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

Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

doi:10.1007/s00449-014-1167-8

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The simulator with instructions is freely available at: http://www.cadlive.jp/cadlive_main/Softwares/DSsimulator/DynamicSensitivity.html. Since the simulator employs our own application of the partial differentiation converter [20, 21], it does not require any options other than the main body of Matlab. Numerical simulation and subsequent statistical analysis were performed by MATLAB ® R2013a version 8.1 (MathWorks 2013) on a personal computer using Windows 7 (CPU: Intel ® Core ™ i7-2760QM 2.40 GHz, RAM: 8.00 GByte).…”

Section: Methodsmentioning

confidence: 99%

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity

et al. 2017

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The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK*) and Akt activity (Akt*), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK* and Akt* change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK* and Akt*. ERK* increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt* had a gradual wide-range response to HRG and a blunt response to EGF. Akt* was sensitive to perturbations of intracellular kinetics, while ERK* was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK* and Akt* in response to the dose of EGF and HRG, illustrated the response characteristics of ERK* and Akt*, and estimated mechanisms for generating robustness in the ErbB signaling network.

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Section: Methodsmentioning

confidence: 99%

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity

et al. 2017

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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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“…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

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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.

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

Cited by 7 publications

References 15 publications

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity

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

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

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