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

职业：多尺度随机模拟与可视化工具的复杂生化系统本项目旨在开发有效的模拟技术的随机生化模型，特别是细胞周期模型。细胞周期是活细胞复制其组分并将其在两个子细胞之间分裂的事件序列，因此每个子细胞都具有重复该过程所需的信息和机制。细胞周期与心血管疾病、肿瘤等多种疾病有关。了解细胞周期调控的分子机制是当代细胞生物学的主要挑战。生物学家已经在芽殖酵母、裂殖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。这些系统是如此复杂，其模拟和分析提出了巨大的挑战，计算科学。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，以整合连续，离散，确定性和随机模型的全部范围，并支持不同模型和算法之间的动态，无缝和自动切换。本项目的重点是三个具体目标。其主要目的是开发创新的计算算法和数学理论的一个关键的多尺度挑战：刚度。本计画将发展化学反应系统离散随机模拟中的刚度理论，并借由运行时间剖面分析，发展自动刚度侦测演算法。第二个目标是开发混合算法来模拟具有多态物种的生物系统，这在具有多个结合位点的生物系统中是一个特殊的挑战。该项目将开发混合方法，以结合联合收割机基于粒子的方法，设计用于多态物种，和基于群体的方法，设计用于一般化学反应。 本计画的第三个目标是发展演算法与模型的视觉化工具，以介绍演算法与模型在计算生物学中的发展给研究生与本科生。该项目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，并将直接有利于系统生物学的整个研究学科。此外，刚度相关技术也适用于其他领域复杂系统的多尺度仿真。该研究项目还为计算机科学，数学和生物学学科的学生提供学习机会和培训。生物模型和模拟方法将在计算细胞生物学的研究生课程中介绍。算法可视化工具将帮助学生理解刚度的重要计算概念。通过与拉德福大学数学系教授合作，该模型可视化工具和与细胞周期模型相关的结果将用于弗吉尼亚理工大学和拉德福大学的本科研究和教育。这种合作将有助于吸引更多的女性和少数民族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.