Model selection and parameter estimation in tumor growth models using approximate Bayesian computation-ABC

Costa, José Mir Justino da; Orlande, Helcio R. B.; Silva, Wellington Betencurte da

doi:10.1007/s40314-017-0479-0

Cited by 16 publications

(10 citation statements)

References 29 publications

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“…The Approximate Bayesian Computation (ABC) algorithm is an efficient tool to infer the posterior distributions when the likelihood function is computationally too expensive to evaluate. It has been successfully implemented in various fields of research (da Costa et al 2018;Liepe et al 2014;Loiola et al 2020;. With the ABC technique, the prior information about the parameters is taken into account.…”

Section: Solving the Inverse Problem Combined With Model Selectionmentioning

confidence: 99%

Parameter estimation and model selection for water sorption in a wood fibre material

et al. 2020

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The sorption curve is an essential feature for the modelling of heat and mass transfer in porous building materials. Several models have been proposed in the literature to represent the amount of moisture content in the material according to the water activity (or capillary pressure) level. These models are based on analytical expressions and few parameters that need to be estimated by inverse analysis. This article investigates the reliability of eight models through the accuracy of the estimated parameters. For this, experimental data for a wood fibre material are generated with special attention to the stop criterion to capture long time kinetic constants. Among five sets of measurements, the best estimate is computed. The reliability of the models is then discussed. After proving the theoretical identifiability of the unknown parameters for each model, the primary identifiability is analysed. It evaluates whether the parameters influence on the model output is sufficient to proceed the parameter estimation with accuracy. For this, a continuous derivative-based approach is adopted. Seven models have a low primary identifiability for at least one parameter. Indeed, when estimating the unknown parameters using the experimental observations, the parameters with low primary identifiability exhibit large uncertainties. Finally, an Approximation Bayesian Computation algorithm is used to simultaneously select the best model and estimate the parameters that best represent the experimental data. The thermodynamic and Feng-Xing models, together with a proposed model in this work, were the best ones selected by this algorithm.

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Section: Solving the Inverse Problem Combined With Model Selectionmentioning

confidence: 99%

Parameter estimation and model selection for water sorption in a wood fibre material

et al. 2020

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“…Such methodology, named Approximate Bayesian Computation (ABC), does not require explicit evaluation of likelihoods for parameter inference and also allows for model selection by combining it with a sequential Monte Carlo method (ABC-SMC). This approach was successfully used in [12] for comparing tumor growth models with and without chemotherapy using hypothetical tumor cells data. Other applications in system biology can be found in [18,21].…”

Section: Introductionmentioning

confidence: 99%

Model Comparison and Uncertainty Quantification in Tumor Growth

Naozuka

Paixão

Silva

et al. 2021

TCAM

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Mathematical and computational modeling have been increasingly applied in many areas of cancer research, aiming to improve the understanding of tumorigenic mechanisms and to suggest more effective therapy protocols. The mathematical description of the tumor growth dynamics is often made using the exponential, logistic, and Gompertz models. However, recent literature has suggested that the Allee effect may play an important role in the early stages of tumor dynamics, including cancer relapse and metastasis. For a model to provide reliable predictions, it is necessary to have a rigorous evaluation of the uncertainty inherent in the modeling process. In this work, our main objective is to show how a model framework that integrates sensitivity analysis, model calibration, and model selection techniques can improve and systematically characterize model and data uncertainties. We investigate five distinct models with different complexities, which encompass the exponential, logistic, Gompertz, and weak and strong Allee effects dynamics. Using tumor growth data published in the literature, we perform a global sensitivity analysis, apply a Bayesian framework for parameter inference, evaluate the associated sensitivity matrices, and use different information criteria for model selection (First- and Second-Order Akaike Information Criteria and Bayesian Information Criterion). We show that such a wider methodology allows having a more detailed picture of each model assumption and uncertainty, calibration reliability, ultimately improving tumor mathematical description. The used in vivo data suggested the existence of both a competitive effect among tumor cells and a weak Allee effect in the growth dynamics. The proposed model framework highlights the need for more detailed experimental studies on the influence of the Allee effect on the analyzed cancer scenario.

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“…These models have been applied for tumors, since in cancer cells the proliferation process is increased due to the abnormal metabolic activity [3]. Costa et al [4], for example, have used one of these proliferation models to represent the behavior of prostate cancer cells (DU-145) in vitro. In addition, they have analyzed a chemotherapy treatment using doxorubicin (DOX).…”

Section: Introductionmentioning

confidence: 99%

“…The goal of this work is to select among four continuous models the one that better represents in vitro experimental data of the proliferation of DU-145 human prostate cancer cells. In order to perform this analysis, the Approximate Bayesian Computation (ABC) algorithm of Toni et al [6] is applied for model selection and calibration, since this algorithm is robust and indicated for cases that the likelihood is not exactly known [4], such as in this work.…”

Section: Introductionmentioning

confidence: 99%

Approximate Bayesian Computation Applied to Model Selection and Parameter Calibration in Cell Proliferation

Silva¹,

Loiola²,

Costa³

et al. 2021

14th WCCM-ECCOMAS Congress

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Approximate Bayesian Computation is used in this work for the selection and calibration of cell proliferation models. Four competing models based on ordinary differential equations are analyzed, by using the measurements of the proliferation of DU-145 prostate cancer viable cells during seven days. The selection criterion of the ABC algorithm is based on the Euclidean distance between the model prediction and the experimental observations. The Richards Model and the Generalized Logistic Model were selected by the ABC algorithm used in this work, providing accurate estimates of the evolution of the number of viable cells. Bayes factor revealed that there was no evidence in favor of any of these two selected models.

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Model selection and parameter estimation in tumor growth models using approximate Bayesian computation-ABC

Cited by 16 publications

References 29 publications

Parameter estimation and model selection for water sorption in a wood fibre material

Parameter estimation and model selection for water sorption in a wood fibre material

Model Comparison and Uncertainty Quantification in Tumor Growth

Approximate Bayesian Computation Applied to Model Selection and Parameter Calibration in Cell Proliferation

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