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Updated: May 16, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Construction and application of dynamic protein interaction network based on time course gene expression data.
Jianxin Wang1, Xiaoqing Peng, Min Li
1School of Information Science and Engineering, Central South University, Changsha, China. jxwang@mail.csu.edu.cn
This study introduces a dynamic protein interaction network (DPIN) to capture cellular cycle changes. DPINs improve protein complex detection and offer new insights into essential protein regulation.
Area of Science:
- Systems Biology
- Bioinformatics
- Computational Biology
Background:
- Static protein interaction networks (PINs) lack dynamic cellular information.
- Understanding protein activity over time is crucial for biological processes.
Purpose of the Study:
- To develop a dynamic protein interaction network (DPIN) model.
- To evaluate DPINs for improved protein complex detection and essential protein identification.
Main Methods:
- A three-sigma method was proposed to identify active protein time points.
- A dynamic protein interaction network (DPIN) was constructed.
- Network algorithms (MCL, CPM, core attachment) were applied to static and dynamic networks.
Main Results:
- DPINs significantly outperformed static PINs in protein complex detection accuracy, sensitivity, and specificity.
- The three-sigma principle revealed 23-45% of proteins are active per time point.
- 94% of essential proteins were active at ≥12 time points, suggesting regulatory feedback.
Conclusions:
- DPINs provide a more accurate representation of protein interactions during the cellular cycle.
- Dynamic network analysis offers new perspectives for predicting essential proteins.
- The findings suggest feedback mechanisms stabilize essential protein expression.
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