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Neural Computing and Applications
Published in: Neural Computing and Applications 12/2020

02-03-2019 | Hybrid Artificial Intelligence and Machine Learning Technologies

Analysis of Boolean functions based on interaction graphs and their influence in system biology

Authors: Ranjeet Kumar Rout, Santi P. Maity, Pabitra Pal Choudhury, Jayanta Kumar Das, Sk. Sarif Hassan, Hari Mohan Pandey

Published in: Neural Computing and Applications | Issue 12/2020

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Abstract

Biological regulatory network can be modeled through a set of Boolean functions. These set of functions enable graph representation of the network structure, and hence, the dynamics of the network can be seen easily. In this article, the regulations of such network have been explored in terms of interaction graph. With the help of Boolean function decomposition, this work presents an approach for construction of interaction graphs. This decomposition technique is also used to reduce the network state space of the cell cycle network of fission yeast for finding the singleton attractors. Some special classes of Boolean functions with respect to the interaction graphs have been discussed. A unique recursive procedure is devised which uses the Cartesian product of sets starting from the set of one-variable Boolean function. Interaction graphs generated with these Boolean functions have only positive/negative edges, and the corresponding state spaces have periodic attractors with length one/two.
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Appendix
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Metadata
Title
Analysis of Boolean functions based on interaction graphs and their influence in system biology
Authors
Ranjeet Kumar Rout
Santi P. Maity
Pabitra Pal Choudhury
Jayanta Kumar Das
Sk. Sarif Hassan
Hari Mohan Pandey
Publication date
02-03-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 12/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04102-2

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