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CycFlowDec: A Python module for decomposing flow networks using simple cycles.

Austen Bernardi1, Jessica M J Swanson1

  • 1Department of Chemistry, University of Utah, 1400 E, Salt Lake City, UT, 84112, United States of America.

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New algorithms quantify expected flow in closed networks, enabling cycle analysis. This method, implemented in the open-source CycFlowDec Python module, offers valuable insights into network behavior.

Keywords:
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Area of Science:

  • Systems Biology
  • Network Analysis
  • Computational Science

Background:

  • Current network analysis tools lack algorithms for expected cyclic flow decomposition.
  • Analyzing cyclic flow is crucial for understanding network dynamics and behavior.

Purpose of the Study:

  • To present novel algorithms for calculating expected flow through simple cycles in closed networks.
  • To introduce CycFlowDec, an open-source Python module for cyclic flow decomposition.

Main Methods:

  • Development of new algorithms for expected cyclic flow determination.
  • Implementation of these algorithms into a general and efficient Python module (CycFlowDec).

Main Results:

  • The developed algorithms enable quantitative characterization of network cycles.
  • The CycFlowDec module provides a tool for analyzing sensitivity and correlative behavior of network cycles.

Conclusions:

  • Expected cyclic flow decomposition offers significant potential for network analysis.
  • CycFlowDec provides an accessible and efficient solution for researchers in network science.