Hybrid Kinetic Monte Carlo Methods with Applications in Biofabrication and Epidemics

混合动力学蒙特卡罗方法在生物制造和流行病中的应用

• 批准号: 2208467
• 负责人: Yi Sun
• 金额: $ 17.63万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208467&HistoricalAwards=false
• 关键词: Hybrid; Kinetic; Monte; Carlo; Methods

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread throughout the world. By July 30, 2022, more than 577 million cases and over 6.4 million fatalities have been reported, making it one of the deadliest pandemics to date. Over recent decades other epidemics such as HIV/AIDS, hepatitis, SARS, H1N1 swine flu, Ebola, Zika, measles, malaria, and tuberculosis have also caused tens of millions of deaths. To combat the pandemics of emergent and re-emergent infectious diseases, mathematical modeling and simulation plays an important role in predicting, assessing, and controlling potential outbreaks. Endothelial network formation is a critical process in fabricating functional vascular constructs, and it is a very crucial impediment in the state-of-the-art biofabrication technology. The goal of this project is to develop new hybrid kinetic Monte Carlo (KMC) methods and algorithms for the study of complex biological systems and problems arising in epidemics and biofabrication. This project will provide multidisciplinary research training to graduate and undergraduate students interested in both computational mathematics and mathematical biology/computational epidemiology.In the thrust (1), to study spatial and temporal dynamics of epidemics, the PI proposes to develop a hybrid KMC method with focuses on the mobility and heterogeneity of individuals and spatial structures as critical aspects in determining possible outcomes for an epidemic. The proposed model describes the motion of individuals on a discrete lattice with moving rates between neighboring sites and couple the SIR-like transition mechanism to describe the conversion between individuals' health states. In the thrust (2), the PI plans to develop another hybrid KMC model with focuses on cell motility, cell-matrix and cell-cell interactions in endothelial network formation. The approach combines a hybrid mechanochemical model with the KMC algorithm to enhance the capability of the computational modeling platform in studying cell-cell and cell-matrix interactions significantly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）引起的2019冠状病毒病（COVID-19）已在全球迅速蔓延。截至2022年7月30日，已报告超过5.77亿例病例和超过640万例死亡，使其成为迄今为止最致命的流行病之一。近几十年来，艾滋病毒/艾滋病、肝炎、SARS、H1N1猪流感、埃博拉病毒、寨卡病毒、麻疹、疟疾和结核病等其他流行病也造成数千万人死亡。为了应对突发和再突发传染病的大流行，数学建模和仿真在预测、评估和控制潜在爆发方面发挥着重要作用。内皮网络的形成是制造功能性血管结构的关键过程，并且它是最先进的生物制造技术中非常关键的障碍。该项目的目标是开发新的混合动力学蒙特卡罗（KMC）方法和算法，用于研究复杂的生物系统和流行病和生物制造中出现的问题。本项目将为对计算数学和数学生物学/计算流行病学感兴趣的研究生和本科生提供多学科的研究培训。在主旨（1）中，为了研究流行病的空间和时间动态，PI建议开发一种混合KMC方法，重点关注个人和空间结构的移动性和异质性，这是确定流行病可能结果的关键方面。该模型描述了个体在离散格点上的运动，相邻格点之间的运动速率为零，并结合类SIR转换机制描述了个体健康状态之间的转换。在推力（2）中，PI计划开发另一种混合KMC模型，重点关注内皮网络形成中的细胞运动性、细胞-基质和细胞-细胞相互作用。该方法结合了混合机械化学模型与KMC算法，以提高计算建模平台在研究细胞与细胞和细胞与基质相互作用显着的能力。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Application of the dynamic Monte Carlo method to pedestrian evacuation dynamics

• DOI: 10.1016/j.amc.2023.127876
• 发表时间: 2023-05
• 期刊: Appl. Math. Comput.
• 作者: Nutthavuth Tamang;Yi Sun

Accelerated kinetic Monte Carlo methods for general nonlocal traffic flow models

一般非局部交通流模型的加速动力学蒙特卡罗方法

• DOI: 10.1016/j.physd.2023.133657
• 发表时间: 2023
• 期刊: Physica D: Nonlinear Phenomena
• 作者: Sun, Yi;Tan, Changhui
• 通讯作者: Tan, Changhui