Stochastic dynamics for multiscale biology

多尺度生物学的随机动力学

基本信息

批准号：
7596501
负责人：
ERIC D MJOLSNESS
金额：
$ 31.91万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-01 至 2012-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7596501
关键词：
Affinity Algorithms Behavior Binding Binding Sites Biological Biological Models Biology Chemicals Chromosome Pairing Complex Computing Methodologies DNA Damage Dendritic Spines Diffusion Drug Formulations Equation Equilibrium Evolution Failure Feedback Free Energy Future Graph Human Intervention Investigation Laws Learning M cell Machine Learning Malignant Neoplasms Mathematics Measurement Medical Memory Methods Modeling Molecular Neurobiology Neurodegenerative Disorders Neuronal Plasticity Numbers Pathway interactions Physics Play Process Production Purpose Rate Reaction Role Sampling Scheme Semantics Signal Transduction Simulate Site Speed Stochastic Processes Surface Synapses System TP53 gene Techniques Testing Time Transcriptional Regulation Validation Vertebral column Work Yeasts anticancer research base concept improved indexing interest mathematical model models and simulation multi-scale modeling next generation novel quantum reaction rate repaired simulation spatiotemporal syntax theories

项目摘要

DESCRIPTION (provided by applicant): Complex biological systems are increasingly subject to investigation by mathematical modeling in general and stochastic simulation in particular. Advanced mathematical methods will be used to generate next-generation computational methods and algorithms for (1) formulating these models, (2) simulating or sampling their stochastic dynamics, (3) reducing them to simpler approximating models for use in multiscale simulation, and (4) optimizing their unknown or partly known parameters to fit observed behaviors and/or measurements. The proposed methods are based on advances in applied statistical and stochastic mathematics, including advances arising from operator algebra, quantum field theory, stochastic processes, statistical physics, machine learning, and related mathematically grounded fields. A central technique in this work will be the use of the operator algebra formulation of the chemical master equation. The biological systems to be studied include and are representative of high-value biomedical target systems whose complexity and spatiotemporal scale requires improved mathematical and computational methods, to obtain the scientific understanding underlying future medical intervention. Cancer research is broadly engaged in signal transduction systems and complexes with feedback, for which the yeast Ste5 MARK pathway is a model system. DNA damage sensing (through ATM) and repair control (though p53 and Mdm2) are at least equally important to cancer research owing to the central role that failure of these systems play in many cancers. The dendritic spine synapse system is central to neuroplasticity and therefore human learning and memory. It is critical to understand this neurobiological system well enough to protect it against neurodegenerative diseases and environmental insults. The project seeks fundamental mathematical breakthroughs in stochastic and multiscale modeling that will enable the scientific understanding of these complex systems necessary to create effective medical interventions of the future.

描述（由申请人提供）：复杂的生物系统越来越多地通过一般数学建模，尤其是随机模拟进行研究。先进的数学方法将用于生成（1）制定这些模型的下一代计算方法和算法，（2）对其随机动态进行模拟或取样，（3）将它们简化为更简单的近似模型，用于在多构型模拟中使用，以及（4）以未知或部分已知或部分的参数为拟合的量表，以优化可观的观察行为和/或/或或/或或/或或/或或/或或或/或或或或或或一位或/或/或/或/或或或/或或或/或或或或或点约为。所提出的方法基于应用统计和随机数学的进步，包括源自操作员代数，量子场理论，随机过程，统计物理学，机器学习以及相关的数学基础领域的进步。这项工作中的一种中心技术将是使用化学主方程的操作员代数公式。要研究的生物系统包括并代表高价值生物医学目标系统的复杂性和时空尺度需要改进的数学和计算方法，以获得基本的未来医疗干预的科学理解。癌症研究广泛地参与了信号转导系统和复合物，并具有反馈，酵母Ste5 Mark途径是模型系统。 DNA损伤感应（通过ATM）和修复控制（尽管p53和MDM2）至少对癌症研究至少重要，因为这些系统在许多癌症中的失败起着的核心作用。树突状脊柱突触系统是神经可塑性的核心，因此是人类的学习和记忆。充分了解这种神经生物学系统以保护其免受神经退行性疾病和环境侮辱至关重要。该项目寻求随机和多尺度建模的基本数学突破，这将使对这些复杂系统的科学理解，以创造有效的未来医疗干预措施。