Modelagem analítico-estocástica de doenças priônicas
AUTOR(ES)
Arlan da Silva Ferreira
DATA DE PUBLICAÇÃO
2005
RESUMO
In this work one studies several characteristics of the so called prionic diseases, a pathogeny caused by protein molecules, known as Prions. Prions can exist in two different molecular conformations. The first of these, known as PrPC, is normally found in mammals, whilst the second, known as PrPSc, is the rare pathogenic form, responsible for the development of the disease. The replication of the pathogen is carried out through a structural transformation from the first to the second form, which acts as a template inducing the transformation. In order to study this problem, one first employs a simple analytical model capable of reproducing, qualitatively, the results observed for the survival time of hamsters inoculated with the infectious agent. Making use of the Michaelis-Menten equation, a new analytical model is then developed, which provides a connection between the model rate constant with the rate constants widely known from biochemical reactions. With this new model it was then possible to estimate the average value of the model rate constant for hamsters, which was found to be within the numerical interval known for enzymatic reactions. Computing simulations were then developed as an Automata Cellular in a square lattice. In this case one also took into consideration the tendency of aggregation of the pathogenic form, which would be of a very difficult analytical implemention. The simulation results for the distribution of the frequency of the incubation time produced an excellent fitting with the observed data for the bovine form of the disease (BSE), showing that the computational model is appropriate for describing the epidemiology of the prionic reactions. This fact led to the development of a stochastic model, with analytical solution, which provided a functional form for the BSE incubation time distribution. The analytical curve obtained with such a model, produced an excellent fitting to the observed data, with the use of one single fitting parameter.
ASSUNTO(S)
stochastic processes processos estocásticos cellular automata fisica da materia condensada doenças priônicas prion diseases automata celular
