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A proposal for augmenting biological model construction with a semi-intelligent computational modeling assistant

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  • Scott Christley

    (University of Chicago)

  • Gary An

    (University of Chicago)

Abstract

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.

Suggested Citation

  • Scott Christley & Gary An, 2012. "A proposal for augmenting biological model construction with a semi-intelligent computational modeling assistant," Computational and Mathematical Organization Theory, Springer, vol. 18(4), pages 380-403, December.
    • Handle: RePEc:spr:comaot:v:18:y:2012:i:4:d:10.1007_s10588-011-9101-y
    DOI: 10.1007/s10588-011-9101-y
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    References listed on IDEAS

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    1. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
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