON ELEMENTARY CELLULAR AUTOMATA RULE 126

2006 ◽  
Vol 20 (08) ◽  
pp. 897-923 ◽  
Author(s):  
MIHAELA T. MATACHE
Keyword(s):  
Cellular Automata ◽  
Network Model ◽  
Boolean Network ◽  
Random Variable ◽  
Variable Number ◽  
Boolean Networks ◽  
Random Boolean Networks ◽  
Elementary Cellular Automata ◽  
Boolean Network Model ◽  
Point Analysis

A Boolean network with N nodes, each node's state at time t being determined by a certain number of parent nodes, which can vary from one node to another, is considered. This is a generalization of previous results obtained for a constant number of parent nodes, by Matache and Heidel in "Asynchronous Random Boolean Network Model Based on Elementary Cellular Automata Rule 126", Phys. Rev. E71, 026 232, 2005. The nodes, with randomly assigned neighborhoods, are updated based on various asynchronous schemes. The Boolean rule is a generalization of rule 126 of elementary cellular automata, and is assumed to be the same for all the nodes. We provide a model for the probability of finding a node in state 1 at a time t for the class of generalized asynchronous random Boolean networks (GARBN) in which a random number of nodes can be updated at each time point. We generate consecutive states of the network for both the real system and the models under the various schemes, and use simulation algorithms to show that the results match well. We use the model to study the dynamics of the system through sensitivity of the orbits to initial values, bifurcation diagrams, and fixed point analysis. We show that the GARBN's dynamics range from order to chaos, depending on the type of random variable generating the asynchrony and the parameter combinations.

scholarly journals An extended gene protein/products boolean network model including post-transcriptional regulation

2014 ◽  
Vol 11 (S1) ◽  
Author(s):  
Alfredo Benso ◽  
Stefano Di Carlo ◽  
Gianfranco Politano ◽  
Alessandro Savino ◽  
Alessandro Vasciaveo
Keyword(s):  
Transcriptional Regulation ◽  
Network Model ◽  
Boolean Network ◽  
Boolean Network Model ◽  
Post Transcriptional Regulation

Estimation of delays in generalized asynchronous Boolean networks

2016 ◽  
Vol 12 (10) ◽  
pp. 3098-3110 ◽  
Author(s):  
Haimabati Das ◽  
Ritwik Kumar Layek
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Network Model ◽  
Boolean Network ◽  
Equation Model ◽  
Boolean Networks ◽  
Temporal Behavior ◽  
Ordinary Differential Equation Model ◽  
Differential Equation Model ◽  
Boolean Network Model

The generalized asynchronous Boolean network model proposed in this paper can reliably mimic the temporal behavior of the Ordinary Differential Equation model without compromising the flexibility of the Boolean network model.

scholarly journals From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0128630 ◽  
Author(s):  
Bin Shao ◽  
Xiang Liu ◽  
Dongliang Zhang ◽  
Jiayi Wu ◽  
Qi Ouyang
Keyword(s):  
Network Model ◽  
Boolean Network ◽  
Continuous Model ◽  
Boolean Network Model
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