A multiscale signaling–biophysical framework reveals mechanisms of macrophage-mediated RBC clearance in sickle cell and Gaucher disease
- 1Division of Applied Mathematics, Brown University , Providence, RI ,
- 2Center for Biomedical Engineering, School of Engineering, Brown University , Providence, RI ,
Abstract
Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage–RBC signaling with dissipative particle dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for simultaneous parameter inference, hidden-state reconstruction, and integration of multiscale mechanistic constraints. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage–RBC contact, generating spatially resolved trajectories of CD47–SIRPα signaling and antibody–receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47–SIRPα–SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRPα antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis.
Related
- A biophysical framework for conscious state transitions based on intrinsic neuronal properties: the dynamic contextual element memory (DCEM) model
- ESX-5 paralog deletions in Mycobacterium tuberculosis alter macrophage cytokine signaling and bacterial heavy metal response
- Acute respiratory distress syndrome FXR/Sptlc2 signaling promotes alveolar macrophage mitophagy and limits lung inflammation
- Quantitative machine learning of protein interactions reveals the multiscale organization and molecular syntax of signaling networks
- Apoptotic Cells Promote Tim4+ Macrophage Differentiation Through Direct and Indirect Signaling Pathways
BibTeX
@article{chai2026multiscale,
title = {{A multiscale signaling–biophysical framework reveals mechanisms of macrophage-mediated RBC clearance in sickle cell and Gaucher disease}},
author = {Zhaojie Chai and Nazanin Ahmadi Daryakenari and George E Karniadakis},
journal = {PNAS Nexus},
year = {2026},
month = oct,
doi = {10.1093/pnasnexus/pgag348},
url = {https://academic.oup.com/pnasnexus/article/doi/10.1093/pnasnexus/pgag348/8868852},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A multiscale signaling–biophysical framework reveals mechanisms of macrophage-mediated RBC clearance in sickle cell and Gaucher disease
AU - Zhaojie Chai
AU - Nazanin Ahmadi Daryakenari
AU - George E Karniadakis
PY - 2026
DA - 2026/10/01
JO - PNAS Nexus
DO - 10.1093/pnasnexus/pgag348
UR - https://academic.oup.com/pnasnexus/article/doi/10.1093/pnasnexus/pgag348/8868852
AB - Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage–RBC signaling with dissipative particle dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for simultaneous parameter inference, hidden-state reconstruction, and integration of multiscale mechanistic constraints. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage–RBC contact, generating spatially resolved trajectories of CD47–SIRPα signaling and antibody–receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47–SIRPα–SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRPα antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis.
SN - 2752-6542
ER -