RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models

Maeda, Kazuhiro; Boogerd, Fred C.; Kurata, Hiroyuki

doi:10.2197/ipsjtbio.14.30

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…In this study, we used a particular real-coded genetic algorithm (RCGA), named the real-coded ensemble crossover star with just generation gap (REX star /JGG) [ 25 ]. REX star /JGG has been demonstrated competitive in parameter estimation tasks [ 6 , 7 , 26 – 28 ]. We employed the implementation provided by RCGAToolbox [ 7 ].…”

Section: Resultsmentioning

confidence: 99%

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MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling

et al. 2022

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Background Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem). Results To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values. Conclusions MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps, which helps modelers perform MLAGO on their own parameter estimation tasks.

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Section: Resultsmentioning

confidence: 99%

“…REX star /JGG has been demonstrated competitive in parameter estimation tasks [ 6 , 7 , 26 – 28 ]. We employed the implementation provided by RCGAToolbox [ 7 ].…”

Section: Resultsmentioning

confidence: 99%

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling

et al. 2022

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“…Another limitation is that the current version of KinModGPT cannot automatically set appropriate kinetic parameter values. Thus, kinetic parameters must be tuned in the downstream modeling process to create a realistic model that complies with experimental data [27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

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

Maeda¹,

Kurata²

2023

Preprint

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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.

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“…Another limitation, is that the current version of KinModGPT cannot automatically set appropriate kinetic parameter values. Thus, kinetic parameters must be tuned in the downstream modeling process, to create a realistic model that complies with experimental data [ 29 , 30 , 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

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

Maeda

Kurata

2023

IJMS

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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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RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models

Cited by 3 publications

References 34 publications

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling

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

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

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