Physiological Knowledge Integration and Recombinant Modeling Via Accelerated Sema

通过加速 Sema 进行生理知识整合和重组建模

• 批准号: 8750569
• 负责人: BRIAN E. CARLSON
• 金额: $ 53.11万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8750569
• 关键词: Address; Biological; Biological Models; Biological Process; Cardiovascular Models; Cataloging; Catalogs; Clinical; Code; Collaborations; Comprehension; Custom; Data; Data Set; Decision Making; Dependency; Development; Disease; Educational workshop; Effectiveness; Engineering; Etiology; Exercise; Experimental Models; Foundations; Goals; Hand functions; Human; Hypertension; Individual; Institution; International; Knowledge; Laboratories; Laws; Lead; Manuals; Medical; Medicine; Methods; Modeling; Neck; Ontology; Organism; Pathology; Patients; Physiological; Physiology; Process; Rattus; Recombinants; Research Personnel; Science; Scientist; Semantics; Simulate; System; Testing; Time; United States National Institutes of Health; Validation; Work; base; biological research; case-by-case basis; computer code; improved; mathematical analysis; meetings; model design; model development; multi-scale modeling; novel strategies; repository; tool; virtual

DESCRIPTION (provided by applicant): Our goal is to evolve the field of biosimulation, and to catalyze biological research wherever biosimulation modeling is used. Our focus is on the rapid development of multi-scale models that leverage prior model development-that is, to facilitate knowledge sharing and reuse. Our novel approach to improving knowledge sharing is to use semantic annotation based on a common foundation: the basic laws of physical dynamics that govern all biological processes. With this common foundation, and with annotations against the common physical semantics, we can automatically detect and make semantic connections between biosimulation models. These connections are a key step to allow researchers to reuse and recombine models in new ways. Annotation can be a bottleneck in this workflow, and our proposal aims to both address this bottle- neck via an automatic annotation process, and then to demonstrate that this semantic annotation of models is sufficient for researchers to more easily find, understand, and reuse models to more rapidly produce new, merged models. We will demonstrate these results both in a controlled, laboratory set- ting, and "in the wild", in collaboration with researchers who are pat of the Virtual Physiological Rat Project (VPR). More specifically, we will (aim 1) develop new methods for automatically assigning semantic annotations to biosimulation models. At the end of this aim, we will have a large corpus of annotated models to evaluate and extend in the next two aims. Next (aim 2), in a controlled, laboratory setting, we will test both the efficiency (how fast can an integrated model be built) an efficacy (how accurate is the resulting model) of our methods. Finally, in aim 3, we will collaborate closely with the VPR scientists to achieve two ends. First, we must validate that our methods are useful in the real world of biosimulation model development. Second, we will leverage the VPR project to participate in and then lead workshops around our methods and ideas of semantic annotation for models. These workshops should lead to additional use-cases and users for further validation, and also for opportunities to promulgate our methods and broaden the long-term impact of our work.

描述(申请人提供)：我们的目标是发展生物模拟领域，并在使用生物模拟建模的地方催化生物研究。我们的重点是快速开发利用先前模型开发的多比例模型-即促进知识共享和重用。我们改进知识共享的新方法是使用基于一个共同基础的语义注释：支配所有生物过程的物理动力学基本定律。在这个共同的基础上，通过针对共同的物理语义进行标注，我们可以自动检测和建立生物模拟模型之间的语义联系。这些联系是允许研究人员以新的方式重用和重组模型的关键一步。 注释可能是此工作流中的瓶颈，我们的建议旨在通过自动注释过程解决这一瓶颈，然后演示模型的这种语义注释足以让研究人员更容易地发现、理解和重用模型，以更快地生成新的合并模型。我们将与虚拟生理大鼠项目(VPR)的研究人员合作，在受控的实验室环境和“野外”两个方面展示这些结果。 更具体地说，我们将(目标1)开发自动为生物模拟模型分配语义注释的新方法。在这个目标结束时，我们将有一个庞大的带注释的模型语料库来评估和扩展下两个目标。下一步(目标2)，在受控的实验室环境中，我们将测试我们方法的效率(建立集成模型的速度有多快)和有效性(得到的模型有多准确)。最后，在目标3中，我们将与VPR的科学家密切合作，以实现两个目标。首先，我们必须验证我们的方法在生物模拟模型开发的真实世界中是有用的。其次，我们将利用VPR项目参与并领导围绕我们的模型语义注释方法和想法的研讨会。这些研讨会应该产生更多的用例和用户，以便进一步验证，并有机会公布我们的方法，扩大我们工作的长期影响。