Defesa de Tesa de Doutorado: Hybrid Modeling Framework for Tumor Growth with Chemotherapy

Orientadores:
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC
Heber Lima da Rocha

Banca Examinadora:
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC (presidente)
Abimael Fernando Dourado Loula - Laboratório Nacional de Computação Científica - LNCC
Álvaro Luiz Gayoso de Azevedo Coutinho - COPPE/UFRJ - UFRJ
Helcio R. B. Orlande - UFRJ - Departamento/Programa de Engenharia Mecânica, POLI/COPPE - POLI/COPPE
Rafael Alves Bonfim de Queiroz - UFJF
Ernesto Augusto Bueno da Fonseca Lima - Laboratório Nacional de Computação Científica - LNCC
Sandra Mara Cardoso Malta - Laboratório Nacional de Computação Científica - LNCC

Resumo:

Cancer is one of the main causes of death in the world, whose mechanisms of origin and growth are not perfectly understood. The mulTple spaTal and temporal scales of this group of diseases make their understanding even more difficult. In this sense, mathemaTcal and computaTonal modeling is a useful tool that contributes to the understanding of the tumor growth dynamics, as well as to the invesTgaTon of the tumor response to different treatment protocols. This research aims to develop a hybrid mathemaTcal and computaTonal model able to characterize the mulTple scales present in tumor growth. We consider that the mechanisms that contribute to the development of cancer act on three scales: Tssue, cellular, and molecular scales. The Tssue and molecular scales are described using conTnuous models, and the cellular scale is represented through a discrete (individual-based) model. In order to invesTgate different mechanisms of response to chemotherapy, we incorporate the chemotherapeuTc drug dispersion in the tumor microenvironment and the signaling dynamics associated with cell cycle control into the hybrid mulTscale model. As a significant novelty, this work develops a framework for invesTgaTng the most suitable therapeuTc protocol to eliminate the tumor and reduce toxicity in a mulTscale context. Due to the hybrid mulTscale model complexity, we firstly determine a surrogate model by applying a data-driven approach to the scenario without chemotherapeuTc drug dispersion. Specifically, we idenTfy a nonlinear dynamical system by developing a combined approach that integrates the Sparse IdenTficaTon of Nonlinear Dynamics (SINDy) method with a global sensiTvity analysis (SA) technique. The proposed SINDy-SA approach is able to discover the most parsimonious tumor growth model that best fits the dynamics informed by the in silico data generated by the complex hybrid model. By incorporaTng a control input term associated with the drug concentraTon in the tumor microenvironment, the idenTfied surrogate model can be invesTgated from the opTmal control point of view. As a preliminary result, we have studied a conTnuous model composed of drug-sensiTve and drug-resistant tumor cells and obtained opTmal controls with administraTon by maximum tolerated dose. The proposed hybrid modeling framework will contribute to the development of effecTve therapeuTc protocols for cancer treatment.