EVENTO
Hybrid Multiscale Modeling for Tumor Growth with Chemotherapy
Tipo de evento: Seminário de Avaliação - Série A
Cancer is one of the main causes of death in the world, whose mechanisms of origin and growth are 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 associated 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.Para assistir acesse: https://us02web.zoom.us/j/88562137348?pwd=VXpGOEFhNTlCeVQwSmxjM2tyQ2szUT09
Data Início: 19/04/2022 Hora: 14:00 Data Fim: 19/04/2022 Hora: 18:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Webinar
Aluno: Gustavo Taiji Naozuka - Laboratório Nacional de Computação Científica - LNCC
Orientador: Heber Lima da Rocha - - Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Abimael Fernando Dourado Loula - Laboratório Nacional de Computação Científica - LNCC Alvaro Luiz Gayoso de Azeredo Coutinho - Universidade Federal do Rio de Janeiro - COPPE/UFRJ Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC Sandra Mara Cardoso Malta - Laboratório Nacional de Computação Científica - LNCC
Suplente Banca Examinadora: Renato Simões Silva - Laboratório Nacional de Computação Científica - LNCC
