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Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies
Published on: January 7, 2019
Nonlinear observer output-feedback MPC treatment scheduling for HIV.
1Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA. ryanz@udel.edu
Biomedical Engineering Online
|May 31, 2011
Summary
Dynamic scheduling of Highly Active Antiretroviral Therapy (HAART) can improve immune control of Human Immunodeficiency Virus (HIV). An output-feedback Model Predictive Control (MPC) approach using viral load measurements effectively implements these dynamic schedules.
Area of Science:
- Immunology
- Control Theory
- Mathematical Biology
Background:
- Mathematical models indicate dynamic Highly Active Antiretroviral Therapy (HAART) schedules can enhance Cytotoxic Lymphocyte (CTL)-mediated control of Human Immunodeficiency Virus (HIV).
- Previous work established a Model Predictive Control (MPC) method for optimizing HAART interruption schedules to boost immune response.
Purpose of the Study:
- To introduce a nonlinear observer for the HIV-immune response system.
- To develop an integrated output-feedback MPC approach for implementing dynamic HAART scheduling using viral load data.
Main Methods:
- Developed a nonlinear observer for state estimation in the HIV-immune response model.
- Integrated the observer with an output-feedback MPC algorithm for treatment scheduling.
- Employed Monte-Carlo simulations to assess the algorithm's robustness to modeling errors and parameter variations.
Main Results:
- The nonlinear observer demonstrated robust state tracking and preserved state positivity with both continuous and discrete measurements.
- The output-feedback MPC algorithm successfully stabilized the system at the desired steady-state.
- Monte-Carlo testing revealed significant robustness, achieving 90% success in stabilization even with 15% variance in model parameters.
Conclusions:
- Dynamic HAART scheduling offers a promising strategy for enhancing immune responsiveness to HIV.
- Output-feedback MPC is well-suited for solving dynamic treatment scheduling problems with complex constraints.
- A specialized nonlinear state estimator effectively addresses challenges like state positivity and slow sampling rates, enabling practical implementation of feedback control strategies.
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