Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes
Author(s) -
Brandon Alexander,
Alexandra Pushkar,
Michelle Girvan
Publication year - 2021
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2020.0790
Subject(s) - assortativity , gene regulatory network , gene , selection (genetic algorithm) , percolation (cognitive psychology) , computational biology , biology , computer science , genetics , complex network , artificial intelligence , gene expression , neuroscience , world wide web
We study a simplified model of gene regulatory network evolution in which links (regulatory interactions) are added via various selection rules that are based on the structural and dynamical features of the network nodes (genes). Similar to well-studied models of ‘explosive’ percolation, in our approach, links are selectively added so as to delay the transition to large-scale damage propagation, i.e. to make the network robust to small perturbations of gene states. We find that when selection depends only on structure, evolved networks are resistant to widespread damage propagation, even without knowledge of individual gene propensities for becoming ‘damaged’. We also observe that networks evolved to avoid damage propagation tend towards disassortativity (i.e. directed links preferentially connect high degree ‘source’ genes to low degree ‘target’ genes and vice versa). We compare our simulations to reconstructed gene regulatory networks for several different species, with genes and links added over evolutionary time, and we find a similar bias towards disassortativity in the reconstructed networks.
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