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 semi- automatically merge models to more effectively build larger, multi-scale models. How- ever, 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 demon- strates 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 mus- cle. 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

Using multiple reference ontologies: Managing composite annotations

There are a growing number of reference ontologies available across a variety of biomedical domains and current research focuses on their construction, organization and use. An important use case for these ontologies is annotation—where users create metadata that access concepts and terms in reference ontologies. We draw on our experience in physiological modeling to present a compelling use case that demonstrates the potential complexity of such annotations. In the domain of physiological biosimulation, we argue that most annotations require the use of multiple reference ontologies. We suggest that these “composite” annotations should be retained as a repository of knowledge about post-coordination that promotes sharing and interoperation across biosimulation models

Advances in semantic representation for multiscale biosimulation: a case study in merging models

As a case-study of biosimulation model integration, we describe our experiences applying the SemSim methodology to integrate independently-developed, multiscale models of cardiac circulation. In particular, we have integrated the CircAdapt model (written by T. Arts for MATLAB) of an adapting vascular segment with a cardiovascular system model (written by M. Neal for JSim). We report on three results from the model integration experience. First, models should be explicit about simulations that occur on different time scales. Second, data structures and naming conventions used to represent model variables may not translate across simulation languages. Finally, identifying the dependencies among model variables is a non-trivial task. We claim that these challenges will appear whenever researchers attempt to integrate models from others, especially when those models are written in a procedural style (using MATLAB, Fortran, etc.) rather than a declarative format (as supported by languages like SBML, CellML or JSim’s MML)

Composite annotations: requirements for mapping multiscale data and models to biomedical ontologies

Abstract—Current methods for annotating biomedical data resources rely on simple mappings between data elements and the contents of a variety of biomedical ontologies and controlled vocabularies. Here we point out that such simple mappings are inadequate for large-scale multiscale, multidomain integrative “virtual human” projects. For such integrative challenges, we describe a “composite annotation” schema that is simple yet sufficiently extensible for mapping the biomedical content of a variety of data sources and biosimulation models to available biomedical ontologies

Bridging Biological Ontologies and Biosimulation: The Ontology of Physics for Biology

We introduce and define the Ontology of Physics for Biology (OPB), a reference ontology of physical principles that bridges the gap between bioinformatics modeling of biological structures and the biosimulation modeling of biological processes. Whereas modeling anatomical entities is relatively wellstudied, representing the physics-based semantics of biosimulation and biological processes remains an open research challenge. The OPB bridges this semantic gap--linking the semantics of biosimulation mathematics to structural bio-ontologies. Our design of the OPB is driven both by theory and pragmatics: we have applied systems dynamics theory to build an ontology with pragmatic use for annotating biosimulation models

Representing physiological processes and their participants with PhysioMaps

BACKGROUND: As the number and size of biological knowledge resources for physiology grows, researchers need improved tools for searching and integrating knowledge and physiological models. Unfortunately, current resources—databases, simulation models, and knowledge bases, for example—are only occasionally and idiosyncratically explicit about the semantics of the biological entities and processes that they describe. RESULTS: We present a formal approach, based on the semantics of biophysics as represented in the Ontology of Physics for Biology, that divides physiological knowledge into three partitions: structural knowledge, process knowledge and biophysical knowledge. We then computationally integrate these partitions across multiple structural and biophysical domains as computable ontologies by which such knowledge can be archived, reused, and displayed. Our key result is the semi-automatic parsing of biosimulation model code into PhysioMaps that can be displayed and interrogated for qualitative responses to hypothetical perturbations. CONCLUSIONS: Strong, explicit semantics of biophysics can provide a formal, computational basis for integrating physiological knowledge in a manner that supports visualization of the physiological content of biosimulation models across spatial scales and biophysical domains

Physical Properties of Biological Entities: An Introduction to the Ontology of Physics for Biology

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities—molecules, cells, organs—are well-established, there are no principled ontologies of physical properties—energies, volumes, flow rates—of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration

Integrating systems biology models and biomedical ontologies

BACKGROUND: Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. RESULTS: We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. CONCLUSIONS: We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms