A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization

Chen Zhao, Thalyta X. Medeiros, Richard J. Sové, Brian H. Annex, Aleksander S. Popel

Research output: Contribution to journalArticlepeer-review

Abstract

Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an in silico population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an in vitro condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive “virtual macrophage” simulation platform.

Original languageEnglish (US)
Article number102112
JournaliScience
Volume24
Issue number2
DOIs
StatePublished - Feb 19 2021

Keywords

  • cell biology
  • in silico biology
  • systems biology

ASJC Scopus subject areas

  • General

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