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Related Experiment Video

Updated: Feb 11, 2026

Counting and Determining the Viability of Cultured Cells

Counting and Determining the Viability of Cultured Cells

Published on: June 23, 2008

Counting motifs in dynamic networks.

Kingshuk Mukherjee¹, Md Mahmudul Hasan², Christina Boucher²

¹Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA. kingdgp@ufl.edu.

BMC Systems Biology

|April 20, 2018

Summary

This summary is machine-generated.

This study introduces a scalable method for efficiently counting network motifs in dynamic biological networks. The approach incrementally updates motif frequencies, significantly outperforming static methods for evolving networks.

Keywords:

Biological networks Dynamic networks Motif finding

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Published on: August 21, 2019

Area of Science:

Computational Biology
Network Science
Bioinformatics

Background:

Network motifs are recurring sub-networks crucial for understanding biological network functions.
Detecting motifs is computationally intensive due to subgraph isomorphism problems.
Biological networks are dynamic, with changing topologies and motif frequencies over time.

Purpose of the Study:

To design and develop a scalable method for counting motifs in dynamic biological networks.
To enable efficient, incremental updates of motif frequencies as network topology evolves.

Main Methods:

Developed a novel algorithm for incremental frequency updates of network motifs.
Tested the method on synthetic and real biological network datasets.

Main Results:

The proposed method updates motif frequencies orders of magnitude faster than re-computation.
Performance gains increase with higher network evolution rates.
Achieved high accuracy (≥96%) and scalability for large, dense networks.

Conclusions:

The method provides an efficient and accurate solution for motif counting in dynamic biological networks.
Demonstrated utility in uncovering insights into the evolution of biological processes.
Scalable approach suitable for large and rapidly changing biological networks.