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Updated: Jul 13, 2026
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Controllability analysis of networks.
Anna Lombardi1, Michael Hörnquist
1Department of Science and Technology, Linköping University, SE-601 74 Norrköping, Sweden.
Summary
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.
- 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.
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