Simultaneous Model Selection and Model Calibration for the Proliferation of Tumor and Normal Cells During In Vitro Chemotherapy Experiments

Journal of computational biology: A journal of computational molecular cell biology

Viviane O.F.Lione^4¹  TaynÁ C.S.Cardoso^4²  Helcio R.B.Orlande^3,2³  JosÉ M.J.Costa^1,2⁴  Antonio G.F.Lima^4⁵ 

[1] Address correspondence to: Dr. Helcio R.B. Orlande, Department of Mechanical Engineering, Federal University of Rio de Janeiro–UFRJ, Caixa Postal 68503, Rio de Janeiro 21941972, Brazil^3;Department of Bioengineering, School of Engineering, University of Santiago de Cali, Cali, Colombia^5;Department of Mechanical Engineering, Federal University of Rio de Janeiro—UFRJ, Rio de Janeiro, Brazil^2;Department of Statistics, Federal University of Amazonas—UFAM, Manaus, Brazil^1;Laboratory of Pharmaceutical Bioassays, Faculty of Pharmacy, Federal University of Rio de Janeiro—UFRJ, Rio de Janeiro, Brazil^4

关键词: approximate Bayesian computation;    chemotherapy;    DU-145 cells;    RAW 264.7 cells;    state estimation.;   

DOI  :  10.1089/cmb.2017.0130

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

In vitro experiments were conducted in this work to analyze the proliferation of tumor (DU-145) and normal (macrophage RAW 264.7) cells under the influence of a chemotherapeutic drug (doxorubicin). Approximate Bayesian Computation (ABC) was used to select among four competing models to represent the number of cells and to estimate the model parameters, based on the experimental data. For one case, the selected model was validated in a replicated experiment, through the solution of a state estimation problem with a particle filter algorithm, thus demonstrating the robustness of the ABC procedure used in this work.

【 授权许可】

Unknown