OBIYagns: a grid-based biochemical simulator with a parameter estimator

Kimura, Shiushichi; Kawasaki, Takuji; Hatakeyama, Mariko; Naka, Takashi; Konishi, Fumikazu; Konagaya, Akihiko

doi:10.1093/bioinformatics/bth122

Cited by 15 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since a search parameter space is very large, the obigrid system (RIKEN, Yokohama) is used to greatly increase a calculation speed, where dozens of computers are available simultaneously[22]. Dynamic simulation programs are written in C. The Runge-Kutta method is employed.…”

Section: Methodsmentioning

confidence: 99%

Mathematical Identification of Critical Reactions in the Interlocked Feedback Model

2007

View full text Add to dashboard Cite

Dynamic simulations are necessary for understanding the mechanism of how biochemical networks generate robust properties to environmental stresses or genetic changes. Sensitivity analysis allows the linking of robustness to network structure. However, it yields only local properties regarding a particular choice of plausible parameter values, because it is hard to know the exact parameter values in vivo. Global and firm results are needed that do not depend on particular parameter values. We propose mathematical analysis for robustness (MAR) that consists of the novel evolutionary search that explores all possible solution vectors of kinetic parameters satisfying the target dynamics and robustness analysis. New criteria, parameter spectrum width and the variability of solution vectors for parameters, are introduced to determine whether the search is exhaustive. In robustness analysis, in addition to single parameter sensitivity analysis, robustness to multiple parameter perturbation is defined. Combining the sensitivity analysis and the robustness analysis to multiple parameter perturbation enables identifying critical reactions. Use of MAR clearly identified the critical reactions responsible for determining the circadian cycle in the Drosophila interlocked circadian clock model. In highly robust models, while the parameter vectors are greatly varied, the critical reactions with a high sensitivity are uniquely determined. Interestingly, not only the per-tim loop but also the dclk-cyc loop strongly affect the period of PER, although the dclk-cyc loop hardly changes its amplitude and it is not potentially influential. In conclusion, MAR is a powerful method to explore wide parameter space without human-biases and to link a robust property to network architectures without knowing the exact parameter values. MAR identifies the reactions critically responsible for determining the period and amplitude in the interlocked feedback model and suggests that the circadian clock intensively evolves or designs the kinetic parameters so that it creates a highly robust cycle.

show abstract

Section: Methodsmentioning

confidence: 99%

Mathematical Identification of Critical Reactions in the Interlocked Feedback Model

2007

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Genetic algorithms are often used for estimating optimal parameter fitting to biological experimental results [18,19]. Genetic algorithms exhibit high degrees of parallelism, since they require multiple trials with various initial conditions as well as fitting function evaluation for each individual on each generation.…”

Section: High-throughput Numerical Processingmentioning

confidence: 99%