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

Kazuhiro Maeda; Fred C. Boogerd; Hiroyuki Kurata

doi:10.2197/IPSJTBIO.14.30

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

Kazuhiro Maeda^*, Fred C. Boogerd, Hiroyuki Kurata

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REX^star/JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.

Original language	English
Pages (from-to)	30-35
Number of pages	6
Journal	IPSJ Transactions on Bioinformatics
Volume	14
Early online date	26 Nov 2021
DOIs	https://doi.org/10.2197/IPSJTBIO.14.30
Publication status	Published - 2021

Bibliographical note

Funding Information:
Acknowledgments This work was supported by a Grant-in-Aid for Young Scientists (18K18153), Grant-in-Aid for Transformative Research Areas (B) (20H05743), and Grant-in-Aid for Scientific Research (B) (19H04208) from the Japan Society for the Promotion of Science. This work was further supported by JST PRESTO (JPMJPR20K8). The authors thank Editage for reviewing and editing the manuscript.

Publisher Copyright:
© 2021 Information Processing Society of Japan.

Funding

Acknowledgments This work was supported by a Grant-in-Aid for Young Scientists (18K18153), Grant-in-Aid for Transformative Research Areas (B) (20H05743), and Grant-in-Aid for Scientific Research (B) (19H04208) from the Japan Society for the Promotion of Science. This work was further supported by JST PRESTO (JPMJPR20K8). The authors thank Editage for reviewing and editing the manuscript.

Funders	Funder number
Precursory Research for Embryonic Science and Technology	JPMJPR20K8
Japan Society for the Promotion of Science	18K18153

Keywords

Evolutionary algorithm
Parameter estimation
Simulation
Systems biology
Usability

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2197/IPSJTBIO.14.30

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title = "RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models",

abstract = "Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REXstar/JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.",

keywords = "Evolutionary algorithm, Parameter estimation, Simulation, Systems biology, Usability",

author = "Kazuhiro Maeda and Boogerd, {Fred C.} and Hiroyuki Kurata",

note = "Funding Information: Acknowledgments This work was supported by a Grant-in-Aid for Young Scientists (18K18153), Grant-in-Aid for Transformative Research Areas (B) (20H05743), and Grant-in-Aid for Scientific Research (B) (19H04208) from the Japan Society for the Promotion of Science. This work was further supported by JST PRESTO (JPMJPR20K8). The authors thank Editage for reviewing and editing the manuscript. Publisher Copyright: {\textcopyright} 2021 Information Processing Society of Japan.",

year = "2021",

doi = "10.2197/IPSJTBIO.14.30",

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N1 - Funding Information: Acknowledgments This work was supported by a Grant-in-Aid for Young Scientists (18K18153), Grant-in-Aid for Transformative Research Areas (B) (20H05743), and Grant-in-Aid for Scientific Research (B) (19H04208) from the Japan Society for the Promotion of Science. This work was further supported by JST PRESTO (JPMJPR20K8). The authors thank Editage for reviewing and editing the manuscript. Publisher Copyright: © 2021 Information Processing Society of Japan.

PY - 2021

Y1 - 2021

N2 - Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REXstar/JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.

AB - Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REXstar/JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.

KW - Evolutionary algorithm

KW - Parameter estimation

KW - Simulation

KW - Systems biology

KW - Usability

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JO - IPSJ Transactions on Bioinformatics

JF - IPSJ Transactions on Bioinformatics

ER -

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

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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