Genetic Networks Encode Secrets of Their Past
Peter Crawford-Kahrl, Robert R. Nerem, Bree Cummins, and Tomas Gedeon

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…
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Taxonomy
TopicsBioinformatics and Genomic Networks · Gene Regulatory Network Analysis · Gene expression and cancer classification
