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CEUR Workshop Proc. 2016 Aug;1747. pii: http://ceur-ws.org/Vol-1747/IT604_ICBO2016.pdf. Epub 2016 Nov 29.

Qualitative causal analyses of biosimulation models.

Author information

1
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
2
Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.

Abstract

We describe an approach for performing qualitative, systems-level causal analyses on biosimulation models that leverages semantics-based modeling formats, formal ontology, and automated inference. The approach allows users to quickly investigate how a qualitative perturbation to an element within a model's network (an increment or decrement) propagates throughout the modeled system. To support such analyses, we must interpret and annotate the semantics of the models, including both the physical properties modeled and the dependencies that relate them. We build from prior work understanding the semantics of biological properties, but here, we focus on the semantics for dependencies, which provide the critical knowledge necessary for causal analysis of biosimulation models. We describe augmentations to the Ontology of Physics for Biology, via OWL axioms and SWRL rules, and demonstrate that a reasoner can then infer how an annotated model's physical properties influence each other in a qualitative sense. Our goal is to provide researchers with a tool that helps bring the systems-level network dynamics of biosimulation models into perspective, thus facilitating model development, testing, and application.

KEYWORDS:

automated inference; biological modeling; biosimulation; network analysis

PMID:
28804276
PMCID:
PMC5551042

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