Career: Multiscale Stochastic Simulation for Complex Biochemical Systems with Visualization Tools

本项目旨在为随机生化模型，特别是细胞周期模型开发有效的模拟技术。细胞周期是一个活细胞复制其组成部分并将其分成两个子细胞的一系列事件，这样每个子细胞都有必要的信息和机制来重复这个过程。细胞周期与心血管疾病、癌症等多种疾病有关。了解调控细胞周期的分子机制是当代细胞生物学的一个重大挑战。生物学家已经在出芽酵母、裂变酵母和哺乳动物细胞中建立了复杂的细胞周期控制数学模型。这些系统非常复杂，其模拟和分析对计算科学提出了巨大的挑战。PI的职业目标是通过开发创新的计算方法和严格的数学理论来解决这些挑战，以整合全范围的连续、离散、确定性和随机模型，并支持根据潜在问题的规模在不同模型和算法之间动态、无缝和自动切换。本项目侧重于三个具体目标。主要目的是发展创新的计算算法和数学理论，以解决关键的多尺度挑战：刚度。本项目将发展化学反应系统离散随机模拟中的刚度理论，并通过运行时剖面分析开发一种自动刚度检测算法。第二个目标是开发混合算法来模拟具有多状态物种的生物系统，这是具有多个结合位点的生物系统中的一个特殊挑战。该项目将开发混合方法，结合针对多状态物种设计的基于粒子的方法和针对一般化学反应设计的基于种群的方法。该项目的第三个目标是开发算法和模型可视化工具，向研究生和本科生介绍计算生物学中的算法和模型开发。在这个项目中开发的算法将使生物学家能够有效地建模和模拟多尺度系统，并将直接使系统生物学的整个研究学科受益。此外，有关刚度的技术也适用于其他领域复杂系统的多尺度模拟。该研究项目还为计算机科学、数学和生物学等学科的学生提供学习机会和培训。计算细胞生物学的研究生课程将介绍生物模型和模拟方法。算法可视化工具将帮助学生理解刚度的重要计算概念。模型可视化工具和与细胞周期模型相关的结果将通过与Radford大学数学系教授的合作，用于弗吉尼亚理工大学和Radford大学的本科生研究和教育。这种合作将有助于吸引更多的女性和少数族裔学生进入计算科学领域。

Career: Multiscale Stochastic Simulation for Complex Biochemical Systems with Visualization ToolsThis project aims to develop efficient simulation techniques for stochastic biochemical models, particularly the cell cycle model. Cell cycle is the sequence of events whereby a living cell replicates its components and divides them between two daughter cells, so that each daughter has the information and machinery necessary to repeat the process. Cell cycle is related to many diseases such as cardiovascular diseases and cancer. Understanding the molecular mechanisms regulating cell cycle is a major challenge of contemporary cell biology. Biologists have developed complex mathematical models of cell-cycle control in budding yeast, fission yeast, and mammalian cells. These systems are so complex that its simulation and analysis present great challenges to computational science. The career goal of the PI is to address these challenges by developing innovative computational methods and rigorous mathematical theories to integrate the full gamut of continuous, discrete, deterministic, and stochastic models, and support dynamic, seamless and automatic switching between different models and algorithms as dictated by the scales of underlying problems. This project focuses on three specific aims in this project. The primary aim is to develop innovative computational algorithms and mathematical theories about a critical multiscale challenge: stiffness. This project will develop the theory of the stiffness in discrete stochastic simulation of chemically reacting systems and an automatic stiffness detection algorithm through a running-time profile analysis. The second aim is to develop hybrid algorithms to simulate biological systems with multistate species, a special challenge in biological systems with multiple binding sites. This project will develop hybrid methods to combine particle-based methods, designed for multistate species, and population-based methods, designed for general chemical reactions. The third aim of this project is to develop algorithm and model visualization tools to introduce the algorithms and model development in computational biology to graduate and undergraduate students. The algorithms developed in this project will enable biologists to efficiently model and simulate multiscale systems and will directly benefit the whole research discipline of systems biology. Moreover, the techniques about the stiffness are also applicable to multiscale simulation of complex systems in other areas. This research project also provides learning opportunities and training for students across the disciplines of computer science, mathematics, and biology. The biological models and simulation methods will be introduced in graduate courses on computational cell biology. The algorithm visualization tool will help students understand the important computational concept of stiffness. The model visualization tool and results related to the cell cycle model will be used in undergraduate research and education in Virginia Tech and Radford University, through collaboration with a professor in the Mathematics department at Radford University. This collaboration will help to attract more women and minority students into computational science areas.