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

K Maeda, Fred C. Boogerd, K Kurata

Research output: Contribution to JournalArticleAcademicpeer-review


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
Original languageEnglish
Pages (from-to)31-40
Number of pages10
JournalIPSJ Transactions on Bioinformatics
Publication statusPublished - 13 Sept 2018


Acknowledgments This work was supported by Grant-in-Aid for Young Scientists (18K18153) and partially supported by Grant-in-Aid for Scientific Research (B) (16H02898) from Japan Society for the Promotion of Science and Aid for Research Abroad from Yoshida Foundation for Science and Technology. The super-computing resource was provided by Human Genome Center, Institute of Medical Science, University of Tokyo. We thank Dr. Yu Matsuoka, Dr. Midori Iida, and Koji Maeda for their help.

FundersFunder number
Japan Society for the Promotion of Science
Foundation for Science and Technology


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