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Rare-event simulation and analysis for elucidating mechanisms of development and disease

用于阐明发育和疾病机制的罕见事件模拟和分析

基本信息

批准号：
10611995
负责人：
Aaron Dinner
金额：
$ 32.96万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10611995
关键词：
Actins Alzheimer&apos s Disease Amyloid Fibrils Anterior Binding Biological Biological Process Cell Shape Complement Computing Methodologies Coupled Cytoskeletal Modeling Cytoskeleton Defect Development Developmental Process Diabetes Mellitus Disease Drug Kinetics Engineering Equilibrium Event Feedback Grain Health Immunologics In Vitro Insulin Insulin Receptor Knowledge Length Life Methods Microscopic Mission Modeling Modification Molecular Monomeric GTP-Binding Proteins Neurodegenerative Disorders Neurons Parkinson Disease Pattern Play Process Property RNA-Binding Proteins Reaction Refrigeration Research Resolution Role Signal Transduction Signaling Molecule Specific qualifier value Structure Testing Therapeutic Therapeutic Index Time Tissues United States National Institutes of Health amyloid formation analog cost design disability experimental study improved in vivo insight mechanical force pandemic disease receptor receptor binding self assembly simulation statistics synaptogenesis tumor growth wound healing

项目摘要

PROJECT SUMMARY. Molecular simulations complement experiments by revealing the microscopic dynamics underlying biological mechanisms and the forces promoting those dynamics. However, most biological processes involve time scales much longer than the time step of numerical integration. While there are many methods for bridging this separation of time scales to obtain equilibrium averages, further advances are needed to robustly estimate dynamical statistics. The proposed research seeks to develop general methods that can meet this need and to apply them to elucidating self-assembly mechanisms at both molecular and cellular length scales. Improving insulin therapies through rare-event analyses of short simulations. There is a pandemic in diabetes mellitus, with tremendous cost worldwide. The main treatment is insulin therapy, but it has a narrow therapeutic index, and its requirement for refrigerated transport and storage is prohibitively costly for much of the world. Insulin analogs have been engineered to have speciﬁc pharmacokinetics based on knowledge of insulin self- association, but an understanding of how insulin binds to the insulin receptor (IR) remains lacking. We seek to develop computational methods that can enable simulation and analysis of coupled folding and binding reactions and to combine these methods with recently obtained structures of IR bound to insulin and single-chain insulin (SCI) analogs to elucidate the microscopic origins of observed therapeutic activities. The study can thus ultimately lead to improved insulin therapies. We will also investigate the improved thermal properties of SCI analogs, in particular, their reduced tendency to form amyloid ﬁbrils. The study thus also promises to yield insights into amyloid formation, with broad implications beyond insulin to neurodegenerative disorders like Parkinson's and Alzheimer's diseases. Modeling cytoskeletal processes leading to developmental patterning. Cytoskeletal dynamics underlie diverse processes, including developmental patterning, neuronal synapse formation, immunological recognition, wound healing, and tumor growth. These dynamics can be very hard to intuit because they involve balances of me- chanical forces, mechanochemistry, network assembly and dissasembly, and feedback to and from cell signaling molecules. Models thus play an important role in parsing contributing molecular processes and testing quanti- tative hypotheses. We will adapt a recently parameterized cytoskeletal model that is quantitatively predictive in vitro to elucidate mechanisms of developmental patterning in vivo. Namely, we will investigate how interactions between the small GTPase RhoA and actin assembly/dissasembly control pulsatile contractility, a widespread phenomenon that drives cortical ﬂow, cell shape change, and tissue deformation. Then we will compare models for the localization of the evolutionarily-conserved RNA-binding protein Staufen during anterior-posterior speci- ﬁcation. In addition to aiding in understanding these key developmental processes, the simulations will yield a model that can be used to study cytoskeletal dynamics in a broad range of contexts with minimal modiﬁcation.

项目摘要。分子模拟通过揭示微观动力学来补充实验潜在的生物机制和促进这些动态的力量。然而，大多数生物过程涉及的时间尺度远长于数值积分的时间步长。虽然有很多方法可以弥合这种时间尺度的分离以获得均衡平均值，需要进一步的进步来稳健地估计动态统计数据。拟议的研究旨在开发能够满足这一需求的通用方法并将它们应用于阐明分子和细胞长度尺度的自组装机制。通过短期模拟的罕见事件分析改进胰岛素治疗。糖尿病正在流行 mellitus，在全球范围内付出了巨大的代价。主要治疗方法是胰岛素治疗，但治疗范围狭窄指数，其对冷藏运输和储存的要求对于世界大部分地区来说成本高昂。基于胰岛素自身的知识，胰岛素类似物被设计为具有特定的药代动力学。关联，但仍缺乏对胰岛素如何与胰岛素受体（IR）结合的了解。我们力求开发可以模拟和分析耦合折叠和结合反应的计算方法并将这些方法与最近获得的与胰岛素和单链胰岛素结合的IR结构相结合（SCI）类似物阐明了观察到的治疗活性的微观起源。因此，这项研究最终可以从而改善胰岛素治疗。我们还将研究 SCI 类似物改进的热性能，特别是，它们形成淀粉样原纤维的倾向降低。因此，这项研究也有望深入了解淀粉样蛋白的形成，其广泛的影响不仅限于胰岛素，还包括帕金森病和帕金森病等神经退行性疾病阿尔茨海默病。对导致发育模式的细胞骨架过程进行建模。细胞骨架动力学是多种机制的基础过程，包括发育模式、神经元突触形成、免疫识别、伤口愈合和肿瘤生长。这些动态可能很难凭直觉理解，因为它们涉及我的平衡—— 机械力、机械化学、网络组装和拆卸以及细胞信号传导的反馈分子。因此，模型在解析贡献分子过程和测试定量方面发挥着重要作用。假设。我们将采用最近参数化的细胞骨架模型，该模型可以定量预测体外阐明体内发育模式的机制。也就是说，我们将研究交互如何小 GTP 酶 RhoA 和肌动蛋白组装/分解控制脉动收缩性之间的关系，这是一种广泛的驱动皮质流动、细胞形状变化和组织变形的现象。然后我们将比较模型用于在前后特异性过程中进化保守的RNA结合蛋白Staufen的定位网络化。除了帮助理解这些关键的发育过程之外，模拟还将产生该模型可用于在广泛的背景下以最小的修改来研究细胞骨架动力学。