The Impact of Self-loops on Boolean Networks Attractor Landscape and Implications for Cell Differentiation Modelling


Sara Montagna, Michele Braccini, Andrea Roli  

Boolean networks are a notable model of gene regulatory networks and, particularly, prominent theories discuss how they can capture cellular differentiation processes. One frequent motif in gene regulatory networks, especially in those circuits involved in cell differentiation, is autoregulation. In spite of this, the impact of autoregulation on Boolean network attractor landscape has not yet been extensively discussed in literature. In this paper we propose to model autoregulation as self-loops, and analyse how the number of attractors and their robustness may change once they are introduced in a well-known and widely used Boolean networks model, namely random Boolean networks. Results show that self-loops provide an evolutionary advantage in dynamic mechanisms of cells, by increasing both number and maximal robustness of attractors. These results provide evidence to the hypothesis that autoregulation is a straightforward functional component to consolidate cell dynamics, mainly in differentiation processes.

(keywords) Genetic regulatory networks model;Boolean networks;Self-Loops;Cell Differentiation

IEEE/ACM Transactions on Computational Biology and Bioinformatics, pages 1–12,  2020.
Pre-print

@ARTICLE{MBR-tcbb2020,
author = {Montagna, Sara and Braccini, Michele and Roli, Andrea},
journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
title={{The Impact of Self-loops on Boolean Networks Attractor Landscape and Implications for Cell Differentiation Modelling}},
year={2020},
volume={},
number={},
pages={1--12},
keywords={Genetic regulatory networks model;Boolean networks;Self-Loops;Cell Differentiation},
doi={10.1109/TCBB.2020.2968310},
ISSN={2374-0043},
month={},
note = {Pre-print}}

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The Impact of Self-loops on Boolean Networks Attractor Landscape and Implications for Cell Differentiation Modelling

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published  

— authors

Sara Montagna, Michele Braccini, Andrea Roli

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original page

— DOI

10.1109/TCBB.2020.2968310

— print ISSN

2374-0043

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— BibTeX ID
MBR-tcbb2020
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article

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