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Representing perturbed dynamics in biological network models.

Gautier Stoll1, Jacques Rougemont, Felix Naef

  • 1NCCR Molecular Oncology, chemin des Boveresses 155, 1066 Epalinges, Switzerland. Gautier.stoll@curie.fr

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 7, 2007
PubMed
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We analyze gene activity dynamics in small biological networks using discrete models. Our method quantifies how network changes impact function, identifying crucial and redundant gene interactions.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Dynamics

Background:

  • Biological networks, such as gene regulatory networks, exhibit complex dynamics.
  • Understanding how network structure influences function is crucial for deciphering cellular processes.
  • Perturbations to network connectivity can lead to significant changes in system behavior.

Purpose of the Study:

  • To develop a quantitative framework for analyzing the impact of structural perturbations on biological network dynamics.
  • To characterize how changes in network connectivity affect fixed points and basin of attraction sizes.
  • To identify critical and redundant interactions within biological networks.

Main Methods:

  • Utilizing deterministic discrete dynamical models to simulate network behavior.

Related Experiment Videos

  • Introducing analytical measures: basin entropy (H) and perturbation size (Δ).
  • Applying the framework to the yeast-cell-cycle network.

    • Main Results:

    • The study provides a low-dimensional fingerprint of network behavior under various perturbations.
    • Identified specific gene interactions crucial for proper cell-cycle progression.
    • Pinpointed functionally redundant connections within the yeast-cell-cycle network.

    Conclusions:

    • The developed analytical approach effectively characterizes dynamical modifications in response to network perturbations.
    • The findings offer insights into the robustness and adaptability of biological networks.
    • This framework aids in understanding gene function and network design principles.