P085 Biological validation of a computational model of nitric oxide dynamics by emulating the nitric oxide diffusion experiment in the endothelium
Pablo Fernández-López1, Ylermi Cabrera-León1, Patricio García Báez1,2, Scott McElroy3, Salvador Dura-Bernal3andCarmen Paz Suárez-Araujo*1
1Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Parque Científico Tecnológico, Campus Universitario de Tafira, Las Palmas de Gran Canaria, 35017, Canary Islands, Spain.
2Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Camino San Francisco de Paula, 19, Escuela Superior de Ingeniería y Tecnología, San Cristóbal de La Laguna, 38200, Canary Islands, Spain.
3State University of New York (SUNY) Downstate Health Sciences University, 450 Clarkson Avenue, Brooklyn, NY, USA 11203.
*Email: carmenpaz.suarez@ulpgc.es
Understanding how the brain works, how it is structured and how it computes is one of the goals of computational neuroscience. An essential step in this direction is to understand the cellular communication that enables the transition from nerve cells to cognition.
It is now accepted that the links between neurons are not only established by synaptic connection, but also by the confluence of different cellular signals that affect global brain activity, with the underlying mechanism being the diffusion of neuroactive substances into the extracellular space (ECS). One of these substances is the free radical gas nitric oxide (NO), which, in turn, determines a new type of information transmission: the volume transmission (VT). VT is a non-simple form of short- and long-distance communication that acts not only as a microenvironment to separate nerve cells, but also as an information channel [1, 2]. NO is a signaling molecule that is synthesized in a number of tissues by NO synthases and has the ability to regulate its own production. It is lipid soluble, membrane permeable and has a high diffusivity in both aqueous and lipid environments.
In the absence of definitive experimental data to understand how NO functions as a neuronal signalling molecule, we have developed a computational model of NO diffusion based on non-negative and compartmental dynamical systems and transport phenomena [3].
The proposed model has been validated in the biological environment, specifically in the endothelium. In this work, the biological validation is approached by reproducing the experiment performed by Tadeuzs et al, 1993 [4] on NO diffusion in the aorta. We implement our model with two compartments, using real measurements of NO synthesis and diffusion processes in the endothelial cell and in the smooth muscle cells of the aorta at a distance of 100 ± 2 µm between them. A fitting procedure to the observed NO dynamics was executed, and hypothesis related to the different processes in the NO dynamics were provided.
Our results provide evidence that the compartmental model of NO diffusion has allowed the design of a computational framework [5] to study and determine the dynamics of synthesis, diffusion and self-regulation of NO in the brain and in artificial environments. We have also shown that this model is very powerful because it allows to incorporate all the biological features and existing constraints in NO release and diffusion and in the environment where NO diffusion processes take place.
Finally, it has been shown that our model is an important tool for designing and interpreting biological experiments on the underlying processes of NO dynamics, NO behaviour and its impact on both brain structure and function and artificial neural systems.
Acknowledgements
This work has been funded by the Consejería de Vicepresidencia 1ª y de O. P., Inf., T. y M. del Cabildo de GC under Grant Nº “23/2021”, as well as by the ‘Marie Curie Chair’ under Grant Nº “38/2023”, and ‘Marie Curie Chair’ under Grant Nº “CGC/2024/9655”.The latter was funded by the Consejeria de Vicepresidencia 1ª y de Gobierno de O. P. e Inf., Arq. y V. del Cabildo de GC.
References
[1] https://doi.org/10.1177/107385849700300113.
[2] https://doi.org/10.1016/j.neuroscience.2004.06.077.
[3] https://doi.org/10.1007/978-3-319-26555-1_59.
[4] https://doi.org/10.1006/bbrc.1993.1914.
[5] https://doi.org/10.1063/1.1291268.
