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中文摘要
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描述(由申请人提供):许多疾病(包括心脏病、糖尿病、癌症和帕金森氏症等神经疾病)不能从单一原因/单一结果关系的角度进行理解。这是因为,尽管存在对基础生理学的深入了解,以及来自疾病动物模型的大量生理和基因组数据,但我们缺乏对多个基因和环境因素如何相互作用来决定表型的了解。我们建议基于对大鼠生理功能的系统多尺度测量、模拟和分析来彻底改变我们对复杂表型和疾病的理解。具体地说,我们建议启动虚拟生理学大鼠项目,以开发计算工具来捕获潜在的系统生理学以及与疾病相关的病理生理学扰动。这些工具将基于对大鼠多个器官系统的生理功能的实验表征而开发和验证,这些大鼠品系被设计成显示相关的疾病表型。计算机模拟将用于整合不同的数据(基因组、解剖学、生理学等)。解释和预测功能,并将动物模型的发现转化为关于人类特定的相互关联的复杂疾病的新信息,包括高血压、肾脏疾病、心力衰竭和代谢综合征。开发的多尺度生理模型将与基因型-表型参数图相关联,以构建虚拟生理学大鼠资源,该资源将用于预测遗传变异性和环境因素对表型的影响,并预测将通过实验获得和表征的新品系的表型。通过系统和迭代地使用多尺度计算模型来分析数据、生成假设、设计实验和预测新品系大鼠的表型,我们将获得预测和理解复杂性状出现的能力。除了拟议的科学研究的直接影响外,VPR中心还将通过为心血管系统研究提供独特的软件和相关数据,向更广泛的社区提供资源。此外,我们将开发课程、研讨会和相关教材,从服务不足的社区培训和招聘科学家,并为附属和非附属研究人员举办年度科学会议。 相关性:尽管我们对基本的心血管生理学有深入的了解,但我们对多个基因和环境因素如何相互作用决定心血管表型缺乏基本的了解。这项建议旨在通过实验和模拟来捕捉复杂的基因型-环境-表型关系,以了解复杂的多方面疾病表型。
英文摘要
DESCRIPTION (provided by applicant): Many diseases (including heart disease, diabetes, cancer, and neurological disorders such as Parkinson's disease) cannot be understood in terms of single-cause/single-effect relationships. This is because although there exist both a depth of knowledge of basic physiology and a host of physiological and genomic data from animal models of disease, we lack an understanding of how multiple genes and environmental factors interact to determine phenotype. We propose to revolutionize our understanding of complex phenotypes and diseases based on systematic multi-scale measurement, simulation, and analysis of physiological function in the rat. Specifically, we propose to initiate The Virtual Physiological Rat Project to develop computational tools to capture the underlying systems physiology as well as the pathophysiological perturbations associated with disease. These tools will be developed and validated based on experimental characterization of physiological function across a number of organ systems in rat strains engineered to show relevant disease phenotypes. Computer simulation will be used to integrate disparate data (genomic, anatomic, physiological, etc.) to explain and predict function, and to translate the findings from animal models to yield new information on specific interrelated complex diseases in humans, including hypertension, kidney disease, heart failure, and metabolic syndrome. The developed multi-scale physiological models will be linked to genotype- phenotype parametric maps to construct a Virtual Physiological Rat resource, which will be used to predict the influence of genetic variability and environmental factors on phenotypes and to predict phenotypes of new strains that will be experimentally derived and characterized. By systematically and iteratively using multi-scale computational models to analyze data, generate hypotheses, design experiments, and predict phenotypes in novel strains of rat, we will attain the capability to predict and understand the emergence of complex traits. In addition to the direct impact of the proposed scientific studies, the VPR Center will be a resource to the broader community by delivering unique software and associated data for cardiovascular systems research. In addition, we will develop courses, workshops, and related educational material, train and recruit scientists from underserved communities, and hold annual scientific meetings for affiliated and nonaffiliated investigators. RELEVANCE: Despite a depth of knowledge of basic cardiovascular physiology, we lack even a rudimentary understanding of how multiple genes and environmental factors interact to determine cardiovascular phenotype. This proposal targets the grand challenge of understanding complex multi-faceted disease phenotypes through experiments and simulations that capture the complex genotype-environment-phenotype relationship.
期刊论文(67)
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会议论文
DOI: 10.1007/s10237-014-0563-y
发表时间: 2014-10
期刊: BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
影响因子: 3.5
作者: [Qureshi, M. Umar, Vaughan, Gareth D. A., Sainsbury, Christopher, Johnson, Martin, Peskin, Charles S., Olufsen, Mette S., Hill, N. A.]
通讯作者: Hill, N. A.
Structural correlation method for model reduction and practical estimation of patient specific parameters illustrated on heart rate regulation.
用于模型简化和实际估计患者特定参数(以心率调节为例)的结构相关方法。
DOI: 10.1016/j.mbs.2014.07.003
发表时间: 2014
期刊: Mathematical biosciences
影响因子: 4.3
作者: [Ottesen,JohnnyT, Mehlsen,Jesper, Olufsen,MetteS]
通讯作者: Olufsen,MetteS
DOI: 10.3389/fbioe.2014.00079
发表时间: 2014
期刊: Frontiers in bioengineering and biotechnology
影响因子: 5.7
作者: [Nickerson DP, Ladd D, Hussan JR, Safaei S, Suresh V, Hunter PJ, Bradley CP]
通讯作者: Bradley CP
Towards causally cohesive genotype-phenotype modelling for characterization of the soft-tissue mechanics of the heart in normal and pathological geometries.
建立因果内聚的基因型-表型模型,用于表征正常和病理几何形状中心脏的软组织力学。
DOI: 10.1098/rsif.2014.1166
发表时间: 2015
期刊: Journal of the Royal Society, Interface
影响因子: --
作者: [Nordbø,Øyvind, Gjuvsland,ArneB, Nermoen,Anders, Land,Sander, Niederer,Steven, Lamata,Pablo, Lee,Jack, Smith,NicolasP, Omholt,StigW, Vik,JonOlav]
通讯作者: Vik,JonOlav
共 44 条
    Systems and Integrative Biology Training Program
    Disentangling the Mechanisms of Coronary Blood Flow Regulation through Multi-scale Modeling
    Computational systems analysis of cardiac mechanical-energetic coupling in heart disease
    Computational systems analysis of cardiac mechanical-energetic coupling in heart disease
    海外基金