Experimental Control of Macrophage Pro-Inflammatory Dynamics Using Predictive Models.

Area of Science:

• Immunology and Systems Biology
• Computational Biology and Mathematical Modeling

Background:

• Macrophage polarization is crucial for immune responses, but dysregulation contributes to various diseases.
• Impaired or chronic macrophage polarization can hinder healing and promote pathogenesis.
• Current therapeutic approaches lack dynamic control over macrophage polarization states.

Purpose of the Study:

• To develop a model-predictive control framework for temporal regulation of macrophage polarization.
• To identify models that predict macrophage responses to inflammatory stimuli.
• To enable dynamic control over macrophage polarization for therapeutic applications.

Main Methods:

• Utilized RAW 264.7 macrophages as a model system.
• Developed transfer function models relating iNOS expression to pro- and anti-inflammatory stimuli (LPS, IFN-γ).
• Incorporated non-linear elements (supra-additivity, hysteresis) into autoregressive with exogenous input (ARX) models.

Main Results:

• Successfully reproduced experimentally observed temporal iNOS dynamics.
• Designed time-varying input trajectories to control the duration and magnitude of iNOS expression.
• Demonstrated recovery of LPS response decay using combined LPS and IFN-γ treatment.

Conclusions:

• A data-driven, model-predictive control framework enables temporal regulation of macrophage polarization.
• This methodology offers a novel strategy for managing inflammatory diseases by controlling immune cell dynamics.
• The findings have broad implications for treating diverse conditions characterized by immune dysregulation.