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DNFE: Directed network flow entropy for detecting tipping points during biological processes.

Xueqing Peng1,2, Rui Qiao1,2, Peiluan Li1,2

  • 1School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China.

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|July 29, 2025
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Summary
This summary is machine-generated.

We developed a new directed network flow entropy (DNFE) method to identify critical tipping points in biological systems using omics data. This robust approach effectively detects dynamic network biomarkers and regulatory gene relationships, even in large-scale, noisy datasets.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Dynamic biological processes exhibit critical tipping points marking state transitions.
  • Traditional methods using undirected networks struggle with high-dimensional, small-sample omics data, especially single-cell data.
  • Identifying tipping points and their driving networks is crucial for predicting and mitigating biological shifts.

Purpose of the Study:

  • To develop a robust method for identifying tipping points and dynamic network biomarkers from omics data.
  • To address limitations of traditional network analysis methods in high-dimensional biological datasets.
  • To explore gene regulatory relationships and identify key drivers in biological transitions.

Main Methods:

  • Developed the directed network flow entropy (DNFE) method to transform omics data into directed networks.
  • Applied DNFE to diverse datasets including single-cell RNA-sequencing (scRNA-seq), bulk tumor, and blood data.
  • Validated the method through numerical simulations on various noise levels and large-scale gene regulatory networks.

Main Results:

  • DNFE effectively identified critical states and their dynamic network biomarkers across multiple real-world datasets.
  • The method demonstrated robustness to noise and outperformed existing approaches in tipping point detection.
  • DNFE successfully predicted active transcription factors and identified previously overlooked "dark genes".

Conclusions:

  • The DNFE method provides a robust and effective framework for analyzing dynamic biological processes using omics data.
  • DNFE enhances the identification of critical states, regulatory networks, and key genes, advancing systems biology research.
  • This approach is applicable to both single-cell and bulk omics data, offering broad utility in biological discovery.