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Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes.

Alejandro Gil-Gomez¹, Joshua S Rest¹

¹Department of Ecology and Evolution, Laufer Center for Physical and Quantitative Biology, Stony Brook University, 650 Life Sciences, Stony Brook, NY 11794-4254, USA.

Molecular Biology and Evolution

|May 20, 2024

Summary

Drug-drug interactions (DDIs) rapidly change across species, offering a novel way to estimate molecular network evolution. Synergistic DDIs involve closely connected targets that evolve faster, providing insights into evolutionary rates.

Keywords:

biological network evolution drug–drug interactions gram-negative bacteria phylogenetic comparative methods

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Area of Science:

Evolutionary biology
Systems biology
Bioinformatics

Background:

Molecular networks, including protein-protein interaction (PPI) and metabolic networks, change as species diverge.
Studying network evolution across species is challenging due to limited data and model organism biases.
Drug-drug interactions (DDIs) can serve as a proxy for molecular network topology variation.

Purpose of the Study:

To propose and evaluate the rate of DDI change across species as an estimate for molecular network evolution.
To investigate the relationship between synergistic DDIs and the evolutionary rates of their molecular targets.
To explore the utility of drug combination (DC) data for large-scale network evolution studies.

Main Methods:

Computed evolutionary rates of DDIs using high-throughput data from gram-negative bacteria.
Applied phylogenetic comparative methods to analyze DDI divergence over evolutionary time.
Mapped drug targets in PPI and co-functional networks to analyze synergistic DDIs and their evolutionary rates.

Main Results:

DDIs exhibit rapid divergence over short evolutionary periods, with saturation over longer timescales.
Synergistic DDIs involve molecular targets that are evolutionarily closer in networks.
Nodes closer in molecular networks demonstrate faster evolutionary rates.

Conclusions:

The rate of DDI evolution can effectively estimate the rate of underlying molecular network changes.
Synergistic drug interactions highlight rapidly evolving molecular targets.
Drug combination data offers a scalable approach for studying network evolution across diverse species.