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Target controllability with minimal mediators in complex biological networks.

Ali Ebrahimi1, Abbas Nowzari-Dalini2, Mahdi Jalili3

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Summary
This summary is machine-generated.

This study introduces a new algorithm for complex network control, minimizing driver nodes while reducing unintended mediator nodes. This method enhances target control by considering path lengths, crucial for applications like cancer treatment.

Keywords:
Complex networksControllabilityDynamical systemsKalman's controllability condition

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Area of Science:

  • Network Science
  • Systems Biology
  • Control Theory

Background:

  • Complex network controllability involves identifying minimum driver nodes for full system control.
  • Target control focuses on controlling specific network portions, often involving unintended mediator nodes.
  • Unintended control of healthy cells in cancer treatment can weaken the immune system.

Purpose of the Study:

  • To propose an algorithm for finding the minimum number of driver nodes for target control.
  • To minimize the number of unintentionally controlled mediator nodes during target control.
  • To develop a controllability condition applicable to directed networks that meets Kalman's criteria.

Main Methods:

  • Developed a novel algorithm for target control in complex networks.
  • Utilized path lengths between node pairs as a key factor in the controllability condition.
  • Ensured the proposed condition aligns with Kalman's controllability rank condition for directed networks.

Main Results:

  • The algorithm successfully identifies minimum driver nodes while minimizing mediator nodes.
  • Path length is a significant factor influencing target control properties.
  • Increasing average path length decreases the driver-to-target node ratio and increases the mediator-to-target node ratio.

Conclusions:

  • The proposed method offers a solution for minimum driver node identification with minimal mediators.
  • Path length analysis provides insights into optimizing target control strategies.
  • The methodology has potential applications in biological networks, including cancer research.