CAREER: Modeling, Analysis and Computation of Stochastic Intracellular Reactions

职业：随机细胞内反应的建模、分析和计算

• 批准号: 0845061
• 负责人: Di Liu
• 金额: $ 41万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845061&HistoricalAwards=false
• 关键词: CAREER; Modeling; Analysis; Computation; Stochastic

The main objective of the proposed research is toprovide simplified dynamics and design efficient numerical methods forcomplex intracellular stochastic chemical reacting networks. Numericalschemes will be developed to simulate systems exhibiting multiple timeand concentration scales, and to solve transition paths and transitionrates for systems with metastability. Error analysis of the schemes aswell as sharper estimates on the transition rates will beprovided. Applications will be focused on developing and analyzing aquantitative model for the Insulin response network, throughcollaboration with biologists at Michigan State University (MSU). Theeducation plan includes training of graduate students that will leadto Ph.D. theses, developing a graduate level course for students inapplied and computational mathematics, introducing the related topicsto undergraduate curriculum, and organizing a workshop on the same topic.The proposed research will be crucial to the understanding offunctional issues of intracellular reacting networks at the systemlevel, which is becoming the new focus of genomic research. Numerical studies of the multi-scale systems will lead to new insights into the simulationschemes for stochastic systems. The development of accurate andefficient numerical methods for multi-scale chemical reaction systemsinvolves novel analytical approaches from stochastic analysis. The studyof the fluctuation driven transitions in metastable chemical reactionsystems will find new applications for advanced probability theory andoptimization techniques. The multi-scale and stochastic methodsdeveloped by the proposed research will find a wide range ofapplications in biological sciences and nano-technologies.The PI is going to build mathematical models and design efficientcomputer based simulation algorithms for chemical reactions insideliving cells, which ususally involve many types of reacting channelsand reacting species, as well as random fluctuations. The proposedresearch is to simplify and reduce complex models, which will lead toinsightful conceptualization of the system. The research will beconducted in collaboration with biologists and the theoretical resultswith be calibrated with real data generated by experiments inlabs. The targeted application, namely the modeling of Insulinresponse network, has a significant potential for the promotion ofpublic health. Type 2 diabetes and impaired glucose tolerance are topcauses of morbidity and mortality in the United States. In the case ofinsulin resistance, tissues such as muscle, fat and liver become lessresponsive or resistant to insulin. The disorder is also linked toother common health problems, such as obesity, hyperlipidaemia,hypertension etc. The models and algorithms developed by the PI willhelp to dandify the major reactions and reactants that are maintainingthe normal range of plasma glaucous in individuals throughout periodsof feeding and fasting. The research output will help to understandthe mechanism of the Insulin disorders and the cause of type2 diabetes.

本文的主要目的是为复杂的细胞内随机化学反应网络提供简化的动力学模型和设计有效的数值方法。数值方案将被开发来模拟具有多个时间和浓度尺度的系统，并解决亚稳态系统的过渡路径和过渡率。将提供该方案的误差分析以及对转移率的更精确的估计。通过与密歇根州立大学（MSU）的生物学家合作，应用将集中在开发和分析胰岛素反应网络的定量模型上。教育计划包括培养研究生，这些研究生将获得博士学位。本论文的主要工作包括：为应用数学和计算数学的研究生开设一门研究生水平的课程，将相关课题引入本科生课程，并组织一个关于同一主题的研讨会，这将对在系统水平上理解细胞内反应网络的功能问题至关重要，细胞内反应网络正成为基因组学研究的新热点。多尺度系统的数值研究将为随机系统的模拟提供新的思路。多尺度化学反应系统的精确和高效数值方法的发展涉及到随机分析的新的分析方法。研究亚稳化学反应体系中涨落驱动的跃迁将为高级概率论和优化技术找到新的应用。本研究所发展的多尺度随机方法将在生物科学和纳米技术中得到广泛的应用，PI将为递送细胞内的化学反应建立数学模型并设计有效的基于计算机的模拟算法，这些化学反应通常涉及多种类型的反应通道和反应物种，以及随机波动。提出的研究是简化和减少复杂的模型，这将导致系统的深刻概念化。这项研究将与生物学家合作进行，理论结果将与实验室实验产生的真实的数据进行校准。针对性的应用，即胰岛素反应网络的建模，对促进公共健康有着巨大的潜力. 2型糖尿病和糖耐量受损是美国发病率和死亡率的主要原因。在胰岛素抵抗的情况下，肌肉、脂肪和肝脏等组织对胰岛素的反应减弱或产生抵抗。这种疾病也与其他常见的健康问题有关，如肥胖、糖尿病、高血压等。PI开发的模型和算法将有助于改进在进食和禁食期间个体血浆白蛋白正常范围内的主要反应和反应物。研究结果将有助于了解胰岛素紊乱的机制和2型糖尿病的病因。