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Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT...

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Detalles Bibliográficos
Autores principales: Maeda, Kazuhiro, Kurata, Hiroyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138937/
https://www.ncbi.nlm.nih.gov/pubmed/37108453
http://dx.doi.org/10.3390/ijms24087296
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author Maeda, Kazuhiro
Kurata, Hiroyuki
author_facet Maeda, Kazuhiro
Kurata, Hiroyuki
author_sort Maeda, Kazuhiro
collection PubMed
description Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein–protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.
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spelling pubmed-101389372023-04-28 Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT Maeda, Kazuhiro Kurata, Hiroyuki Int J Mol Sci Article Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein–protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation. MDPI 2023-04-14 /pmc/articles/PMC10138937/ /pubmed/37108453 http://dx.doi.org/10.3390/ijms24087296 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Maeda, Kazuhiro
Kurata, Hiroyuki
Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT
title Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT
title_full Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT
title_fullStr Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT
title_full_unstemmed Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT
title_short Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT
title_sort automatic generation of sbml kinetic models from natural language texts using gpt
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138937/
https://www.ncbi.nlm.nih.gov/pubmed/37108453
http://dx.doi.org/10.3390/ijms24087296
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