A Multiscale Signaling--Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease
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 robust parameter inference. 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-SIRP 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-SIRP-SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRP 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. Significance statementRed blood cells are normally removed from circulation by macrophages through tightly regulated molecular signals. In diseases such as sickle cell disease and Gaucher disease, this clearance process becomes abnormal, contributing to anemia and other complications. However, the mechanisms linking the physical properties of red blood cells to immune signaling remain poorly understood. Here we develop a multiscale computational framework that combines particle-based biophysical simulations, systems biology models, and physics-informed machine learning. This approach provides a quantitative framework to interpret how changes in red blood cell mechanics and surface signaling disrupt the CD47-SIRP inhibitory pathway that normally prevents phagocytosis. The framework provides a predictive platform for studying immune clearance and may help guide therapeutic strategies targeting red blood cell-macrophage interactions.