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Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response

Gregory Zitelli1, Seddik M Djouadi, Judy D Day

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This study enhances acute inflammation modeling by coupling nonlinear model predictive control with a particle filter. Robust state estimation improves predictability for complex inflammatory states.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • The inflammatory response is crucial for homeostasis but can cause tissue damage in acute systemic inflammation, necessitating response down-regulation.
  • A nonlinear ordinary differential equations (ODE) model of acute inflammation with feedback loops was previously studied.
  • Nonlinear model predictive control (NMPC) was explored for therapeutic intervention in silico, but state estimation challenges exist due to unmeasurable variables.

Purpose of the Study:

  • To investigate the nonlinear reachability of the acute inflammation ODE model.
  • To improve the predictability of the inflammation model by integrating NMPC with a particle filter for robust state estimation.

Main Methods:

  • Analysis of nonlinear reachability for the ODE model.
  • Coupling of nonlinear model predictive control (NMPC) with a particle filter for enhanced state estimation.
  • Validation of the integrated approach for modeling complex inflammatory states.

Main Results:

  • Demonstrated nonlinear reachability of the underlying inflammation model.
  • Achieved improved model predictability through NMPC coupled with a particle filter.
  • Showcased robust state estimation for the highly nonlinear ODE model.

Conclusions:

  • Robust state estimation using NMPC and particle filters offers an alternative to traditional updating strategies for unmeasurable states.
  • This approach enhances the predictability of complex biological models like acute inflammation.
  • Advances in computational methods can aid in understanding and managing complex physiological responses.