Physiological Knowledge Integration and Recombinant Modeling Via Accelerated Sema

描述（由申请人提供）：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。我们的重点是利用先前的模型开发来快速开发多尺度模型，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。有了这个共同的基础，并对共同的物理语义注释，我们可以自动检测和生物模拟模型之间的语义连接。这些连接是允许研究人员以新的方式重用和重组模型的关键一步。 注释可能是这个工作流程中的瓶颈，我们的建议旨在通过自动注释过程解决这个瓶颈，然后证明模型的这种语义注释足以让研究人员更容易地找到，理解和重用模型，以更快地产生新的合并模型。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”中展示这些结果。 更具体地说，我们将（目标1）开发新的方法，自动分配语义注释的生物模拟模型。在这个目标的最后，我们将有一个大型的注释模型语料库来评估和扩展接下来的两个目标。接下来（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（建立综合模型的速度）和功效（最终模型的准确性）。最后，在目标3中，我们将与VPR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的世界中是有用的。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思想的研讨会。这些研讨会应该会带来更多的用例和用户，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。

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.