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Tracking perturbations in Boolean networks with spectral methods.

Juha Kesseli1, Pauli Rämö, Olli Yli-Harja

  • 1Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland. kesseli@cs.tut.fi

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 4, 2005
PubMed
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We present a novel method for predicting perturbation spread in Boolean networks, applicable even to irregular networks. This approach uses abstract Fourier transforms for efficient computation, aiding in chaos quantification and network comparison.

Area of Science:

  • Computational Biology
  • Network Science
  • Dynamical Systems

Background:

  • Boolean networks are widely used to model complex biological systems.
  • Understanding perturbation spread is crucial for predicting system behavior and stability.
  • Existing methods often struggle with networks lacking regular topology.

Purpose of the Study:

  • To develop an efficient method for predicting perturbation spread in Boolean networks, irrespective of their topology.
  • To apply the method to biological network data and quantify chaos.
  • To enable comparison of different function distributions based on their generated order.

Main Methods:

  • Development of a prediction method for perturbation spread in Boolean networks.
  • Utilizing abstract Fourier transforms for efficient computation of iterative formulas.

Related Experiment Videos

  • Application to networks with function distributions derived from biological data.

    • Main Results:

    • The proposed method efficiently predicts perturbation spread in Boolean networks, including those with no regular topology.
    • The method allows for the computation of Derrida plots over arbitrary time steps.
    • Comparison of function distributions based on their capacity to generate order in random networks is enabled.

    Conclusions:

    • The developed method provides an efficient way to predict perturbation spread in diverse Boolean networks.
    • This work facilitates the quantification of chaos in Boolean networks.
    • The findings enable comparative analysis of network function distributions regarding their inherent order.