TY - JOUR
T1 - A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization
AU - Zhao, Chen
AU - Medeiros, Thalyta X.
AU - Sové, Richard J.
AU - Annex, Brian H.
AU - Popel, Aleksander S.
N1 - Publisher Copyright:
© 2021 The Authors
PY - 2021/2/19
Y1 - 2021/2/19
N2 - 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.
AB - 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.
KW - cell biology
KW - in silico biology
KW - systems biology
UR - http://www.scopus.com/inward/record.url?scp=85100695487&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100695487&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2021.102112
DO - 10.1016/j.isci.2021.102112
M3 - Article
AN - SCOPUS:85100695487
SN - 2589-0042
VL - 24
JO - iScience
JF - iScience
IS - 2
M1 - 102112
ER -