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Controllability analysis of networks.

Anna Lombardi1, Michael Hörnquist

  • 1Department of Science and Technology, Linköping University, SE-601 74 Norrköping, Sweden.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 7, 2007
PubMed
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Controllability theory from linear systems helps analyze biological networks. Input signals can control specific nodes, like protein levels, in biological systems, with implications for gene regulatory networks.

Area of Science:

  • Systems Biology
  • Control Theory
  • Network Science

Background:

  • Linear systems theory provides tools to understand system dynamics.
  • Biological networks, such as gene regulatory networks, exhibit complex behaviors.
  • Controllability is a key concept for understanding external influence on systems.

Purpose of the Study:

  • To apply the concept of controllability from control theory to biological networks.
  • To define and analyze the controllability of individual nodes within these networks.
  • To explore the implications of controllability for understanding biological system regulation.

Main Methods:

  • Adaptation of linear systems controllability definitions to biological network contexts.
  • Analysis of necessary and sufficient conditions for node controllability.

Related Experiment Videos

  • Interpretation of the controllability matrix for network analysis.
  • Application to case studies, including a gene regulatory network.

    • Main Results:

    • A node is controllable if external signals can arbitrarily adjust its level in finite time.
    • Being downstream of an input node is necessary but not sufficient for controllability.
    • An interpretation of the controllability matrix for biological networks is provided.
    • Case studies illustrate the application of these concepts.

    Conclusions:

    • Controllability theory offers a framework for analyzing external control of biological networks.
    • Understanding controllability is crucial for predicting and manipulating biological system behavior.
    • The study provides insights into the regulation of gene regulatory networks.