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ERIC Number: ED520181
Record Type: Non-Journal
Publication Date: 2010
Pages: 135
Abstractor: As Provided
Reference Count: 0
ISBN: ISBN-978-1-1242-2887-7
Modular, Semantics-Based Composition of Biosimulation Models
Neal, Maxwell Lewis
ProQuest LLC, Ph.D. Dissertation, University of Washington
Biosimulation models are valuable, versatile tools used for hypothesis generation and testing, codification of biological theory, education, and patient-specific modeling. Driven by recent advances in computational power and the accumulation of systems-level experimental data, modelers today are creating models with an unprecedented level of complexity. These researchers need tools that manage this complexity and scale across biological levels of organization and physical domain. Historically, many industries have addressed the issue of complexity by adopting a modular product design. In order to apply this approach to the field of biosimulation, existing models must be cast as interoperable components. However, modelers today use a variety of simulation languages so that interoperability is the exception rather than the rule. For my dissertation research I have worked on the challenges of modularity and interoperability within biosimulation. I helped develop a modular, multi-scale, multi-domain modeling approach called SemSim that provides broad model interoperability. The SemSim approach includes a declarative model description format that can capture the computational and semantic information in existing models, thereby converting them into interoperable, reusable components. Because they interoperate at the semantic level, SemSim models offer opportunities to automate common composition and decomposition tasks beyond currently available methods. For my dissertation work I created and tested a software tool called SemGen that helps automate the modular composition and decomposition of SemSim models. With this tool, users can (1) convert existing models into the SemSim format and annotate them with semantic data, (2) automatically decompose SemSim models into interoperable sub-models, (3) semi-automatically merge SemSim models into larger systems, and (4) encode SemSim models in an executable simulation format. As a proof-of-concept demonstration of modular modeling, I used SemGen to perform a set of model composition and decomposition tasks using models of hemodynamics, neural signaling, molecular diffusion, and chemical pathway kinetics. This demonstration establishes SemGen's capabilities for automating the modular composition and decomposition of biosimulation models across physical scales and physical domains. Thus, SemGen has the potential to advance the entire field of biosimulation by spurring the development of complex models for biological research, drug target identification, and patient-specific modeling. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page:]
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Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A