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Register a new userA Simple Cellular Automaton Model for Influenza A Viral Infections
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Added by
Catherine Beauchemin
Publication date
2004
fields
Biology
and research's language is
English
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Viral kinetics have been extensively studied in the past through the use of spatially homogeneous ordinary differential equations describing the time evolution of the diseased state. However, spatial characteristics such as localized populations of dead cells might adversely affect the spread of infection, similar to the manner in which a counter-fire can stop a forest fire from spreading. In order to investigate the influence of spatial heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of a viral infection has been developed. In this initial phase of the investigation, the CA model is validated against clinical immunological data for uncomplicated influenza A infections. Our results will be shown and discussed.
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In this paper we develop mathematical models for collective cell motility. Initially we develop a model using a linear diffusion-advection type equation and fit the parameters to data from cell motility assays. This approach is helpful in classifying the results of cell motility assay experiments. In particular, this model can determine degrees of directed versus undirected collective cell motility. Next we develop a model using a nonlinear diffusion term that is able capture in a unified way directed and undirected collective cell motility. Finally we apply the nonlinear diffusion approach to a problem in tumor cell invasion, noting that neither chemotaxis or haptotaxis are present in the system under consideration in this article.
A simulation approach to the stochastic growth of bacterial towers is presented, in which a non-uniform and finite nutrient supply essentially determines the emerging structure through elementary chemotaxis. The method is based on cellular automata and we use simple, microscopic, local rules for bacterial division in nutrient-rich surroundings. Stochastic nutrient diffusion, while not crucial to the dynamics of the total population, is influential in determining the porosity of the bacterial tower and the roughness of its surface. As the bacteria run out of food, we observe an exponentially rapid saturation to a carrying capacity distribution, similar in many respects to that found in a recently proposed phenomenological hierarchical population model, which uses heuristic parameters and macroscopic rules. Complementary to that phenomenological model, the simulation aims at giving more microscopic insight into the possible mechanisms for one of the recently much studied bacterial morphotypes, known as towering biofilm, observed experimentally using confocal laser microscopy. A simulation suggesting a mechanism for biofilm resistance to antibiotics is also shown.
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