A CellML simulation compiler and code generator using ODE solving schemes
: Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving sche...
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Veröffentlicht in: | Source code for biology and medicine 2012-10, Vol.7 (1), p.11-11, Article 11 |
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creator | Punzalan, Florencio Rusty Yamashita, Yoshiharu Soejima, Naoki Kawabata, Masanari Shimayoshi, Takao Kuwabara, Hiroaki Kunieda, Yoshitoshi Amano, Akira |
description | : Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. The CellML Compiler source code is available for download at http://sourceforge.net/projects/cellmlcompiler. |
doi_str_mv | 10.1186/1751-0473-7-11 |
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However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. The CellML Compiler source code is available for download at http://sourceforge.net/projects/cellmlcompiler.</description><identifier>ISSN: 1751-0473</identifier><identifier>EISSN: 1751-0473</identifier><identifier>DOI: 10.1186/1751-0473-7-11</identifier><identifier>PMID: 23083065</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Analysis ; Differential equations ; Numerical analysis ; Physiological aspects ; Programming languages ; Simulation ; Software ; Studies</subject><ispartof>Source code for biology and medicine, 2012-10, Vol.7 (1), p.11-11, Article 11</ispartof><rights>COPYRIGHT 2012 BioMed Central Ltd.</rights><rights>2012 Punzalan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2012 Punzalan et al.; licensee BioMed Central Ltd. 2012 Punzalan et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c485t-7dfb8316a1e1ef07a544513fa862e04eafa26b62dce50e1c986f83e345320a7e3</citedby><cites>FETCH-LOGICAL-c485t-7dfb8316a1e1ef07a544513fa862e04eafa26b62dce50e1c986f83e345320a7e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778851/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778851/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23083065$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Punzalan, Florencio Rusty</creatorcontrib><creatorcontrib>Yamashita, Yoshiharu</creatorcontrib><creatorcontrib>Soejima, Naoki</creatorcontrib><creatorcontrib>Kawabata, Masanari</creatorcontrib><creatorcontrib>Shimayoshi, Takao</creatorcontrib><creatorcontrib>Kuwabara, Hiroaki</creatorcontrib><creatorcontrib>Kunieda, Yoshitoshi</creatorcontrib><creatorcontrib>Amano, Akira</creatorcontrib><title>A CellML simulation compiler and code generator using ODE solving schemes</title><title>Source code for biology and medicine</title><addtitle>Source Code Biol Med</addtitle><description>: Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. 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However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. The CellML Compiler source code is available for download at http://sourceforge.net/projects/cellmlcompiler.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>23083065</pmid><doi>10.1186/1751-0473-7-11</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Differential equations Numerical analysis Physiological aspects Programming languages Simulation Software Studies |
title | A CellML simulation compiler and code generator using ODE solving schemes |
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