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Asynchronous stochastic Boolean networks as gene network models.

Peican Zhu1, Jie Han

  • 1Department of Electrical and Computer Engineering, University of Alberta , Edmonton, Alberta, Canada .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|June 18, 2014
PubMed
Summary
This summary is machine-generated.

Asynchronous stochastic Boolean networks (ASBNs) model gene regulatory networks (GRNs) with asynchronous gene updates and noise. ASBNs reveal the robustness of T helper network attractors under various perturbations.

Keywords:
asynchronous state updategene regulatory networksstochastic Boolean networksstochasticity

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genetics

Background:

  • Gene regulatory networks (GRNs) are often modeled using synchronous logical models.
  • Synchronous updates may not accurately reflect biological reality, as gene state changes occur at different rates.
  • Biological systems are inherently subject to noise and external perturbations.

Purpose of the Study:

  • To introduce asynchronous stochastic Boolean networks (ASBNs) for modeling GRNs.
  • To investigate the impact of asynchronous state updates and stochasticity on GRN dynamics.
  • To analyze the robustness of biological networks under varying conditions.

Main Methods:

  • Development and application of asynchronous stochastic Boolean networks (ASBNs).
  • Incorporation of stochastic sequences to encode probabilities and model noise.
  • Analysis of gene regulatory network dynamics using multiple stochasticity and asynchronicity models.

Main Results:

  • ASBNs effectively model GRNs with asynchronous gene state transitions.
  • The method efficiently evaluates steady-state distributions (SSDs) even with random gene perturbations.
  • ASBNs demonstrate the robustness of T helper network attractors under stochastic and asynchronous conditions.

Conclusions:

  • ASBNs provide a powerful framework for studying GRN dynamics beyond synchronous assumptions.
  • The proposed model captures essential biological features like noise and asynchronous updates.
  • The T helper network exhibits robust behavior, as evidenced by ASBN analysis.