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)

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

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

Open Access

physical dependencies as a basis for biological processe

Accounting Hall of Fame 2000 induction: Charles W. Haskins

For the induction of Charles W. Haskins: Remarks by J. Michael Cook; Citation prepared by Daniel L. Jensen, The Ohio State University, read by J. Michael Coo

Evolution of a Foundational Model of Physiology: Symbolic Representation for Functional Bioinformatics

We describe the need for a Foundational Model of Physiology (FMP) as a reference ontology for functional bioinformatics . The FMP is intended to support symbolic lookup, logical inference and mathematical analysis by integrating descriptive, qualitative and quantitative functional knowledge. The FMP will serve as a symbolic representation of biological functions initially pertaining to human physiology and ultimately extensible to other species. We describe the evolving architecture of the FMP, which is based on the ontological principles of the BioD biological description language and the Foundational Model of Anatomy (FMA)

NASA Exploration Launch Projects Systems Engineering Approach for Astronaut Missions to the Moon, Mars, and Beyond

The U.S. Vision for Space Exploration directs NASA to design and develop a new generation of safe, reliable, and cost-effective transportation systems to hlfill the Nation s strategic goals and objectives. These launch vehicles will provide the capability for astronauts to conduct scientific exploration that yields new knowledge from the unique vantage point of space. American leadership in opening new fi-ontiers will improve the quality of life on Earth for generations to come. The Exploration Launch Projects office is responsible for delivering the Crew Launch Vehicle (CLV) that will loft the Crew Exploration Vehicle (CEV) into low-Earth orbit (LEO) early next decade, and for the heavy lift Cargo Launch Vehicle (CaLV) that will deliver the Lunar Surface Access Module (LSAM) to LEO for astronaut return trips to the Moon by 2020 in preparation for the eventual first human footprint on Mars. Crew travel to the International Space Station will be made available as soon possible after the Space Shuttle retires in 2010

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

Nucleolar localization of an isoform of the IGF-I precursor

BACKGROUND: Alternative exons encode different isoforms of the human insulin-like growth factor-I (IGF-I) precursor without altering mature IGF-I. We hypothesized that the various IGF-I precursors may traffic IGF-I differently. Chimeric IGF-I precursors were made with green fluorescent protein (GFP) cloned between the signal and mature IGF-I domains. RESULTS: Chimeras containing exons 1 or 2 were located in the cytoplasm, consistent with a secretory pathway, and suggesting that both exons encoded functional signal peptides. Exon 5-containing chimeras localized to the nucleus and strongly to the nucleolus, while chimeras containing exon 6 or the upstream portion of exon 5 did not. Nuclear and nucleolar localization also occurred when the mature IGF-I domain was deleted from the chimeras, or when signal peptides were deleted. CONCLUSIONS: We have identified a nucleolar localization for an isoform of the human IGF-I precursor. The findings are consistent with the presence of a nuclear and nucleolar localization signal situated in the C-terminal part of the exon 5-encoded domain with similarities to signals in several other growth factors