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

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

doi:10.2197/ipsjtbio.11.31

Cited by 6 publications

(7 citation statements)

References 56 publications

(111 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation (3) is the standard formalism of constrained optimization problems, and a wide variety of optimization algorithms and software (e.g. 53–55 ) have been proposed to deal with them. We employ a genetic algorithm (GA) named IS-SR-REX star /JGG (Iterative Start and Stochastic Ranking, Real-coded Ensemble Crossover star/Just Generation Gap).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

et al. 2019

Self Cite

View full text Add to dashboard Cite

The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a ‘rubber band method’ to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli' s AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.

show abstract

Section: Methodsmentioning

confidence: 99%

“…IS-SR-REX star /JGG has been a slightly modified from SR-REX star /JGG. 53 For details, see Section 4.3 of Supplementary Information. Here we provide a brief description of how IS-SR-REX star /JGG solves the constrained optimization problem [Eq.…”

Section: Methodsmentioning

confidence: 99%

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Please note that our previous software (C library) libR-CGA [31] lacks GUI, SBML, and PEtab support, although it can achieve a greater speed-up factor and thus has an advantage over RCGAToolbox in parallel computations (Section 2.2 of the Supplementary Material).…”

Section: Usabilitymentioning

confidence: 99%

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

Maeda

Boogerd

Kurata

2021

IPSJ Transactions on Bioinformatics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In global optimization, the values of kinetic parameters are optimized so that models best fit the experimental data. Although different algorithms and software tools have been developed (e.g., [6][7][8][9][10][11][12]), the global optimization approach is time-consuming due to the large number of model parameters, nonlinear dynamics, and multiple local optima [13]. The conventional approach often yields unrealistic parameter values (e.g., extremely small or large values) because it simply seeks a better fit to the experimental data.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 6 publications

References 56 publications

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

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

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

Contact Info

Product

Resources

About