CADLIVE optimizer: web-based parameter estimation for dynamic models

Inoue, Kentaro; Maeda, Kazuhiro; Kato, Yuki; Tonami, Shinpei; TAKAGI, S; Kurata, Hiroyuki

doi:10.1186/1751-0473-7-9

Cited by 3 publications

(1 citation statement)

References 12 publications

(15 reference statements)

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“…There are other numerous applications for parameter estimation [41], [42], [43], [44], [45], [46], including our previously proposed CADLIVE [47], [48], [49], [50], [51]. However, most of them are designed for unconstrained optimizations and/or run on MATLAB or in specially provided environments.…”

Section: Libsres (The Existing Library)mentioning

confidence: 99%

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

Maeda

Boogerd

Kurata

2018

IPSJ Transactions on Bioinformatics

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Kinetic modeling is a powerful tool to understand how a biochemical system behaves as a whole. To develop a realistic and predictive model, kinetic parameters need to be estimated so that a model fits experimental data. However, parameter estimation remains a major bottleneck in kinetic modeling. To accelerate parameter estimation, we developed a C library for real-coded genetic algorithms (libRCGA). In libRCGA, two real-coded genetic algorithms (RCGAs), viz. 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), are implemented in C language and paralleled by Message Passing Interface (MPI). We designed libRCGA to take advantage of high-performance computing environments and thus to significantly accelerate parameter estimation. Constrained optimization formulation is useful to construct a realistic kinetic model that satisfies several biological constraints. libRCGA employs stochastic ranking to efficiently solve constrained optimization problems. In the present paper, we demonstrate the performance of libRCGA through benchmark problems and in realistic parameter estimation problems. libRCGA is freely available for academic usage at http://kurata21.bio.kyutech.ac.jp/maeda/index.html.

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Section: Libsres (The Existing Library)mentioning

confidence: 99%

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

Maeda

Boogerd

Kurata

2018

IPSJ Transactions on Bioinformatics

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CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis

Inoue

Maeda

Miyabe

et al. 2014

Bioprocess Biosyst Eng

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Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

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Dynamic Modeling of Metabolic and Gene Regulatory Systems toward Developing Virtual Microbes

Kurata

Maeda

Matsuoka

2014

J. Chem. Eng. Japan / JCEJ

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CADLIVE optimizer: web-based parameter estimation for dynamic models

Cited by 3 publications

References 12 publications

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis

Dynamic Modeling of Metabolic and Gene Regulatory Systems toward Developing Virtual Microbes

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