The translational challenge in biomedical research lies in the effective and efficient transfer of mechanistic knowledge from one biological context to another. Implicit in this process is the establishment of causality from correlation in the form of mechanistic hypotheses. Effectively addressing the translational challenge requires the use of automated methods, including the ability to computationally capture the dynamic aspect of putative hypotheses such that they can be evaluated in a high throughput fashion. Ontologies provide structure and organization to biomedical knowledge; converting these representations into executable models/simulations is the next necessary step. Researchers need the ability to map their conceptual models into a model specification that can be transformed into an executable simulation program. We suggest this mapping process, which approximates certain steps in the development of a computational model, can be expressed as a set of logical rules, and a semi-intelligent computational agent, the Computational Modeling Assistant (CMA), can perform reasoning to develop a plan to achieve the construction of an executable model. Presented herein is a description and implementation for a model construction reasoning process between biomedical and simulation ontologies that is performed by the CMA to produce the specification of an executable model that can be used for dynamic knowledge representation.

中文翻译：

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使用半智能计算建模助手增强生物模型构建的建议。

生物医学研究中的转化挑战在于机械知识从一种生物环境到另一种生物环境的有效和高效转移。这个过程中隐含的是以机械假设的形式从相关性中建立因果关系。有效解决翻译挑战需要使用自动化方法，包括能够以计算方式捕获假定假设的动态方面，以便可以以高通量方式对其进行评估。本体为生物医学知识提供结构和组织；将这些表示转换为可执行的模型/模拟是下一个必要步骤。研究人员需要能够将他们的概念模型映射到可以转换为可执行仿真程序的模型规范中。我们建议这个映射过程，它近似计算模型开发中的某些步骤，可以表示为一组逻辑规则，半智能计算代理，计算建模助手 (CMA)，可以执行推理来开发一个计划实现可执行模型的构建。这里呈现的是生物医学和模拟本体之间的模型构建推理过程的描述和实现，该过程由 CMA 执行以生成可用于动态知识表示的可执行模型的规范。可以进行推理以制定计划以实现可执行模型的构建。这里呈现的是生物医学和模拟本体之间的模型构建推理过程的描述和实现，该过程由 CMA 执行以生成可用于动态知识表示的可执行模型的规范。可以进行推理以制定计划以实现可执行模型的构建。这里呈现的是生物医学和模拟本体之间的模型构建推理过程的描述和实现，该过程由 CMA 执行以生成可用于动态知识表示的可执行模型的规范。