@conference {IT604, title = {IT604: Qualitative causal analyses of biosimulation models}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

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 model{\~O}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, 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 model{\~O}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 facilitat-ing model development, testing, and application.

}, url = {http://ceur-ws.org/Vol-1747/IT604_ICBO2016.pdf}, author = {Maxwell Neal and John Gennari and Daniel Cook} }