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

• 批准号: 10396476
• 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
• 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/10396476
• 关键词: Actins; Alzheimer' s Disease; Amyloid Fibrils; Anterior; Binding; Biological; Biological Process; Cell Shape; Complement; Computing Methodologies; Coupled; Cytoskeletal Modeling; Defect; Development; Developmental Process; Diabetes Mellitus; Disease; Drug Kinetics; Engineering; Equilibrium; Event; Feedback; Grain; Health; Immunologics; In Vitro; Insulin; Insulin Receptor; Knowledge; Lead; 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; Research; Resolution; Role; Signal Transduction; Signaling Molecule; Structure; Testing; Therapeutic; Therapeutic Index; Time; Tissues; United States National Institutes of Health; amyloid formation; analog; base; cost; design; disability; experimental study; improved; in vivo; insight; pandemic disease; receptor; receptor binding; self assembly; simulation; statistics; synaptogenesis; tumor growth; wound healing

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。 然而，大多数生物过程的基本生物机制和促进这些动态的力量。 涉及的时间尺度比数值积分的时间步长得多，尽管有很多方法。 桥接这种时间尺度的平衡分离以获得平均值，需要进一步的进步来稳健地 估计动态统计数据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善胰岛素治疗 糖尿病正在流行。 胰岛素治疗是一种在全球范围内花费巨大的治疗方法，但其治疗范围较窄。 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，但我们仍然缺乏对胰岛素如何与胰岛素受体（IR）结合的了解。 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）类似物可以阐明观察到的治疗活性的微观起源。 我们还将研究 SCI 类似物的热性能的改善。 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提供一些见解。 淀粉样蛋白的形成，其影响范围不仅限于胰岛素，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。 细胞骨架过程的建模导致发育模式的形成是多种细胞骨架动力学的基础。 过程，包括发育模式、神经元突触形成、免疫识别、伤口 这些动态可能很难凭直觉理解，因为它们涉及到我的平衡。 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 因此，模型在解析分子过程和定量测试方面发挥着重要作用。 我们将采用最近的参数化细胞骨架模型，该模型可以定量预测。 体外阐明体内发育模式的机制，即，我们将研究如何相互作用。 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，这是一种广泛存在的现象 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 除了帮助理解这些关键的发展过程之外，模拟还将产生一个结果。 该模型可用于在广泛的背景下研究细胞骨架动力学，只需进行最小的修改。