A Model of Nitric Oxide Diffusion Based in Compartmental Systems
p. 172-186
Abstract
The brain is a largely parallel system whose activity is induced by the functional coupling of many nerve cells, the cellular communication and learning. At present, a new type of process for signalling between cells seems to be emerging, the Volume Transmission (VT). Its underlying mechanism is the diffusion of neuroactive substances and diffusible signals, like nitric oxide (NO), in the extracellular space (ECS), extra-synaptic gap which affect the brain activity in a global way.
This paper is dedicated to the Theoretical Framework of the global study framework of NO diffusion (GSFNO). We present a new model of NO diffusion based in compartmental sytems and transport phenomena, which allows to understand how NO functions as a neural signalling molecule. We model the dynamic of NO diffusion from a molecular level where the spatial dimension is discrete. This model is highly powerful for studying and determining the dynamic of NO production and diffusion, both in brain and artificial neural systems, showing the capabilities of NO in cellular signalling and learning and its influence in the anticipatory behaviour. It has category of general formal tool with biological plausibility. We present a short analysis of the model in a three-dimensional environment for properties such as the dynamic of NO release, the self-regulation effect, the diffuse neighbourhoods (DNB), among others.
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References
Bibliographical reference
P. Fernández López, Carmen Paz Suárez-Araujo, P. García Báez and José Luis Simões da Fonseca, « A Model of Nitric Oxide Diffusion Based in Compartmental Systems », CASYS, 18 | 2006, 172-186.
Electronic reference
P. Fernández López, Carmen Paz Suárez-Araujo, P. García Báez and José Luis Simões da Fonseca, « A Model of Nitric Oxide Diffusion Based in Compartmental Systems », CASYS [Online], 18 | 2006, Online since 10 October 2024, connection on 10 November 2024. URL : http://popups.lib.uliege.be/1373-5411/index.php?id=2273
Authors
P. Fernández López
Institute for Cybernetics, University of Las Palmas de Gran Canaria, Spain
Carmen Paz Suárez-Araujo
Institute for Cybernetics, University of Las Palmas de Gran Canaria, Spain
P. García Báez
Dept. of Statistics, Operations Research and Computation, University of la Laguna, Spain
José Luis Simões da Fonseca
Faculty of Medicine, University of Lisboa, Portugal