Year: 2014
Type: article
Abstract: Biological researchers increasingly rely on computational models to integrate biological systems knowledge, test hypotheses, and forecast system behavior.The expanding size of these models requires solutions for managing their complexity.Modularity, a time-tested design principle for managing complexity, can be applied within the biological modeling field to parallelize work, automate composition, and promote effective model sharing.As modelers of complex biological systems, we aim to apply modular production to accelerate our efforts and have therefore investigated several currently available approaches for modular modeling.We argue that some traditional features of modularity, in particular the isolation of a module's contents from the rest of the system, can impede model sharing and composition when applied within the context of biological simulation.Alternative approaches that can automatically interface model components based on the biological meaning of their contents (their semantics) avoid these limitations.Our conclusions have strategic implications for the design of systems biology, synthetic biology, and integrated physiological modeling technologies, as well as communitylevel model curation efforts.
Source: PLoS Computational Biology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: American Heart Association, Royal Society, European Commission, Royal Society Te Apārangi, Maurice Wilkins Centre for Molecular Biodiscovery, National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences, U.S. National Library of Medicine
Awards: Researching Interoperability using Core Reference Datasets and Ontologies for the Virtual Physiological Human, G5818179528, The Virtual Physiological Rat Project, A synthetic biology library for the Semantic Web, Continued Development and Maintenance of the Systems Biology Workbench, University of Washington Biomedical Informatics and Data Science Research Training Program
OpenAlex ID: W2016462601