UW SIG Biosimulation

Led by Dan Cook

Application Model Ontology (AMO) Project

Mathematical biosimulations model biological processes in a variety of computational environments (e.g., JSim, Fortran, MatLab), yet the semantics of such models are implicit in the mathematics or, at best, embedded in in-line code annotations that are often opaque to anyone but model authors. Recognizing the need to reuse and integrate legacy and evolving biosimulation models, we have proposed the Application Model Ontology (AMO) as a light-weight semantic framework for building ApplModels — each of which is a symbolic representation that maps computational simulation constructs (e.g., variables, equations) to classes in reference ontologies. Benefits to the biosimulation community include the ability to: 1) rigorously annotate and expose the semantic content of biosimulation models to support model reuse, 2) exploit strong semantics to symbolically modularize, merge, and augment multiscale models, 3) symbolically query and reason about simulation models to reveal their functional bases and input/output relations, 4) generate biosimulation code in a variety of languages according to the semantic structure of models.

Ontology of Physics for Biology (OPB) Project

Central to the task of modern biology is representing the biological structures and the biological processes in which they participate at all levels of structural granularity (e.g., molecular, cellular, organ systems) across multiple scientific disciplines (e.g., molecular biology, systems physiology, organismal development). Whereas the representation of biological structure in formal ontologies such as the Foundational Model of Anatomy has set standards for biological ontologies, the representation of dynamic biological processes has lagged substantially. Yet, bioengineers and biophysicists have for years been representing and studying biological processes using physics-based mathematical models yet there is, as yet, no way to map between the sophisticated declarative knowledge of the biological structure and the sophisticated quantitative knowledge of biological processes. Here we describe the Ontology of Physics for Biology (OPB) that is a reference ontology for those aspects of classical physics that are essential for representing, annotating and encoding quantitative models of biological processes. In conjunction with our Application Model Ontology (AMO), the OPB will bridge the gap between declarative representations of structures and physics-based, mathematical representations of processes.