A semantics, energy-based approach to automate biomodel composition

Shahidi, Niloofar; Pan, Michael; Tran, Kenneth; Crampin, Edmund J.; Nickerson, David

doi:10.1371/journal.pone.0269497

Cited by 6 publications

(3 citation statements)

References 69 publications

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“…We can use the same approach as in PMR to complete the annotations and understand the relationships between models. We can also extend studies demonstrating automated and hierarchical model composition for model samples in each repository using energy-based bond graphs [103,104]. Hence, in this directive, we need to investigate ways to look at both repositories and then find and link interrelated and meaningful models to extract and present their knowledge.…”

Section: Limitationsmentioning

confidence: 98%

Smart Searching for the Physiome Project

Munarko

2024

Preprint

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The Physiome Project's standards for biosimulation models in the Physiome Model Repository (PMR) aim to create a virtual physiological human. Despite an increase in models, users need help finding relevant ones. This thesis proposes approaches to aid search using semantic annotations and Natural Language Processing (NLP). The first approach is NLIMED, which converts free-text queries to SPARQL, while the second one is CASBERT, which encodes model entities for exploratory searches. Integrated into a web-based tool, CASBERT enables comprehensive searches, facilitating model discovery and maximizing FAIRness. These methods can be extended to other standards like SBML, promising multi-scale, multi-repository searches in the future. We further investigate CASBERT to identify unannotated entities by leveraging the hierarchical structure of the model. This exploration led to the development of a functional search tool called BMSE.

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Section: Limitationsmentioning

confidence: 98%

Smart Searching for the Physiome Project

Munarko

2024

Preprint

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“…Shahidi et al [172] used SemGem (a software tool for automating the modular composition and decomposition of biosimulation models) to automatically combine CellML, an open standard language based on the XML (Extensible Markup language), [173] models by converting them to analogous BG models. In the following articles [174], they also converted systems biology markup language [175], and CellML models to BGs to apply the same method of composition to generate complex models from existing modules [176]. In this work, the computational models are first converted into BG models and enriched with semantic annotations.…”

Section: (E) Other Biological and Physiological Applicationsmentioning

confidence: 99%

A review of the diverse applications of bond graphs in biology and physiology

Akbarpour Ghazani,

Pan,

Tran

et al. 2024

Proc. R. Soc. A.

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Computational biology and physiology is an interdisciplinary endeavour, requiring input from biologists, physiologists, mathematicians, chemists, engineers and clinicians. These systems are composed of complex phenomena across disparate temporal and spatial scales, and a holistic understanding of system behaviour typically requires the application of advanced multi-scale models. While many modelling techniques have been used, the bond graph (BG) is the only approach for modelling physical systems, where ‘causality’ is represented graphically. Additionally, the BG approach with its intrinsic properties allows the modular construction of models and verifying the conservation of mass and energy algorithmically. The BG approach has been widely used in engineering and, more recently, has been increasingly applied to biology and physiological systems. In this review, we briefly introduce the concepts and strengths of BG modelling. Following this, we review the history of BGs in modelling cellular mechanisms, biochemical reactions and musculoskeletal and cardiovascular systems. Then, current developments in BG software are reviewed, and opportunities and perspectives on the future application of BGs are discussed.

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“…Computational modeling is a powerful tool that can be utilized to further understand the lymphatic system through simulation at multiple spatial scales, including at the molecular, cellular, tissue, and organ level (de Bono et al 2013 ; de Bono and Hunter 2012 ; Safaei et al 2016 , 2018 ; Shahidi et al 2021 , 2022 ). However, computational models of the lymphatics are much less common than cardiovascular models (Safaei et al 2016 , 2018 ; Niederer et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Computational fluid dynamic modeling of the lymphatic system: a review of existing models and future directions

Jayathungage Don,

Safaei,

Maso Talou

et al. 2023

Biomech Model Mechanobiol

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Historically, research into the lymphatic system has been overlooked due to both a lack of knowledge and limited recognition of its importance. In the last decade however, lymphatic research has gained substantial momentum and has included the development of a variety of computational models to aid understanding of this complex system. This article reviews existing computational fluid dynamic models of the lymphatics covering each structural component including the initial lymphatics, pre-collecting and collecting vessels, and lymph nodes. This is followed by a summary of limitations and gaps in existing computational models and reasons that development in this field has been hindered to date. Over the next decade, efforts to further characterize lymphatic anatomy and physiology are anticipated to provide key data to further inform and validate lymphatic fluid dynamic models. Development of more comprehensive multiscale- and multi-physics computational models has the potential to significantly enhance the understanding of lymphatic function in both health and disease.

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A semantics, energy-based approach to automate biomodel composition

Cited by 6 publications

References 69 publications

Smart Searching for the Physiome Project

Smart Searching for the Physiome Project

A review of the diverse applications of bond graphs in biology and physiology

Computational fluid dynamic modeling of the lymphatic system: a review of existing models and future directions

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