NOCAD

NOCAD performs network-based observability and controllability analysis of linear or linearized dynamical systems to identify driver nodes and optimize sensor placement in biological networks, implemented for MATLAB and Octave.

Key Features:

• Structural controllability and observability measures: Generates quantitative measures associated with structural controllability and observability for linear or linearized dynamical systems.
• Driver node identification: Identifies driver nodes required to control networked dynamical systems.
• Optimal sensor placement: Automates comparison of sensor placement strategies and supports selection of optimal sensors.
• Network science methodologies: Applies network science approaches to analyze the structural properties of dynamical systems.
• MATLAB and Octave implementation: Provided as a toolbox compatible with MATLAB and Octave environments.

Scientific Applications:

• Analysis of complex biological networks: Facilitates examination of structural control and observation properties in complex biological networks within the life sciences.
• Caenorhabditis elegans neural network study: Can be applied to the neural network of Caenorhabditis elegans to investigate controllability and observability.
• Experimental design and intervention optimization: Supports optimization of experimental designs and interventions by comparing sensor placement strategies.
• Understanding network responses: Enables assessment of how network structure influences responses to stimuli via controllability and observability analysis.

Methodology:

Uses network science methodologies and an automated suite of methods to compute structural controllability and observability measures on linear or linearized dynamical systems, enabling identification of driver nodes and comparison of sensor placement strategies.