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Investigation of Macrophage Polarization Using Bone Marrow Derived Macrophages
Published on: June 23, 2013
Experimental Control of Macrophage Pro-Inflammatory Dynamics Using Predictive Models
Laura D Weinstock1,2, James E Forsmo2, Alexis Wilkinson3
1Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, United States.
Abstract:
Macrophage activity is a major component of the healthy response to infection and injury that consists of tightly regulated early pro-inflammatory activation followed by anti-inflammatory and regenerative activity. In numerous diseases, however, macrophage polarization becomes dysregulated and can not only impair recovery, but can promote further injury and pathogenesis, e.g., after trauma or in diabetic ulcers. Dysregulated macrophages may either fail to polarize or become chronically polarized, resulting in increased production of cytotoxic factors, diminished capacity to clear pathogens, or failure to promote tissue regeneration. In these cases, a method of predicting and dynamically controlling macrophage polarization will enable a new strategy for treating diverse inflammatory diseases. In this work, we developed a model-predictive control framework to temporally regulate macrophage polarization. Using RAW 264.7 macrophages as a model system, we enabled temporal control by identifying transfer function models relating the polarization marker iNOS to exogenous pro- and anti-inflammatory stimuli. These stimuli-to-iNOS response models were identified using linear autoregressive with exogenous input terms (ARX) equations and were coupled with non-linear elements to account for experimentally identified supra-additive and hysteretic effects. Using this model architecture, we were able to reproduce experimentally observed temporal iNOS dynamics induced by lipopolysaccharides (LPS) and interferon gamma (IFN-γ). Moreover, the identified model enabled the design of time-varying input trajectories to experimentally sustain the duration and magnitude of iNOS expression. By designing transfer function models with the intent to predict cell behavior, we were able to predict and experimentally obtain temporal regulation of iNOS expression using LPS and IFN-γ from both naïve and non-naïve initial states. Moreover, our data driven models revealed decaying magnitude of iNOS response to LPS stimulation over time that could be recovered using combined treatment with both LPS and IFN-γ. Given the importance of dynamic tissue macrophage polarization and overall inflammatory regulation to a broad number of diseases, the temporal control methodology presented here will have numerous applications for regulating immune activity dynamics in chronic inflammatory diseases.
Insights
This study developed a predictive control framework to dynamically regulate macrophage polarization, a key immune cell process. This method allows precise temporal control of inflammatory responses, offering new therapeutic strategies for diseases involving immune dysregulation.
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.

