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Path-based quantification of activation and repression in Boolean models using BooLEVARD.

Marco Fariñas1, Eirini Tsirvouli2,3,4, John Zobolas5,6

  • 1Department of Biomedical Laboratory Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. marco.farinas@ntnu.no.

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This study introduces BooLEVARD, a Python package for analyzing biological Boolean models. BooLEVARD quantifies signaling paths, enhancing understanding of signal transduction and disease development.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Boolean models are valuable for studying biological dynamics but struggle with continuous signal transduction aspects.
  • Existing tools for path exploration in Boolean models often require simplifying assumptions, compromising accuracy.

Purpose of the Study:

  • To introduce BooLEVARD, a Python package for efficient quantification of signaling paths in Boolean models.
  • To provide a more detailed and quantitative perspective on molecular signal propagation.
  • To enhance the representation of signal strength in biological networks.

Main Methods:

  • Developed BooLEVARD, a Python package focusing on non-redundant paths influencing Boolean outcomes.
  • Applied BooLEVARD to a Boolean model of cancer metastasis for cell-fate decision analysis.
  • Evaluated BooLEVARD's scalability on large, complex Boolean models.

Main Results:

  • BooLEVARD efficiently quantifies paths leading to node activation or repression.
  • Demonstrated BooLEVARD's utility in identifying critical signaling events in cancer metastasis.
  • Showcased BooLEVARD's effective scalability for large-scale Boolean models.

Conclusions:

  • BooLEVARD offers a precise tool for quantitative analysis of signaling dynamics in Boolean models.
  • The package enhances understanding of disease development and drug responses.
  • BooLEVARD is freely available for research use.