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Shin W, Gennari JH, Hellerstein JL, Sauro HM. An automated model annotation system (AMAS) for SBML models. Bioinformatics 2023; 39:btad658. [PMID: 37882737 PMCID: PMC10628433 DOI: 10.1093/bioinformatics/btad658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/03/2023] [Accepted: 10/24/2023] [Indexed: 10/27/2023] Open
Abstract
MOTIVATION Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. RESULTS We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g. species, reactions) by specifying the reference database (e.g. ChEBI for species) and the match score function (e.g. string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has subsecond response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. AVAILABILITY AND IMPLEMENTATION Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.
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Affiliation(s)
- Woosub Shin
- Auckland Bioengineering Institute, University of Auckland, 1010 Auckland, New Zealand
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, United States
| | - Joseph L Hellerstein
- eScience Institute, University of Washington, Seattle, WA 98195, United States
- Paul G. Allen School of Computer Science, University of Washington, Seattle, WA 98195, United States
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA 98195, United States
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Shin W, Gennari JH, Hellerstein JL, Sauro HM. An Automated Model Annotation System (AMAS) for SBML Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.19.549722. [PMID: 37503075 PMCID: PMC10370092 DOI: 10.1101/2023.07.19.549722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Motivation Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. Results We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g., species, reactions) by specifying the reference database (e.g., ChEBI for species) and the match score function (e.g., string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has sub-second response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. Availability Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.
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Affiliation(s)
- Woosub Shin
- Auckland Bioengineering Institute, University of Auckland, Auckland,1010,New Zealand
| | - John H. Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, 98195, WA, USA
| | - Joseph L. Hellerstein
- eScience Institute, University of Washington, Seattle,98195, WA, USA
- Paul G. Allen School of Computer Science, University of Washington, Seattle, 98195, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, 98195, WA, USA
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May RW, Maso Talou GD, Clark AR, Mynard JP, Smolich JJ, Blanco PJ, Müller LO, Gentles TL, Bloomfield FH, Safaei S. From fetus to neonate: A review of cardiovascular modeling in early life. WIREs Mech Dis 2023:e1608. [DOI: 10.1002/wsbm.1608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 01/31/2023] [Accepted: 03/03/2023] [Indexed: 04/03/2023]
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Munarko Y, Rampadarath A, Nickerson DP. CASBERT: BERT-based retrieval for compositely annotated biosimulation model entities. FRONTIERS IN BIOINFORMATICS 2023; 3:1107467. [PMID: 36865672 PMCID: PMC9971925 DOI: 10.3389/fbinf.2023.1107467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/31/2023] [Indexed: 02/16/2023] Open
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 Resource Description Framework 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 unsuitable for most repository users who explore biosimulation models freely without adequate knowledge of 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 of entity embeddings. 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 list 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.
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Affiliation(s)
- Yuda Munarko
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand,*Correspondence: Yuda Munarko,
| | - Anand Rampadarath
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand,The New Zealand Institute for Plant & Food Research Ltd., Auckland, New Zealand
| | - David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Munarko Y, Rampadarath A, Nickerson D. Building a search tool for compositely annotated entities using Transformer-based approach: Case study in Biosimulation Model Search Engine (BMSE). F1000Res 2023; 12:162. [PMID: 37842339 PMCID: PMC10570691 DOI: 10.12688/f1000research.128982.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/25/2023] [Indexed: 10/17/2023] Open
Abstract
The Transformer-based approaches to solving natural language processing (NLP) tasks such as BERT and GPT are gaining popularity due to their ability to achieve high performance. These approaches benefit from using enormous data sizes to create pre-trained models and the ability to understand the context of words in a sentence. Their use in the information retrieval domain is thought to increase effectiveness and efficiency. This paper demonstrates a BERT-based method (CASBERT) implementation to build a search tool over data annotated compositely using ontologies. The data was a collection of biosimulation models written using the CellML standard in the Physiome Model Repository (PMR). A biosimulation model structurally consists of basic entities of constants and variables that construct higher-level entities such as components, reactions, and the model. Finding these entities specific to their level is beneficial for various purposes regarding variable reuse, experiment setup, and model audit. Initially, we created embeddings representing compositely-annotated entities for constant and variable search (lowest level entity). Then, these low-level entity embeddings were vertically and efficiently combined to create higher-level entity embeddings to search components, models, images, and simulation setups. Our approach was general, so it can be used to create search tools with other data semantically annotated with ontologies - biosimulation models encoded in the SBML format, for example. Our tool is named Biosimulation Model Search Engine (BMSE).
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Affiliation(s)
- Yuda Munarko
- Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand
| | - Anand Rampadarath
- Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand
- The New Zealand Institute for Plant and Food Research Limited, Auckland, New Zealand
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand
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Noroozbabaee L, Blanco PJ, Safaei S, Nickerson DP. A modular and reusable model of epithelial transport in the proximal convoluted tubule. PLoS One 2022; 17:e0275837. [PMID: 36355848 PMCID: PMC9648790 DOI: 10.1371/journal.pone.0275837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 09/24/2022] [Indexed: 11/12/2022] Open
Abstract
We review a collection of published renal epithelial transport models, from which we build a consistent and reusable mathematical model able to reproduce many observations and predictions from the literature. The flexible modular model we present here can be adapted to specific configurations of epithelial transport, and in this work we focus on transport in the proximal convoluted tubule of the renal nephron. Our mathematical model of the epithelial proximal convoluted tubule describes the cellular and subcellular mechanisms of the transporters, intracellular buffering, solute fluxes, and other processes. We provide free and open access to the Python implementation to ensure our multiscale proximal tubule model is accessible; enabling the reader to explore the model through setting their own simulations, reproducibility tests, and sensitivity analyses.
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Affiliation(s)
- Leyla Noroozbabaee
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Pablo J. Blanco
- National Laboratory for Scientific Computing, Petrópolis, Brazil
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Niarakis A, Waltemath D, Glazier J, Schreiber F, Keating SM, Nickerson D, Chaouiya C, Siegel A, Noël V, Hermjakob H, Helikar T, Soliman S, Calzone L. Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology. Brief Bioinform 2022; 23:bbac212. [PMID: 35671510 PMCID: PMC9294410 DOI: 10.1093/bib/bbac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/20/2022] [Accepted: 05/06/2022] [Indexed: 11/14/2022] Open
Abstract
Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.
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Affiliation(s)
- Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Dagmar Waltemath
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - James Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Anne Siegel
- Univ Rennes, CNRS, Inria - IRISA lab. Rennes
| | - Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Henning Hermjakob
- EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
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Hendrix M, Clerx M, Tamuri AU, Keating SM, Johnstone RH, Cooper J, Mirams GR. cellmlmanip and chaste_codegen: automatic CellML to C++ code generation with fixes for singularities and automatically generated Jacobians. Wellcome Open Res 2022; 6:261. [PMID: 35299708 PMCID: PMC8902258 DOI: 10.12688/wellcomeopenres.17206.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 11/20/2022] Open
Abstract
Hundreds of different mathematical models have been proposed for describing electrophysiology of various cell types. These models are quite complex (nonlinear systems of typically tens of ODEs and sometimes hundreds of parameters) and software packages such as the Cancer, Heart and Soft Tissue Environment (Chaste) C++ library have been designed to run simulations with these models in isolation or coupled to form a tissue simulation. The complexity of many of these models makes sharing and translating them to new simulation environments difficult. CellML is an XML format that offers a widely-adopted solution to this problem. This paper specifically describes the capabilities of two new Python tools: the cellmlmanip library for reading and manipulating CellML models; and chaste_codegen, a CellML to C++ converter. These tools provide a Python 3 replacement for a previous Python 2 tool (called PyCML) and they also provide additional new features that this paper describes. Most notably, they can generate analytic Jacobians without the use of proprietary software, and also find singularities occurring in equations and automatically generate and apply linear approximations to prevent numerical problems at these points.
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Affiliation(s)
- Maurice Hendrix
- Centre for Mathematical Medicine & Biology, University of Nottingham, Nottingham, UK
- Digital Research Service, School of Mathematical Sciences, University of Nottingham, Nottingham, NG8 1BB, UK
| | - Michael Clerx
- Centre for Mathematical Medicine & Biology, University of Nottingham, Nottingham, UK
| | - Asif U Tamuri
- Centre for Advanced Research Computing, University College London, London, WC1E 6BT, UK
| | - Sarah M Keating
- Centre for Advanced Research Computing, University College London, London, WC1E 6BT, UK
| | - Ross H Johnstone
- Computational Biology & Healthcare Informatics, Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
| | - Jonathan Cooper
- Centre for Advanced Research Computing, University College London, London, WC1E 6BT, UK
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, University of Nottingham, Nottingham, UK
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Munarko Y, Sarwar DM, Rampadarath A, Atalag K, Gennari JH, Neal ML, Nickerson DP. NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories. Front Physiol 2022; 13:820683. [PMID: 35283794 PMCID: PMC8908213 DOI: 10.3389/fphys.2022.820683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 01/31/2022] [Indexed: 12/04/2022] Open
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 utilizing 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 utilizing 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 behavior against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.
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Affiliation(s)
- Yuda Munarko
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- *Correspondence: Yuda Munarko
| | - Dewan M. Sarwar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Anand Rampadarath
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Koray Atalag
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - John H. Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Maxwell L. Neal
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, United States
| | - David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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10
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Hendrix M, Clerx M, Tamuri AU, Keating SM, Johnstone RH, Cooper J, Mirams GR. chaste codegen: automatic CellML to C++ code generation with fixes for singularities and automatically generated Jacobians. Wellcome Open Res 2021; 6:261. [PMID: 35299708 PMCID: PMC8902258 DOI: 10.12688/wellcomeopenres.17206.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/29/2021] [Indexed: 02/15/2024] Open
Abstract
Hundreds of different mathematical models have been proposed for describing electrophysiology of various cell types. These models are quite complex (nonlinear systems of typically tens of ODEs and sometimes hundreds of parameters) and software packages such as the Cancer, Heart and Soft Tissue Environment (Chaste) C++ library have been designed to run simulations with these models in isolation or coupled to form a tissue simulation. The complexity of many of these models makes sharing and translating them to new simulation environments difficult. CellML is an XML format that offers a solution to this problem and has been widely-adopted. This paper specifically describes the capabilities of chaste_codegen, a Python-based CellML to C++ converter based on the new cellmlmanip Python library for reading and manipulating CellML models. While chaste_codegen is a Python 3 redevelopment of a previous Python 2 tool (called PyCML) it has some additional new features that this paper describes. Most notably, chaste_codegen has the ability to generate analytic Jacobians without the use of proprietary software, and also to find singularities occurring in equations and automatically generate and apply linear approximations to prevent numerical problems at these points.
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Affiliation(s)
- Maurice Hendrix
- Centre for Mathematical Medicine & Biology, University of Nottingham, Nottingham, UK
- Digital Research Service, School of Mathematical Sciences, University of Nottingham, Nottingham, NG8 1BB, UK
| | - Michael Clerx
- Centre for Mathematical Medicine & Biology, University of Nottingham, Nottingham, UK
| | - Asif U Tamuri
- Centre for Advanced Research Computing, University College London, London, WC1E 6BT, UK
| | - Sarah M Keating
- Centre for Advanced Research Computing, University College London, London, WC1E 6BT, UK
| | - Ross H Johnstone
- Computational Biology & Healthcare Informatics, Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
| | - Jonathan Cooper
- Centre for Advanced Research Computing, University College London, London, WC1E 6BT, UK
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, University of Nottingham, Nottingham, UK
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11
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Shahidi N, Pan M, Safaei S, Tran K, Crampin EJ, Nickerson DP. Hierarchical semantic composition of biosimulation models using bond graphs. PLoS Comput Biol 2021; 17:e1008859. [PMID: 33983945 PMCID: PMC8148364 DOI: 10.1371/journal.pcbi.1008859] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/25/2021] [Accepted: 04/27/2021] [Indexed: 11/19/2022] Open
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 example, 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.
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Affiliation(s)
- Niloofar Shahidi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
| | - Soroush Safaei
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Kenneth Tran
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
| | - David P. Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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12
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Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021; 12:637999. [PMID: 33841175 PMCID: PMC8027332 DOI: 10.3389/fphys.2021.637999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
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Affiliation(s)
- Karim Azer
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | - Chanchala D. Kaddi
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | | | - Jane P. F. Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Sean T. McQuade
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Nathaniel J. Merrill
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Benedetto Piccoli
- Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Susana Neves-Zaph
- Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States
| | - Luca Marchetti
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Rosario Lombardo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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13
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Rougny A, Touré V, Albanese J, Waltemath D, Shirshov D, Sorokin A, Bader GD, Blinov ML, Mazein A. SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks. Brief Bioinform 2021; 22:6184415. [PMID: 33758926 PMCID: PMC8425392 DOI: 10.1093/bib/bbab049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/13/2021] [Indexed: 11/14/2022] Open
Abstract
A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.
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Affiliation(s)
- Adrien Rougny
- Corresponding authors: Adrien Rougny, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan and Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan; E-mail: ; Michael L. Blinov, R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA; E-mail: ; Alexander Mazein, European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France; Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg; E-mail:
| | - Vasundra Touré
- Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, Realfagbygget, 7491 Trondheim, Norway
| | - John Albanese
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Dagmar Waltemath
- Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, D-17475 Greifswald, Germany
| | - Denis Shirshov
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
- Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia
| | - Anatoly Sorokin
- Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia
- Moscow Institute of Physics and Technology, 9 Institutsky per., Dolgoprudny, Moscow Region, 141700, Russia
- University of Liverpool, Liverpool L7 3EA, UK
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, M5S 3E1, Toronto, Canada
| | - Michael L Blinov
- Corresponding authors: Adrien Rougny, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan and Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan; E-mail: ; Michael L. Blinov, R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA; E-mail: ; Alexander Mazein, European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France; Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg; E-mail:
| | - Alexander Mazein
- Corresponding authors: Adrien Rougny, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan and Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan; E-mail: ; Michael L. Blinov, R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA; E-mail: ; Alexander Mazein, European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France; Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg; E-mail:
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14
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Kokash N, de Bono B. Knowledge Representation for Multi-Scale Physiology Route Modeling. Front Neuroinform 2021; 15:560050. [PMID: 33664662 PMCID: PMC7921311 DOI: 10.3389/fninf.2021.560050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Abstract
We present a framework for the topological and semantic assembly of multiscale physiology route maps. The framework, called ApiNATOMY, consists of a knowledge representation (KR) model and a set of knowledge management (KM) tools. Using examples of ApiNATOMY route maps, we present a KR format that is suitable for the analysis and visualization by KM tools. The conceptual KR model provides a simple method for physiology experts to capture process interactions among anatomical entities. In this paper, we outline the KR model, modeling format, and the KM procedures to translate concise abstraction-based specifications into fully instantiated models of physiology processes.
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Affiliation(s)
- Natallia Kokash
- Peoples' Friendship University of Russia (RUDN University), Moscow, Russia
| | - Bernard de Bono
- Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.,Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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15
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Biomedical Repositories for Simulation Studies. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11684-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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16
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Sarwar DM, Nickerson DP. CellML Model Discovery with the Physiome Model Repository. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11681-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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17
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Waltemath D, Golebiewski M, Blinov ML, Gleeson P, Hermjakob H, Hucka M, Inau ET, Keating SM, König M, Krebs O, Malik-Sheriff RS, Nickerson D, Oberortner E, Sauro HM, Schreiber F, Smith L, Stefan MI, Wittig U, Myers CJ. The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE). J Integr Bioinform 2020; 17:jib-2020-0005. [PMID: 32598315 PMCID: PMC7756615 DOI: 10.1515/jib-2020-0005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 05/14/2020] [Indexed: 01/23/2023] Open
Abstract
This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.
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Affiliation(s)
- Dagmar Waltemath
- Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | | | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - Michael Hucka
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Esther Thea Inau
- Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | | | - Matthias König
- Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Olga Krebs
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Ernst Oberortner
- U.S. Department of Energy (DOE) Joint Genome Institute (JGI), Lawrence Berkeley National Labs, Berkeley, CA, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Falk Schreiber
- Department of Computer and Information Science, University ofKonstanz, Germany.,Faculty of IT, Monash University, Melbourne, VIC, Australia
| | - Lucian Smith
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Melanie I Stefan
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK.,ZJU-UoE Institute, Zhejiang University, Haining, China.,University of Utah, Salt Lake City, UT, USA
| | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Chris J Myers
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK
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