Seminário de Avaliação - Série A: Hybrid Multiscale Modeling for Tumor Growth with Chemotherapy

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

Banca Examinadora:
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC (presidente)
Abimael Fernando Dourado Loula - Laboratório Nacional de Computação Científica - LNCC
Sandra Mara Cardoso Malta - Laboratório Nacional de Computação Científica - LNCC
Álvaro Luiz Gayoso de Azevedo Coutinho - COPPE/UFRJ - UFRJ

Suplentes:
Renato Simões Silva - 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 a re not perfectly understood. The multiple spatial and temporal scales of this group of diseases make their understanding even more difficult. In this sense, mathematical and computational modeling is a useful tool that contributes to the understanding of the tumor growth dynamics, as well as to the investigation of the tumor response to different treatment protocols. This Ph.D. dissertation aims to develop a hybrid mathematical and computational model able to characterize the multiple scales present in tumor growth. We consider that the mechanisms that contribute to the development of cancer act in three scales: tissue, cellular, and molecular. The tissue and molecular scales are described using continuous models, and the cellular scale is represented through a discrete (individual-based) model. In order to investigate different mechanisms of response to chemotherapies, we incorporate the chemotherapeutic drug dispersion in the tumor microenvironment and the signaling dynamics assoc iated with cell cycle control to the hybrid multiscale model. As a significant novelty, this Ph.D. dissertation aims to investigate procedures to obtain the most suitable therapeutic protocol to eliminate the tumor and reduce toxicity in a multiscale context. Due to the hybrid multiscale model complexity, we firstly determine a surrogate model by applying a data-driven approach to the scenario without chemotherapeutic drug dispersion. Specifically, we identify a nonlinear dynamical system by using a combined approach that integrates the Sparse Identification of Nonlinear Dynamics (SINDy) method to a global sensitivity 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. By incorporating a control input term associated with the drug concentration in the tumor microenvironment, the identified surrogate model will be investigated from the optimal control point of view. Initially, we have studied a continuous model composed of drug-sensitive and drug-resistant tumor cells and obtained optimal controls with administration by maximum tolerated dose. In a second scenario, this approach will be translated into the hybrid model through the differentiation of tumor cells according to the drug resistance. The proposed modeling approach will contribute to the development of effective therapeutic protocols for cancer treatment.