Christley Scott, An Gary
Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA.
Comput Math Organ Theory. 2012 Dec;18(4):380-403. doi: 10.1007/s10588-011-9101-y.
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.
生物医学研究中的转化挑战在于将机制性知识从一种生物学背景有效且高效地转移到另一种生物学背景。这一过程中隐含的是从相关性中以机制性假设的形式建立因果关系。有效应对转化挑战需要使用自动化方法，包括能够以计算方式捕捉假定假设的动态方面，以便能够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；将这些表示形式转化为可执行模型/模拟是下一步必要步骤。研究人员需要有能力将他们的概念模型映射到一个可以转化为可执行模拟程序的模型规范中。我们认为，这个近似于计算模型开发中某些步骤的映射过程，可以表示为一组逻辑规则，并且一个半智能计算代理，即计算建模助手（CMA），可以进行推理以制定一个实现可执行模型构建的计划。本文介绍了由CMA执行的生物医学本体和模拟本体之间的模型构建推理过程的描述和实现，以生成可用于动态知识表示的可执行模型规范。
