CellML 2.0

Clerx, Michael; Cooling, Michael T.; Cooper, Jonathan; Garny, Alan; Moyle, Keri; Nickerson, David; Nielsen, Poul M. F.; Sorby, Hugh

doi:10.1515/jib-2020-0021

Cited by 28 publications

(26 citation statements)

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“…NEW CellML 2.0 [ 12 ]: The development of CellML 2.0 was guided by observing the use of CellML 1.1 in the community for over 10 years. The syntax of CellML 1.1 has been clarified in areas where discrepancies in model interpretation are often seen and simplified to remove features that are never used.…”

Section: Current Versions Of Combine Standardsmentioning

confidence: 99%

Specifications of standards in systems and synthetic biology: status and developments in 2020

Schreiber

Sommer

Czauderna

et al. 2020

Journal of Integrative Bioinformatics

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Section: Current Versions Of Combine Standardsmentioning

confidence: 99%

Specifications of standards in systems and synthetic biology: status and developments in 2020

Schreiber

Sommer

Czauderna

et al. 2020

Journal of Integrative Bioinformatics

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“…To facilitate collaboration, several model formats have been created for these frameworks and algorithms. For example, the BioNetGen Language (BNGL, ( 3 )), CellML ( 4 ), NeuroML ( 5 ), and the Systems Biology Markup Language (SBML, ( 6 )) can capture kinetic models, and the SBML flux balance constraints (SBML-fbc; ( 7 )) and qualitative modeling (SBML-qual; ( 8 )) packages can capture flux balance and logical models.…”

Section: Introductionmentioning

confidence: 99%

RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats

Shaikh

Marupilla

Wilson

et al. 2021

Nucleic Acids Research

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Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.

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“…A requirement for this is that the modules be both accessible and reusable [2]. Over the past decade biosimulation models November 12, 2021 2/40 have become increasingly accessible on public repositories such as the Physiome Model Repository (PMR) [3] and BioModels [4], which store models in XML-based format such as CellML [5] and the Systems Biology Markup Language (SBML) [6,7].…”

Section: Introductionmentioning

confidence: 99%

A semantics, energy-based approach to automate biomodel composition

Shahidi

Pan

Tran

et al. 2021

Preprint

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Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model’s components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.Author summaryDetailed, multi-scale computational models bridging from biomolecular processes to entire organs and bodies have the potential to revolutionise medicine by enabling personalised treatments. One of the key challenges to achieving these models is connecting together the vast number of isolated biosimulation models into a coherent whole. Using recent advances in both modelling techniques and biological standards in the scientific community, we developed an approach to integrate and compose models in a physics-based environment. This provides significant advantages, including the automation of model composition and post-model-composition adjustments. We anticipate that our approach will enable the faster development of realistic and accurate models to understand complex biological systems.

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CellML 2.0

Cited by 28 publications

References 0 publications

Specifications of standards in systems and synthetic biology: status and developments in 2020

Specifications of standards in systems and synthetic biology: status and developments in 2020

RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats

A semantics, energy-based approach to automate biomodel composition

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