CAREER: Mesoscale computational modeling of intracellular soft matter

职业：细胞内软物质的介观计算模型

• 批准号: 1552903
• 负责人: Anastasios Matzavinos
• 金额: $ 40万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552903&HistoricalAwards=false
• 关键词: CAREER; Mesoscale; computational; modeling; intracellular

Intracellular networks of entangled or cross-linked polymers, such as the actin and microtubule cytoskeleton, interact with and respond to mechanical cues from the environment, influencing how cells grow and divide, how stem cells differentiate into specific cell types, and how cancer cells proliferate, among other important molecular and cellular phenomena. In many cases, the structure of these networks is dynamically altered by the mechanical feedback of lipid membranes and cytoplasmic flows. However, the majority of current modeling and computational approaches focus on computational tractability at the expense of these mechanical feedbacks. A central theme of this project is that the development of physically realistic models of intracellular polymer networks (and other biological matter) requires a computational approach that efficiently integrates the mechanics of the network with the hydrodynamics of intracellular fluid flows and the mechanical interactions with biological membranes. Such integrative computational efforts are key to building a better understanding of intracellular phenomena. In particular, they hold the potential to help diagnose and treat cytoskeleton-related diseases and guide the development of stem cell techniques. The proposed work will help with the scientific and professional development of both graduate students and undergraduate students. It will also integrate excellent educational opportunities including the development of open education resources; promoting computer-coding literacy as a means to investigate physical phenomena through the development of an educational website with specialized computer coding activities pertaining to the proposed project; conducting boot camps to train undergraduate students on "Mesoscale computational modeling of intracellular soft matter" and; working with the Science Centre at Brown University to engage high school students from underrepresented communities in scientific computing and the physics of soft matter.The PI proposes a synergistic combination of approaches consisting of dissipative particle dynamics (DPD) simulations and stochastic homogenization methods. DPD can accurately model the molecular-scale motion of polymers and fluids without the computational burden that normally makes alternative simulations (e.g., molecular dynamics) unviable. Stochastic homogenization methods, on the other hand, provide the means of efficiently extracting bulk mechanical properties from the mesoscale DPD description. Specific aims of the project include (i) the development of specialized particle-based methods for investigating the mechanical interactions between cytoplasmic flows and intracellular matter, (ii) a computational and analytic investigation of stochastic homogenization approaches to developing mesoscopic mechanical descriptions of intracellular matter, (iii) applications of the developed algorithms to the computational modeling of specific examples of biological matter, such as the actin cytoskeleton and DNA macromolecules, and (iv) the clustering and classification of simulation data in order to characterize the dynamics of biological matter in the systems under investigation. Given the recent interest in intracellular polymer networks (e.g., the actin cytoskeleton) as therapeutic targets in a broad array of pathological conditions, including cancer, neurodegenerative diseases, and kidney disease, the proposed computational approach is expected to advance significant biomedical applications, especially therapeutic perturbations of cytoskeletal dynamics.

缠结或交联聚合物的细胞内网络，如肌动蛋白和微管细胞骨架，与来自环境的机械信号相互作用并对其作出反应，影响细胞如何生长和分裂，干细胞如何分化成特定细胞类型，以及癌细胞如何增殖，以及其他重要的分子和细胞现象。在许多情况下，这些网络的结构是动态改变的脂质膜和细胞质流动的机械反馈。然而，大多数当前的建模和计算方法集中在计算的易处理性，这些机械反馈的代价。该项目的一个中心主题是，细胞内聚合物网络（和其他生物物质）的物理现实模型的发展需要一种计算方法，有效地整合了细胞内流体流动的流体动力学和与生物膜的机械相互作用的网络的力学。这种综合计算的努力是建立一个更好地理解细胞内现象的关键。特别是，它们具有帮助诊断和治疗细胞因子相关疾病并指导干细胞技术发展的潜力。本文的工作将有助于研究生和本科生的科学和专业发展。计划亦会提供良好的教育机会，包括发展公开教育资源;透过发展一个教育网站，提供与计划有关的电脑编码活动，以推广电脑编码知识，作为研究物理现象的一种方法;举办靴子训练营，训练本科生“细胞内软物质的中尺度计算模型”;与布朗大学的科学中心合作，让来自科学计算和软物质物理学的代表性不足的社区的高中生参与进来。PI提出了一种由耗散粒子动力学（DPD）模拟和随机均匀化方法组成的方法的协同组合。DPD可以准确地模拟聚合物和流体的分子尺度运动，而无需通常进行替代模拟的计算负担（例如，分子动力学）不可行。随机均匀化方法，另一方面，提供了有效地提取散装力学性能的介观DPD描述的手段。该项目的具体目标包括：（i）开发专门的基于粒子的方法，用于研究细胞质流和细胞内物质之间的机械相互作用;（ii）对随机均匀化方法进行计算和分析研究，以开发细胞内物质的介观力学描述;（iii）将开发的算法应用于生物物质特定实例的计算建模，如肌动蛋白细胞骨架和DNA大分子，和（iv）模拟数据的聚类和分类，以表征生物物质的动力学系统正在调查。考虑到最近对细胞内聚合物网络的兴趣（例如，肌动蛋白细胞骨架）作为包括癌症、神经变性疾病和肾病在内的广泛病理状况中的治疗靶点，预期所提出的计算方法将推进重要的生物医学应用，特别是细胞骨架动力学的治疗扰动。