ORCID Profile
0000-0003-4667-9779
Current Organisation
The University of Auckland
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Publisher: Springer Science and Business Media LLC
Date: 15-03-2014
DOI: 10.4056/SIGS.5279417
Publisher: Physiome Incorporated
Date: 23-01-2023
DOI: 10.36903/PHYSIOME.21708176
Abstract: The system of equations and figures presented in Imtiaz et al. (2002) are verified and reproduced in the current curation paper. Here, to demonstrate reproducibility, We describe the model encoded in the CellML and document the differences between our curated model and the one published by Imtiaz et al.. From the primary publication, we extracted data applying the Engauge digitizer software (Mitchell et al., 2020) to compare the current CellML simulation results against those in the primary publication. Editor's note: revised to remove LaTeX code from the abstract.
Publisher: Cold Spring Harbor Laboratory
Date: 29-05-2022
DOI: 10.1101/2022.05.25.493355
Abstract: The Systems Biology Markup Language (SBML) is a popular software-independent XML-based format for describing models of biological phenomena. The BioModels Database is the largest online repository of SBML models. Several tools and platforms are available to support the reuse and composition of SBML models. However, these tools do not explicitly assess whether models are physically plausibile or thermodynamically consistent. This often leads to ill-posed models that are physically impossible, impeding the development of realistic complex models in biology. Here, we present a framework that can automatically convert SBML models into bond graphs, which imposes energy conservation laws on these models. The new bond graph models are easily mergeable, resulting in physically plausible coupled models. We illustrate this by automatically converting and coupling a model of pyruvate distribution to a model of the pentose phosphate pathway. A framework to convert suitable SBML models of biochemical networks into bond graphs is developed. The framework is applied here to two interconnecting models of metabolism pathways. We automatically integrate the generated bond graph modules. We qualitatively illustrate the functionality of the composed model.
Publisher: Public Library of Science (PLoS)
Date: 03-06-2022
DOI: 10.1371/JOURNAL.PONE.0269497
Abstract: 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.
Publisher: Cold Spring Harbor Laboratory
Date: 13-11-2021
DOI: 10.1101/2021.11.12.468343
Abstract: 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. Detailed, 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.
Publisher: Cold Spring Harbor Laboratory
Date: 05-09-2019
DOI: 10.1101/756304
Abstract: Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommends the use of the Resource Description Framework (RDF). This grounding in RDF provides the flexibility to enable searching for entities within models (e.g. variables, equations, or entire models) by utilising the RDF query language SPARQL. However, the rigidity and complexity of the SPARQL syntax and the nature of the tree-like structure of semantic annotations, are challenging for users. Therefore, we propose NLIMED, an interface that converts natural language queries into SPARQL. We use this interface to query and discover model entities from repositories of biosimulation models. NLIMED works with the Physiome Model Repository (PMR) and the BioModels database and potentially other repositories annotated using RDF. Natural language queries are first ‘chunked’ into phrases and annotated against ontology classes and predicates utilising different natural language processing tools. Then, the ontology classes and predicates are composed as SPARQL and finally ranked using our SPARQL Composer and our indexing system. We demonstrate that NLIMED’s approach for chunking and annotating queries is more effective than the NCBO Annotator for identifying relevant ontology classes in natural language queries. Comparison of NLIMED’s behaviour against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.
Publisher: Elsevier BV
Date: 10-2011
DOI: 10.1016/J.PBIOMOLBIO.2011.06.015
Abstract: The VPH/Physiome Project is developing the model encoding standards CellML (cellml.org) and FieldML (fieldml.org) as well as web-accessible model repositories based on these standards (models.physiome.org). Freely available open source computational modelling software is also being developed to solve the partial differential equations described by the models and to visualise results. The OpenCMISS code (opencmiss.org), described here, has been developed by the authors over the last six years to replace the CMISS code that has supported a number of organ system Physiome projects. OpenCMISS is designed to encompass multiple sets of physical equations and to link subcellular and tissue-level biophysical processes into organ-level processes. In the Heart Physiome project, for ex le, the large deformation mechanics of the myocardial wall need to be coupled to both ventricular flow and embedded coronary flow, and the reaction-diffusion equations that govern the propagation of electrical waves through myocardial tissue need to be coupled with equations that describe the ion channel currents that flow through the cardiac cell membranes. In this paper we discuss the design principles and distributed memory architecture behind the OpenCMISS code. We also discuss the design of the interfaces that link the sets of physical equations across common boundaries (such as fluid-structure coupling), or between spatial fields over the same domain (such as coupled electromechanics), and the concepts behind CellML and FieldML that are embodied in the OpenCMISS data structures. We show how all of these provide a flexible infrastructure for combining models developed across the VPH/Physiome community.
Publisher: Springer Science and Business Media LLC
Date: 14-01-2011
Publisher: Physiome Incorporated
Date: 23-01-2023
DOI: 10.36903/PHYSIOME.21708176.V2
Abstract: The system of equations and figures presented in Imtiaz et al. (2002) are verified and reproduced in the current curation paper. Here, to demonstrate reproducibility, We describe the model encoded in the CellML and document the differences between our curated model and the one published by Imtiaz et al.. From the primary publication, we extracted data applying the Engauge digitizer software (Mitchell et al., 2020) to compare the current CellML simulation results against those in the primary publication. Editor's note: revised to remove LaTeX code from the abstract.
Publisher: Physiome Incorporated
Date: 20-01-2023
DOI: 10.36903/PHYSIOME.21708176.V1
Abstract: The system of equations and figures presented in \\citet{imtiaz2002theoretical} are verified and reproduced in the current curation paper. Here, to demonstrate reproducibility, We describe the model encoded in the CellML and document the differences between our curated model and the one published by \\citeauthor{imtiaz2002theoretical}. From the primary publication, we extracted data applying the Engauge digitizer software \\citep{mark_mitchell_2020_3941227} to compare the current CellML simulation results against those in the primary publication.
Publisher: Oxford University Press (OUP)
Date: 07-05-2022
DOI: 10.1093/NAR/GKAC331
Abstract: Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For ex le, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.
Publisher: Cold Spring Harbor Laboratory
Date: 10-03-2021
DOI: 10.1101/2021.03.09.434672
Abstract: Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an ex le, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition. Biological and physiological systems usually involve multiple underlying processes, mechanisms, structures, and phenomena, referred to here as sub-systems. Modelling the whole system every time from scratch requires a huge amount of effort. An alternative is to model each sub-system in a modular fashion, i.e ., containing meaningful interfaces for connecting to other modules. Such modules are readily combined to produce a whole-system model. For the combined model to be consistent, modules must be described using the same modelling scheme. One way to achieve this is to use energy-based models that are consistent with the conservation laws of physics. Here, we present an approach that achieves this using bond graphs, which allows modules to be combined faster and more efficiently. First, physically plausible modules are generated using a small number of template modules. Then a meaningful interface is added to each module to automate connection. This approach is illustrated by applying this method to an existing model of the circulatory system and verifying the results against the reference model.
Publisher: Cold Spring Harbor Laboratory
Date: 24-11-2022
DOI: 10.1101/2022.11.22.517475
Abstract: Maximising FAIRness of biosimulation models requires a comprehensive description of model entities such as reactions, variables, and components. The COmputational Modeling in BIology NEtwork (COMBINE) community encourages the use of RDF with composite annotations that semantically involve ontologies to ensure completeness and accuracy. These annotations facilitate scientists to find models or detailed information to inform further reuse, such as model composition, reproduction, and curation. SPARQL has been recommended as a key standard to access semantic annotation with RDF, which helps get entities precisely. However, SPARQL is not suitable for most repository users who explore biosimulation models freely without adequate knowledge regarding ontologies, RDF structure, and SPARQL syntax. We propose here a text-based information retrieval approach, CASBERT, that is easy to use and can present candidates of relevant entities from models across a repository’s contents. CASBERT adapts Bidirectional Encoder Representations from Transformers (BERT), where each composite annotation about an entity is converted into an entity embedding for subsequent storage in a list-like structure. For entity lookup, a query is transformed to a query embedding and compared to the entity embeddings, and then the entities are displayed in order based on their similarity. The simple list-like structure makes it possible to implement CASBERT as an efficient search engine product, with inexpensive addition, modification, and insertion of entity embedding. To demonstrate and test CASBERT, we created a dataset for testing from the Physiome Model Repository and a static export of the BioModels database consisting of query-entities pairs. Measured using Mean Average Precision and Mean Reciprocal Rank, we found that our approach can perform better than the traditional bag-of-words method.
Publisher: Oxford University Press (OUP)
Date: 06-01-2011
DOI: 10.1093/BIOINFORMATICS/BTQ723
Abstract: Motivation: The Physiome Model Repository 2 (PMR2) software was created as part of the IUPS Physiome Project (Hunter and Borg, 2003), and today it serves as the foundation for the CellML model repository. Key advantages brought to the end user by PMR2 include: facilities for model exchange, enhanced collaboration and a detailed change history for each model. Availability: PMR2 is available under an open source license at ools mr/ a fully functional instance of this software can be accessed at Contact: tommy.yu@auckland.ac.nz
Publisher: The Royal Society
Date: 28-05-2009
Abstract: The development of standards for encoding mathematical models is an important component of model building and model sharing among scientists interested in understanding multi-scale physiological processes. CellML provides such a standard, particularly for models based on biophysical mechanisms, and a substantial number of models are now available in the CellML Model Repository. However, there is an urgent need to extend the current CellML metadata standard to provide biological and biophysical annotation of the models in order to facilitate model sharing, automated model reduction and connection to biological databases. This paper gives a broad overview of a number of new developments on CellML metadata and provides links to further methodological details available from the CellML website.
Publisher: Cold Spring Harbor Laboratory
Date: 09-10-2021
DOI: 10.1101/2021.10.09.463757
Abstract: The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.
Publisher: Public Library of Science (PLoS)
Date: 13-05-2021
DOI: 10.1371/JOURNAL.PCBI.1008859
Abstract: Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an ex le, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.
Publisher: Cold Spring Harbor Laboratory
Date: 08-05-2019
DOI: 10.1101/631465
Abstract: In this paper we present a web-based platform enabling scientists to construct a novel epithelial transport model to investigate their hypotheses, aided by building on existing models discovered in the Physiome Model Repository (PMR). We have comprehensively annotated a cohort of epithelial transport models deposited in the PMR as a seeding collection of building blocks that are freely available for reuse. On the platform, users are able to semantically display models for visualization, graphical editing, and model assembly. In addition, we leverage web services from the European Bioinformatics Institute (EBI) to help rank similar models based on the suggestions provided by the platform. In addition to potential use in biomedical and clinical research, novice modellers could use our platform as a learning tool. The source code and links to a live demonstration of the platform are available at ewancse/ epithelial-modelling-platform.
Publisher: Walter de Gruyter GmbH
Date: 06-2020
Abstract: This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.
No related grants have been discovered for David Nickerson.