Name: ADRIANA MACHADO MALAFAIA DA MATA
Type: MSc dissertation
Publication date: 21/07/2017
Advisor:
Name |
Role |
|---|---|
| WELLINGTON BETENCURTE DA SILVA | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| JULIO CESAR SAMPAIO DUTRA | Co advisor * |
| LUCIA CATABRIGA | Internal Examiner * |
| WELLINGTON BETENCURTE DA SILVA | Advisor * |
Summary: Cancer is a disease arising from the disordered growth of cells. Commonly, antineoplastic chemotherapy is used to treat the most common cancers. In this context, researches have turned to mathematical models that describe the growth of tumor cells with an action of a chemotherapeutic drug. Faced with a variety of models in the literature for this purpose, a method for selecting the most suitable model is necessary. This dissertation studies mathematical models
of cell growth and applies the Approximate Bayesian Computation (ABC) to select the model that best represents the observed data. The ABC algorithm used was deterministic, prioritizing the model selection. To the selected model, the SIR particle filter was applied, which allowed to
improve the parameter estimates. Tumor growth models were studied using ordinary differential equations and the parameters to be assumed as constants. The models were structured from Bicompartmental pharmacokinetics, which allow the study of antineoplastic drugs administered orally. In addition, known tumor growth formulations were used by adding the influence factor of a single dose of chemotherapeutic drug.
