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Master's Dissertation
DOI
https://doi.org/10.11606/D.45.2011.tde-05072011-041153
Document
Author
Full name
Manuel Alejandro Gonzalez Navarrete
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2011
Supervisor
Committee
Neves, Eduardo Jordao (President)
Garcia, Nancy Lopes
Iambartsev, Anatoli
Title in Portuguese
Sistemas de partículas interagentes dependentes de tipo e aplicações ao estudo de redes de sinalização biológica
Keywords in Portuguese
campo médio
convergência quase certa
dinâmica estocástica não reversível
Modelo de spins estocástico dependente de tipo
processo de perfil de densidade
rede de sinalização biológica
sistema dinâmico
Abstract in Portuguese
Neste trabalho estudamos os type-dependent stochastic spin models propostos por Fernández et al., os que chamaremos de modelos de spins estocástico dependentes de tipo, e que foram usados para modelar redes de sinalização biológica. A modelagem original descreve a evolução macroscópica de um modelo de spin-flip de tamanho finito com k tipos de spins, possuindo um número arbitrário de estados internos, que interagem através de uma dinâmica estocástica não reversível. No limite termodinânico foi provado que, em um intervalo de tempo finito as trajetórias convergem quase certamente para uma trajetória determinística, dada por uma equação diferencial de primeira ordem. Os comportamentos destes sistemas dinâmicos podem incluir bifurcações, relacionadas às transições de fase do modelo. O nosso objetivo principal foi de estender os modelos de spins com dinâmica de Glauber utiliza- dos pelos autores, permitindo trocas múltiplas dos spins. No contexto biológico tentamos incluir situações nas quais moléculas de tipos diferentes trocam simultaneamente os seus estados internos. Utilizando diversas técnicas, como as de grandes desvíos e acoplamento, tem sido possível demonstrar a convergência para o sistema dinâmico associado.
Title in English
Type-dependent interacting particle systems and their applications in the study of signaling biological networks
Keywords in English
almost sure convergence
biological signaling network
density-profile process
dynamical system
mean field
non-reversible stochastic dynamics
Type-dependent stochastic spin model
Abstract in English
We study type-dependent stochastic spin models proposed by Fernández et al., which were used to model biological signaling networks. The original modeling setup describes the macroscopic evolution of a finite-size spin-flip model with k types of spins with arbitrary number of internal states interacting through a non-reversible stochastic dynamics. In the thermodynamic limit it was proved that, within arbitrary finite time-intervals, the path converges almost surely to a deterministic trajectory determined by a first-order (non-linear) differential equation. The behavior of the associated dynamical system may include bifurcations, associated to phase transitions in the statistical mechanical setting. Our aim is to extend the spin model with Glauber dynamics, to allow multiple spin-flips. In the biological context we included situations in which molecules of different types simultaneously change their internal states. Using several methods, such as large deviations and coupling, we prove the convergence theorem.
 
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Publishing Date
2011-07-20
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • GONZALEZ NAVARRETE, Manuel. Interacting Particles Systems describing Biological Signaling Networks. In Escola Brasileira de Probabilidade (EBP), 15, Mambucaba, Brasil, 2011.
  • GONZALEZ NAVARRETE, Manuel. Type-dependent interactingparticle systems and their applications in the study of signaling biologi. In 35th Conference on Stochastic Processes and their Applications (SPA), 35, Oaxaca, Mexico, 2011.
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