Genetic Networks Encode Secrets of Their Past

TL;DR

This paper models the evolution of genetic networks through vertex duplication and edge deletion, introducing the concept of ancestrally distinguished subgraphs to infer ancestral network features from current data.

Contribution

It presents a novel framework for analyzing genetic network evolution using ancestral subgraphs, distinguishing features arising from duplication versus other mechanisms.

Findings

Genetic networks lack large ancestrally distinguished subgraphs, supporting duplication-driven evolution.

The model accurately predicts ancestral network features from current genetic networks.

Tools developed enable analysis of ancestral networks from present-day data.

Abstract

Research shows that gene duplication followed by either repurposing or removal of duplicated genes is an important contributor to evolution of gene and protein interaction networks. We aim to identify which characteristics of a network can arise through this process, and which must have been produced in a different way. To model the network evolution, we postulate vertex duplication and edge deletion as evolutionary operations on graphs. Using the novel concept of an ancestrally distinguished subgraph, we show how features of present-day networks require certain features of their ancestors. In particular, ancestrally distinguished subgraphs cannot be introduced by vertex duplication. Additionally, if vertex duplication and edge deletion are the only evolutionary mechanisms, then a graph's ancestrally distinguished subgraphs must be contained in all of the graph's ancestors. We analyze two experimentally derived genetic networks and show that our results accurately predict lack of large ancestrally distinguished subgraphs, despite this feature being statistically improbable in associated random networks. This observation is consistent with the hypothesis that these networks evolved primarily via vertex duplication. The tools we provide open the door for analysing ancestral networks using current networks. Our results apply to edge-labeled (e.g. signed) graphs which are either undirected or directed.