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Protein Networks

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Updated: Apr 25, 2026

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

TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information.

Yongsoo Kim¹, Jin-Hyeok Jang¹, Seungjin Choi¹

¹School of Interdisciplinary Bioscience and Bioengineering and Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 790-784, Korea and Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea.

Bioinformatics (Oxford, England)

|August 28, 2014

Summary

This summary is machine-generated.

We developed TEMPI, a probabilistic model to analyze dynamic protein-protein interaction networks over time. TEMPI reveals how cellular processes transition and interact, offering insights into biological mechanisms.

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Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Understanding time-evolving protein-protein interaction (PPI) networks is crucial for deciphering dynamic cellular processes.
Current network inference methods struggle to capture temporal structural transitions and associated biological process interplays.

Purpose of the Study:

To present TEMPI, a novel probabilistic model for estimating time-evolving differential PPI networks.
To jointly infer dynamic PPI networks and the serial activation of differentially regulated processes (DRPs).

Main Methods:

Developed TEMPI, a probabilistic model integrating network structures, time-course gene expression, and Gene Ontology biological processes (GOBPs).
Employed maximum likelihood estimation to jointly estimate time-evolving differential networks (TDNs) and DRP activation.
Applied TEMPI to two time-course datasets for validation.

Main Results:

TEMPI successfully estimated TDNs that capture temporal transitions in PPI network structures.
Identified serial activation patterns of DRPs linked to these network transitions.
Revealed time-dependent associations between DRPs, providing mechanistic hypotheses.

Conclusions:

TEMPI enables the interpretation of dynamic PPI networks through the lens of temporal DRP transitions.
The model facilitates the study of mechanisms underlying serial activation and temporal interplay of key cellular processes.