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，但也能清除衰老的红细胞。 血液循环以及因后天或遗传性疾病而改变的循环。在镰状细胞病(SCD)患者中 脾是致病过程的首要靶点之一，也是预防重大并发症的潜在保护者。 在低氧条件下，突变的镰状血红蛋白(HBS)聚合成纤维，从而增加 红细胞的硬度和粘附性。脾滤过改变的红细胞容易出现镰状(这一过程不能 在人体中直接观察到的)会导致贫血，并可能引发急性脾隔离危机 (ASSC)。另一方面，它有可能预防与血管内镰刀相关的并发症。自我- 伴有镰状红细胞的血管被放大的阻塞确实是血管闭塞危象、急性胸腔积液的特征 严重影响SCD患者生活质量和预期的综合征和急性肝脏危象。 我们建议制定和验证一个新的预测模型框架，用于研究脾滤器如何改变 SCD中的RBC通过使用基于多保真度的协同集成在计算机、体外、体外和体内的数据来实现 神经网络(NN)。这将提供预测模型，当新数据变为 可用，是生物医学建模的范式转变。我们将开发多尺度/多保真度计算模型 (和相应的神经网络实现)，它链接跨越的亚细胞、细胞和血管级别的现象 在时空尺度上跨越四个数量级。这种比例耦合将通过使用 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 通过来自体外和体外实验的数据和收集的RBC数量特征建立计算模型 SCD患者。具体地说，我们将使用三种新的芯片上脾微流控装置，具有氧气控制和 我们的外国合作伙伴的独特的人脾灌流系统，目标如下：目标1：开发和 验证脾内皮细胞间缝隙滤过模型；目标2：开发新的红细胞-巨噬细胞黏附模型 和脾的吞噬作用；目标3：进行芯片上的脾实验和验证；目标4：验证 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和 脾在SCD中的保护作用。为我们的新多保真神经网络提供形态和 SCD患者红细胞循环功能测定将导致SCD患者残脾功能模型的建立， 这将有助于预测急性脾隔离危机的风险，并指导干细胞的最佳时机 移植或基因疗法。使用深度学习工具整合来自不同领域的数据的新范式 来源将适用于对许多其他血液疾病进行建模。

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.