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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Phylogenies from dynamic networks.

Cornelia Metzig¹, Oliver Ratmann², Daniela Bezemer³

¹Dept of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.

Plos Computational Biology

|February 27, 2019

Summary

This summary is machine-generated.

Simulating pathogen spread on dynamic contact networks reveals that network dynamics significantly impact phylogenetic tree shapes. Accurately modeling these dynamic networks is crucial for reliable phylodynamic analyses and epidemiological parameter estimation.

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Area of Science:

Phylodynamics and Network Science
Computational Epidemiology

Background:

Phylogenetic trees from pathogen spread can inform about contact networks, but this relationship is complex and often studied using static network assumptions.
Human contact networks are inherently dynamic, making static models potentially inaccurate for understanding pathogen transmission.

Purpose of the Study:

To investigate how network dynamics influence pathogen phylogenetic tree shapes.
To compare the accuracy of dynamic versus static network models in phylodynamic inference.
To assess the impact of network mis-specification on estimating transmission rates.

Main Methods:

Simulated pathogen phylogenetic trees on various dynamic random networks (Erdős-Renyi, skewed degree, clustered).
Analyzed tree shape features (substructures, imbalance, network science metrics, branch lengths) using principal component analysis.
Compared dynamic network simulations with time-integrated static network models (e.g., Barabasi-Albert).
Evaluated classifier performance for transmission rate estimation under network mis-specification.

Main Results:

Network type and dynamics significantly alter phylogenetic tree shapes.
Static network models can be poor approximations of dynamic contact networks.
Mis-specifying the contact network or its parameters (mean degree, turnover rate) adversely affects transmission rate estimation.
HIV phylogenetic trees were classified using models trained on simulated data, highlighting real-world implications.

Conclusions:

Accurate modeling of dynamic contact networks is essential for robust phylodynamic inference.
Ignoring network dynamics can lead to significant errors in estimating epidemiological parameters.
Phylodynamic tools require careful consideration of contact network structure and temporal changes for reliable results.