Dissipativity-Based Filtering for Switched Genetic Regulatory Networks with Stochastic Disturbances and Time-Varying...

Area of Science:

• Systems Biology
• Control Theory
• Computational Biology

Background:

• Genetic regulatory networks (GRNs) are crucial for cellular functions but are complex and prone to noise.
• Switched systems and time-varying delays introduce significant challenges in analyzing and controlling GRNs.
• Stochastic perturbations further complicate the accurate modeling and filtering of GRN dynamics.

Purpose of the Study:

• To design a robust dissipativity-based filter for switched genetic regulatory networks (GRNs).
• To address the challenges posed by stochastic perturbations and time-varying delays in GRN filtering.
• To ensure mean-square exponential stability and guaranteed dissipativity performance for the filtering error system.

Main Methods:

• Utilizing a piecewise Lyapunov function for stability analysis.
• Applying the average dwell time method to handle system switching.
• Formulating sufficient conditions using Linear Matrix Inequalities (LMIs) for filter existence.

Main Results:

• Developed a novel set of sufficient conditions for designing dissipative filters.
• Ensured that the filtering error system achieves mean-square exponential stability.
• Guaranteed the dissipativity performance of the filtering error system.
• Provided a method to compute filter gains by solving LMIs.

Conclusions:

• The proposed dissipativity-based filtering approach is effective for switched GRNs with stochastic perturbations and time-varying delays.
• The LMI-based conditions provide a systematic way to design stable and performant filters.
• Simulation results validate the practical applicability and effectiveness of the developed filter design.