The objective of this project is to develop a validated multiscale modeling methodology for quantifying the biophysical characteristics of sickle cell disease (SCD) -- a hematological disorder that affects tens of thousands of people in US with one in every 500 African-American births resulting in a child with SCD. The pathogenesis of SCD results from (1) irregular red blood cell (RBC) shapes due to hemoglobin polymerization inside the RBCs;(2) stiffening of the RBC membrane;and (3) adhesion of sickle RBCs to the endothelium and the other blood cells. The combination of these phenomena results in vaso-occlusive events or """"""""crises"""""""" responsible for the majority of morbidity and mortality associated with SCD but little is certain about the proximal causes or the circumstances in which they occur. The spatio-temporal scales involved in accurately modeling SCD blood flow and vaso-occlusion span at least four orders of magnitude, hence new numerical methods are needed to simulate such multiscale phenomena. We present a general methodology based on 3D dissipative particle dynamics (DPD) to model flow and soft matter seamlessly, i.e., RBCs and other blood cells, blood plasma, cytosol, hemoglobin polymerization, and adhesive dynamics. DPD can be interfaced with molecular dynamics (MD) and with continuum-based description (e.g. Navier-Stokes) based on the triple-decker algorithm we have developed in order to capture molecular details or for computational efficiency in simulating large arteries or networks, respectively. We adopt the same approach here that has proven very effective in our previous work on malaria, namely that models for single RBCs (healthy or sickled), informed and validated from comprehensive single-cell measurements, will be used to predict the collective dynamics and rheology of SCD blood flow. We also present a systematic experimental plan, using microfluidics, nanomechanics and advanced optical techniques, to validate the various stages of the development of our models by targeting individual scales as well as interactions between scales. We will extend the first generation of models to study different modalities of existing and experimental therapeutic interventions for SCD, including simple transfusion, fetal hemoglobin (HbF) induction by hydroxyurea, and RBC hydration. Predictability of multiscale models requires quantifying uncertainty, and, to this end, we will incorporate polynomial chaos methods to model and propagate parametric uncertainties through the multiscale system. We plan to disseminate our models, software tools, and experimental data including the general-purpose triple-decker algorithm, via web-based repositories, existing public open-ware sites, tutorials and through the MSM consortium.
We propose to develop and validate a multiscale modeling methodology for sickle cell disease (SCD) affecting 72,000 people in US. We will model multiscale phenomena across more than four orders of magnitude in spatio-temporal scales. We will develop a new generation of models to study different modalities of therapeutic interventions for SCD, including simple transfusion, HbF induction by hydroxyurea, and RBC hydration.
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|Papageorgiou, Dimitrios P; Abidi, Sabia Z; Chang, Hung-Yu et al. (2018) Simultaneous polymerization and adhesion under hypoxia in sickle cell disease. Proc Natl Acad Sci U S A 115:9473-9478|
|Li, He; Yang, Jun; Chu, Trang T et al. (2018) Cytoskeleton Remodeling Induces Membrane Stiffness and Stability Changes of Maturing Reticulocytes. Biophys J 114:2014-2023|
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|Tang, Yu-Hang; Lu, Lu; Li, He et al. (2017) OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution. Biophys J 112:2030-2037|
|Lu, Xinran; Galarneau, Michelle M; Higgins, John M et al. (2017) A microfluidic platform to study the effects of vascular architecture and oxygen gradients on sickle blood flow. Microcirculation 24:|
|Xu, Mengjia; Papageorgiou, Dimitrios P; Abidi, Sabia Z et al. (2017) A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Comput Biol 13:e1005746|
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