Integration of multi-scale biosimulation models via light-weight semantics.
Currently, biosimulation researchers use a variety of computational environments and languages to model biological processes. Ideally, researchers should be able to semiautomatically merge models to more effectively build larger, multi-scale models. However, current modeling methods do not capture the underlying semantics of these models sufficiently to support this type of model construction. In this paper, we both propose a general approach to solve this problem, and we provide a specific example that demonstrates the benefits of our methodology. In particular, we describe three biosimulation models: (1) a cardio-vascular fluid dynamics model, (2) a model of heart rate regulation via baroreceptor control, and (3) a sub-cellular-level model of the arteriolar smooth muscle. Within a light-weight ontological framework, we leverage reference ontologies to match concepts across models. The light-weight ontology then helps us combine our three models into a merged model that can answer questions beyond the scope of any single model.
Gennari
John H
JH
Biomedical & Health Informatics, University of Washington, Seattle, WA 98195, USA.
Neal
Maxwell L
ML
Carlson
Brian E
BE
Cook
Daniel L
DL
D055592:Biophysical Phenomena; D001703:Biophysics; D002118:Calcium / Q000378:metabolism; D002320:Cardiovascular Physiological Phenomena; D019295:Computational Biology; D003198:Computer Simulation; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D017136:Ion Transport; D008954:Models, Biological; D008955:Models, Cardiovascular; D009131:Muscle, Smooth, Vascular / Q000502:physiology; D011311:Pressoreceptors / Q000502:physiology; D012660:Semantics; D012984:Software; D049490:Systems Biology
D002118:Calcium