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Semantics-based model discovery and assembly for renal transport

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Version 3 2018-10-19, 02:16
Version 2 2018-10-12, 12:58
Version 1 2018-10-12, 03:21
posted on 2018-10-19, 02:16 authored by Dewan SarwarDewan Sarwar, Reza KalbasiReza Kalbasi, Koray AtalagKoray Atalag, David NickersonDavid Nickerson
Presentation I presented on behalf of Dewan at the COMBINE 2018 meeting in Boston, October 2018.


Biophysically-based computational models have great potential to make significant contributions to many clinical applications. To help realise this potential, we have been developing tools to help scientists and clinicians discover existing models that are relevant to their needs and to then comprehend the capabilities of such models prior to their assembly into novel model-driven projects. We have comprehensively annotated an initial cohort of renal epithelial transport models with biological semantics, including knowledge such as protein identifiers, anatomical locations, and solutes transported. These annotations are deposited in the Physiome Model Repository (PMR) to ensure they are accessible, persistent, discoverable, and resolvable. We have developed a web-based tool which enables users to discover models relevant to the questions and hypotheses they are investigating and then semantically assemble models. In addition to model discovery and assembly, this tool provides visualisation of the biological semantics and guided graphical editing of a model description. Model editing is aided by a recommender system which guides users to relevant models in the repository, using standard bioinformatics services to help rank recommended models.

The semantic annotation and modelling platform we have developed is a new contribution enabling scientists and clinicians to discover relevant models in the PMR and reuse them in other computational modelling initiatives and translational projects linking to real-world health data. We believe that this approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. Furthermore, novice modellers could use this platform as a learning tool. The source code is available on GitHub:


Aotearoa Foundation; MedTech CoRE



University of Auckland