Maeda, K.; Kurata, H. Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. Int. J. Mol. Sci.2023, 24, 7296.
Maeda, K.; Kurata, H. Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. Int. J. Mol. Sci. 2023, 24, 7296.
Maeda, K.; Kurata, H. Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. Int. J. Mol. Sci.2023, 24, 7296.
Maeda, K.; Kurata, H. Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. Int. J. Mol. Sci. 2023, 24, 7296.
Abstract
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-3 as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML 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.
Keywords
GPT; language model; kinetic modeling; simulation; systems biology; natural language processing
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
