Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes

Abstract

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.

Ensemble Learning Based Gene Regulatory Network Inference

“…Moreover, the inferred GRNs, and particularly those generated by the Rank-full and the Z-score-full integration schemes, exhibit a disassortative topology, i.e., a negative degree-degree assortativity Pearson correlation. This is a characteristic that has been observed in several technological and biological networks [37], [38] and could be an important characteristic related to the network robustness to perturbations [37]. Considering both the network topology and the evaluation scores, Z-score-full or Rankfull seem good integration schemes to be applied in further inference projects.…”