A Bayesian framework of reaction networks for dynamical population models

• Ano de defesa: 2021
• Autor(a) principal: Araujo, Guilherme David
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Bayesian probability; Eco-evolução; Eco-evolution; Especialização individual; Individual specialization; Markov jump processes; Probabilidade Bayesiana; Processos de pulo Markovianos; Reaction Networks; Redes de reação
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/76/76131/tde-15102021-164903/

The tradition of mathematical modeling in the biological sciences is yet to reach a mature state in many fields. The most pressing issues are the difficulty in first translating the complexities of life to quantitative modeling terms and the lack of robust frameworks providing structure and cohesion to the building and interpretation of models. In particular, the quantitative study of biological populations, as for example in behavioral ecology and evolutionary dynamics, is composed of a set of scattered methodologies that generate models without an anchored conceptual foundation. Modeling concepts are often ambiguous and do not directly translate to actual biological terms. Inspired by modeling advances in biochemistry, this thesis aims at the conceptualization and application of a general modeling framework for dynamical populations in biology. Combining a Bayesian probabilistic paradigm with the theory of reaction networks, I was able to structure a framework of relational interactions among populations, one that extends biochemical applications to all types of populations, unifying and generalizing existing methods in eco-evolutionary dynamics. The framework comprises both stochastic and deterministic models, and also their connection; it considers the connection with data through statistical model determination and brings a large emphasis on unambiguous design-informed dynamical equations. I validated the framework through applications to genetic regulation, parental investment, and ecological predator-prey dynamics.