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Sickle cell disease is induced by a mutation that converts normal adult hemoglobin to sickle hemoglobin (HbS) and engenders intracellular polymerization of deoxy-HbS and erythrocyte sickling. Development of anti-sickling therapies requires quantitative understanding of HbS polymerization kinetics under organ-specific conditions, which are difficult to assess with existing experimental techniques. Thus, we developed a kinetic model based on the classical nucleation theory to examine the effectiveness of potential anti-sickling drug candidates. We validated this model by comparing its predictability against prior in vivo and in vitro experimental results. We used the model to quantify the efficacy of sickling inhibitors and obtain results consistent with recent screening assays. Global sensitivity analysis on the kinetic parameters in the model revealed that the solubility, nucleation rate prefactor, and oxygen affinity are quantities that dictate HbS polymerization. This finding provides quantitative guidelines for the discovery of intracellular processes to be targeted by sickling inhibitors. 

Computational modeling and simulations can tackle a broad range of morphological, mechanical, and rheological problems relevant to blood and blood cells. Here, we review some continuum-based and particle-based computational approaches towards the modeling of healthy and diseased red blood cells (RBCs) with focus on the most recent contributions, including the three-level multiscale RBC model coupled with the boundary integral method of surrounding flows and two-component RBC models with explicit descriptions of lipid bilayer, cytoskeleton, and transmembrane proteins.