Multiscale Computational Methods with Applications in Materials Science and Tissue Engineering

多尺度计算方法在材料科学和组织工程中的应用

• 批准号: 1318866
• 负责人: Yi Sun
• 金额: $ 15.8万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318866&HistoricalAwards=false
• 关键词: Multiscale; Computational; Methods; Applications; Materials

In this project the investigator will develop new computational multiscale methods to model and simulate (a) epitaxial growth in materials science; (b) cellular aggregate fusion in organ biofabrication and tissue engineering. Even though the two problems arise from distinct disciplines, they all involve complex phenomena on multiple spatio-temporal scales across several orders of magnitude. Numerically solving these problems directly at the finest scale with standard algorithms inevitably leads to an enormous computational cost. Therefore, it is necessary to develop multiscale methods that share the efficiency of the macroscopic models while achieving the accuracy of the microscopic models. In our methods, the microscale dynamics is described by a lattice model based on the kinetic Monte Carlo algorithm, while ordinary/partial differential equations are used for modeling the macroscale dynamics on continuum level. There are several challenging issues to be addressed in the research: (1) a detailed understanding of the relation between the physical or biological models at different scale levels; (2) how to apply suitable boundary conditions and constraints for microscopic models; (3) systematic and accurate procedures of coarse-graining. Once these issues are addressed, the investigator will extend the ideas produced in this research to a broad range of relevant multiscale problems. This computational approach will also set up a paradigm for investigating (a) both homoepitaxial and heteroepitaxial growth using various material species; (b) cell fusion and tissue/organ fabrication in general by incrementally incorporating additional chemically significant protein dynamics.This research has the potential to significantly and positively impact relevant applications in materials science, tissue engineering, and cell biology. It can help us to (a) understand the physical and mechanical processes during epitaxial growth, which is an affordable method of high-quality crystal growth for many semiconductor materials and is important in nanotechnology and in semiconductor fabrication; (b) understand the origin of intercellular forces due to cellular responses in cells as well as tissue fusion and quantitatively characterize the mechanochemical process during tissue morphogenesis, accurately predict and optimize postprinting structure formation in organ biofabrication, and intelligently develop approaches in drug design and tissue engineering. Through collaborating with scientists and engineers from these disciplines, the investigator will develop a suitable computational multiscale framework for solving these problems to better understand complex phenomena in these systems. Educational impact includes interdisciplinary training of graduate and undergraduate students interested in computational mathematics. Special attention will be paid to underrepresented groups including minorities.

在这个项目中，研究者将开发新的计算多尺度方法来建模和模拟（a）材料科学中的外延生长;（B）器官生物制造和组织工程中的细胞聚集体融合。尽管这两个问题来自不同的学科，但它们都涉及多个时空尺度上的复杂现象，跨越几个数量级。用标准算法直接在最小尺度上数值求解这些问题不可避免地会导致巨大的计算成本。因此，有必要开发多尺度方法，共享宏观模型的效率，同时实现微观模型的精度。在我们的方法中，微尺度动力学描述的晶格模型的基础上的动力学蒙特卡罗算法，而常/偏微分方程用于建模的宏观尺度动力学的连续水平。在研究中有几个具有挑战性的问题需要解决：（1）在不同尺度水平上的物理或生物模型之间的关系的详细理解;（2）如何为微观模型应用合适的边界条件和约束;（3）系统和准确的粗粒化过程。一旦这些问题得到解决，研究人员将在这项研究中产生的想法扩展到广泛的相关多尺度问题。这种计算方法也将建立一个范例，调查（a）均质外延和异质外延生长使用各种材料物种;（B）细胞融合和组织/器官制造一般通过逐步纳入额外的化学显着的蛋白质动力学。这项研究有可能显着和积极的影响，在材料科学，组织工程和细胞生物学的相关应用。它可以帮助我们（a）理解外延生长过程中的物理和机械过程，这是一种经济实惠的方法，可以为许多半导体材料提供高质量的晶体生长，在纳米技术和半导体制造中非常重要;（B）理解由于细胞中的细胞反应以及组织融合引起的细胞间力的起源，并定量表征组织形态发生期间的机械化学过程，准确预测和优化器官生物制品中的打印后结构形成，并智能开发药物设计和组织工程方法。 通过与这些学科的科学家和工程师合作，研究人员将开发一个合适的计算多尺度框架来解决这些问题，以更好地理解这些系统中的复杂现象。教育影响包括对计算数学感兴趣的研究生和本科生的跨学科培训。将特别关注代表性不足的群体，包括少数群体。