IT604: Qualitative causal analyses of biosimulation models
|Title||IT604: Qualitative causal analyses of biosimulation models|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Neal M, Gennari J, Cook D|
|Conference Name||International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)|
|Publisher||CEUR-ws.org Volume 1747|
We describe an approach for performing qualita-tive, 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 modelÕs network (an increment or decrement) propagates throughout the modeled system. To support such analyses, we must interpret and annotate the semantics of the models, includ-ing 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 se-mantics for dependencies, which provide the critical knowledge necessary for causal analysis of biosimulation models. We de-scribe 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 modelÕs 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 facilitat-ing model development, testing, and application.