Utiliza este identificador para citar o vincular este elemento: http://hdl.handle.net/10553/36063
Títulos: A digital communication analysis of gene expression of proteins in biological systems: a layered network model view
Autores/as: Cevallos, Yesenia
Molina, Lorena
Santillán, Alex
De Rango, Floriano
Rushdi, Ahmad
Alonso-Hernández, Jesús B. 
Clasificación UNESCO: 2407 Biología celular
33 Ciencias tecnológicas
Palabras clave: Digital communication
Gene expression
Biological communication
Layered network model
Medical applications
Fecha de publicación: 2017
Revistas: Cognitive Computation 
Resumen: Biological communication is a core component of biological systems, mainly presented in the form of evolution, transmitting information from a generation to the next. Unfortunately, biological systems also include other components and functionalities that would cause unwanted information processing and/or communication problems that manifest as diseases. On the other hand, general communication systems, e.g. digital communications, have been well developed and analysed to yield accuracy, high performance, and efficiency. Therefore, we extend the theories of digital communication systems to analyse biological communications. However, in order to accurately model biological communication as digital ones, an analysis of the analogies between both systems is essential. In this work, we propose a novel stacked-layer network model that presents gene expression (i.e. the process by which the information carried by deoxyribonucleic acid or DNA is transformed into the appropriate proteins) and the role of the Golgi apparatus in transmitting these proteins to a target organ. This is analogous to the transmit process in digital communications where a transmitting device in some network would send digital information to a destination/receiver device in another network through a router. The proposed stacked-layer network model exploits key networks' theories and applies them into the broad field genomic analysis, which in turn can impact our understanding and use of medical methods. For example, it would be useful in detecting a target site (e.g. tumour cells) for drug therapy, improving the targeting accuracy (addressing), and reducing side effects in patients from health and socio-economic perspectives. Besides improving our understanding of biological communication systems, the proposed model unleashes the true duality between digital and biological communication systems. Therefore, it could be deployed into leveraging the advantages and efficiencies of biological systems into digital communication systems as well and to further develop efficient models that would overcome the disadvantages of either system.
URI: http://hdl.handle.net/10553/36063
ISSN: 1866-9956
DOI: 10.1007/s12559-016-9434-4
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