Multifidelity and multiscale modeling of the spleen function in sickle cell disease with in vitro, ex vivo and in vivo validations

项目摘要 脾在人体免疫系统中起着关键作用，但也清除衰老的红细胞（RBC）， 血液循环和后天或遗传性疾病改变的血液循环。镰状细胞病（SCD）患者， 脾是致病过程的首要目标之一，也是对抗主要并发症的潜在保护者。 在缺氧条件下，突变的镰状血红蛋白（HbS）聚合成纤维， 红细胞硬度和粘附性。脾过滤改变的红细胞容易镰状化（一个不能被治疗的过程）。 在人类受试者中直接观察到的）导致贫血并可能引发急性脾隔离危象 （ASSC）。另一方面，它可能预防与血管内镰状化相关的并发症。自我 镰状红细胞对血管的放大性阻断确实是血管闭塞性危象、急性胸 综合征和急性肝危象，严重影响SCD患者的生活质量和预期。 我们建议制定并验证一个新的预测模型框架，以了解脾脏过滤器如何改变 通过使用基于多重性的计算机模拟、体外、离体和体内数据协同整合SCD中的RBC 神经网络（NN）。这将提供预测模型，当新数据成为 这是生物医学建模的一个范式转变。我们将开发多尺度/多保真度计算模型 (and相应的NN实现），其链接亚细胞、细胞和血管水平现象， 跨越时空尺度的四个数量级。该刻度耦合将使用 分子动力学/耗散粒子动力学（MD/DPD）框架。我们将验证这些预测 通过体外和离体实验的数据以及在体外收集的RBC定量特征建立计算模型。 SCD患者。具体来说，我们将使用三种新的具有氧气控制的芯片上脾脏微流体装置， 我们的外国合作伙伴的独特的人脾灌注装置，具有以下目的：目的1：开发和 目的2：建立新的红细胞-巨噬细胞粘附模型 目的3：进行脾芯片实验和验证;目的4： 基于患者红细胞样本的预测框架。 实现我们的四个具体目标将大大增加我们对复杂的致病性和 脾在SCD中的保护作用。为我们新的多保真度神经网络提供形态学和 SCD患者中RBC循环的功能测量将导致SCD中残余脾功能的模型， 这将有助于预测急性脾隔离危象的风险，并指导干细胞移植的最佳时机。 移植或基因治疗。使用深度学习工具整合来自不同领域的数据的新范式 来源将适用于建模许多其他血液疾病。

Project Summary The spleen plays a key role in the human immune system but also clears senescent red blood cells (RBC) from the circulation and those altered by acquired or inherited diseases. In patients with sickle cell disease (SCD), the spleen is one of the first targets of pathogenic processes and a potential protector against major complications. Under hypoxic conditions, mutated sickle hemoglobin (HbS) polymerizes to fibers which increase both the stiffness and adhesion of RBC. Splenic filtration of altered RBC prone to sickling (a process that cannot be directly observed in human subjects) contributes to anemia and likely triggers acute splenic sequestration crises (ASSC). On the other hand, it potentially prevents complications associated with intravascular sickling. Self- amplified blockade of vessels with sickled RBCs is indeed a hallmark of vaso-occlusive crises, acute chest syndrome, and acute hepatic crises, that severely impact the life quality and expectancy of patients with SCD. We propose to formulate and validate a new predictive modeling framework for how the spleen filters altered RBC in SCD by synergistically integrating in silico, in vitro, ex vivo and in vivo data using multifidelity-based neural networks (NN). This will deliver predictive models that can continuously learn when new data become available, a paradigm shift in biomedical modeling. We will develop multiscale/multifidelity computational models (and corresponding NN implementations) that link sub-cellular, cellular, and vessel level phenomena spanning across four orders of magnitude in spatio-temporal scales. This scale coupling will be accomplished using a molecular dynamics/dissipative particle dynamics (MD/DPD) framework. We will validate these predictive computational models by data from in vitro and ex vivo experiments, and RBC quantitative features collected in SCD patients. Specifically, we will use three new spleen-on-a-chip microfluidic devices with oxygen control and the unique human spleen perfusion setup of our foreign partner, with the following aims: Aim 1: Develop and validate a splenic inter-endothelial slit filtration model; Aim 2: Develop new models of RBC macrophage adhesion and of phagocytosis in the spleen; Aim 3: Perform Spleen-on-a-Chip experiments and validation; Aim 4: Validate the predictive framework based on RBC samples from patients. Realization of our four Specific Aims will significantly increase our understanding of the complex pathogenic and protective roles of the spleen in SCD. Feeding our new multifidelity neural networks with morphological and functional measures of RBC circulating in SCD patients will lead to models for residual spleen function in SCD, which should help predict the risk of acute splenic sequestration crises, and guide optimal timing for Stem Cell Transplantation or Gene Therapy. The new paradigm in using deep learning tools to integrate data from different sources will be applicable to modeling many other blood diseases.