Paper Info
Title
Self-loops Favour Diversification and Asymmetric Transitions Between Attractors in Boolean Network Models.
Abstract
The process of cell differentiation manifests properties such as non-uniform robustness and asymmetric transitions among cell types. In this paper we adopt Boolean networks to model cellular differentiation, where attractors (or set of attractors) in the network landscape epitomise cell types. Since changes in network topology and functions strongly impact attractor landscape characteristics, in this paper we study how self-loops influence diversified robustness and asymmetry of transitions. The purpose of this study is to identify the best configuration for a network owning these properties. Our results show that a moderate amount of self-loops make random Boolean networks more suitable to reproduce differentiation phenomena. This is a further evidence that self-loops play an important role in genetic regulatory networks.
Year
DOI
Venue
2018
10.1007/978-3-030-21733-4_3
Communications in Computer and Information Science
Field
DocType
Volume
Boolean network,Attractor,Topology,Computer science,A moderate amount,Robustness (computer science),Cellular differentiation,Network topology,Diversification (marketing strategy),Asymmetry
Conference
900
ISSN
Citations 
PageRank 
1865-0929
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Michele Braccini102.03
Sara Montagna234523.45
Andrea Roli3148691.09