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Controlling network ensembles.

Isaac Klickstein¹, Francesco Sorrentino²

¹Department of Mechanical Engineering, University of New Mexico, Albuquerque, NM, USA. iklick@protonmail.com.

Nature Communications

|March 26, 2021

Summary

This summary is machine-generated.

This study addresses optimal control for uncertain biological networks by developing a minimum energy controller. The research analyzes how control costs scale with network complexity and uncertainty, offering insights for robust system management.

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Area of Science:

Systems Biology
Control Theory
Network Science

Background:

Optimal control typically assumes perfect system knowledge, which is unrealistic for uncertain biological networks.
Biological and engineered networks often exhibit significant uncertainty in their parameters and structure.
Existing control methods may not be robust to the inherent variability in network dynamics.

Purpose of the Study:

To investigate minimum energy control for network ensembles with uncertain realizations.
To develop a controller that achieves tunable accuracy in the presence of uncertainty.
To characterize the scaling of control energy and cost with the number of possible network realizations.

Main Methods:

Formulating an optimal control problem for network ensembles.
Analyzing the solution in the limit of continuous distributions for system parameters.
Deriving weighting terms for the objective function in uncertain optimal control.
Verifying the theoretical framework using three distinct network models.

Main Results:

A controller is derived that guarantees tunable accuracy for uncertain network control.
The study quantifies how control energy and cost increase with the number of possible network realizations.
Analysis provides insights into posing objective functions for continuous distribution-based control.
Theoretical predictions are validated across diverse network examples.

Conclusions:

The developed optimal control framework effectively manages uncertainty in network ensembles.
Understanding the scaling of control costs is crucial for designing robust controllers for complex systems.
The findings are applicable to biological signaling networks and other systems with inherent parameter uncertainty.