Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Stochastic optimal therapy for enhanced immune response.

Robert F Stengel1, Raffaele Ghigliazza

  • 1Department of Mechanical and Aerospace Engineering, P.O. Box CN5263, Princeton University, School of Engineering and Applied Science, D-202 Engineering Quadrangle, Princeton, NJ 08544, USA. stengel@princeton.edu

Mathematical Biosciences
|September 15, 2004
PubMed
Summary

This study enhances immune response therapies using optimal control and state estimation. Imperfect measurements are managed to improve therapeutic strategies against microbial attacks.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dynamics of a cytokine storm.

PloS one·2012
Same author

Mutation and control of the human immunodeficiency virus.

Mathematical biosciences·2008
Same author

The signatures of autozygosity among patients with colorectal cancer.

Cancer research·2008
Same author

Relationship of gene expression and chromosomal abnormalities in colorectal cancer.

Cancer research·2006
Same author

Smooth function approximation using neural networks.

IEEE transactions on neural networks·2005
Same author

Optimal enhancement of immune response.

Bioinformatics (Oxford, England)·2002

Area of Science:

  • * Mathematical modeling and control theory applied to immunology.
  • * Investigating dynamic systems for therapeutic interventions.

Background:

  • * Humoral immune response to microbial attack modeled as a dynamic system.
  • * Previous work established optimal control for immune response with complete state information.
  • * Therapies were designed to balance pathogen elimination and organ health.

Purpose of the Study:

  • * To evaluate the impact of corrupted or incomplete measurements on feedback control strategies.
  • * To develop methods for implementing therapeutic protocols with imperfect state information.
  • * To extend the application of optimal control theory to immune response enhancement.

Main Methods:

  • * Utilized a generic mathematical model of immune response.

Related Experiment Videos

  • * Focused on stochastic optimal control and neighboring-optimal feedback control.
  • * Incorporated optimal state estimation to handle measurement errors and incomplete data.

    • Main Results:

    • * Imperfect measurements degrade feedback control precision.
    • * Optimal state estimation enables effective feedback control despite measurement errors.
    • * Demonstrated complete observability with specific measurement combinations for a four-state system.

    Conclusions:

    • * State estimation is crucial for robust feedback control in immune response therapies.
    • * This approach extends the applicability of optimal control for developing new therapeutic protocols.
    • * Enhancing immune response with optimal control and state estimation offers a promising therapeutic avenue.