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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.

项目总结。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推动这些动态的力量。然而，大多数生物过程涉及的时间尺度比数值积分的时间步长要长得多。虽然有很多方法可以用来弥合这种时间尺度的分离以获得均衡平均值，需要进一步的进展才能强劲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一般方法并将其应用于阐明分子和细胞长度尺度上的自组装机制。通过短期模拟的罕见事件分析改进胰岛素疗法。糖尿病是一种流行病糖尿病，在全球范围内耗资巨大。主要的治疗方法是胰岛素治疗，但它的治疗范围很窄它的冷藏运输和储存所需费用对世界大部分地区来说都高得令人望而却步。胰岛素类似物已被设计成具有特定的ﬁc药代动力学，其基础是胰岛素自身的知识。但对胰岛素如何与胰岛素受体(IR)结合仍缺乏了解。我们寻求开发能够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胰岛素和单链胰岛素结合的IR结构相结合 (SCI)类似物，以阐明观察到的治疗活动的微观来源。因此，该研究最终可以导致胰岛素疗法的改进。我们还将研究SCI类似物的改进的热性能，在特别是，它们形成淀粉样蛋白ﬁBrins的倾向降低了。因此，这项研究也有望获得对淀粉样蛋白的形成，从胰岛素到帕金森氏症和神经退行性疾病具有广泛的意义阿尔茨海默病。对导致发育模式的细胞骨架过程进行建模。细胞骨架动力学是不同的基础过程，包括发育模式，神经元突触形成，免疫识别，创伤治愈和肿瘤生长。这些动态可能很难直觉，因为它们涉及到我的平衡- 机械力、机械力化学、网络组装和分裂，以及细胞信号的反馈分子。因此，模型在解析起作用的分子过程和测试量子过程中起着重要作用。推测性假设。我们将采用一个最近参数化的细胞骨架模型，该模型可以定量预测体外实验，以阐明体内发育模式的机制。也就是说，我们将调查交互是如何在小GTP酶RhoA和肌动蛋白组装/分散之间可控制搏动性收缩，广泛存在导致皮质ﬂ下降、细胞形状改变和组织变形的现象。然后我们将比较模型对于进化保守的RNA结合蛋白Staufen在前后部物种中的定位 ﬁ阳离子。除了有助于理解这些关键的发育过程外，模拟还将产生一个该模型可用于在具有最小Modiﬁ阳离子的大范围上下文中研究细胞骨架动力学。