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The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity

Genomic and clinical evidence suggest a major role of microRNAs (miRNAs) in the regulatory mechanisms of gene expression, with a clear impact on development and physiology; miRNAs are a class of endogenous 22-25 nt single-stranded RNA molecules, that negatively regulate gene expression post-transcri...

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Autores principales: Ripoli, Andrea, Rainaldi, Giuseppe, Rizzo, Milena, Mercatanti, Alberto, Pitto, Letizia
Formato: Texto
Lenguaje:English
Publicado: Bentham Science Publishers Ltd 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2945000/
https://www.ncbi.nlm.nih.gov/pubmed/21286312
http://dx.doi.org/10.2174/138920210791616707
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author Ripoli, Andrea
Rainaldi, Giuseppe
Rizzo, Milena
Mercatanti, Alberto
Pitto, Letizia
author_facet Ripoli, Andrea
Rainaldi, Giuseppe
Rizzo, Milena
Mercatanti, Alberto
Pitto, Letizia
author_sort Ripoli, Andrea
collection PubMed
description Genomic and clinical evidence suggest a major role of microRNAs (miRNAs) in the regulatory mechanisms of gene expression, with a clear impact on development and physiology; miRNAs are a class of endogenous 22-25 nt single-stranded RNA molecules, that negatively regulate gene expression post-transcriptionally, by imperfect base pairing with the 3’ UTR of the corresponding mRNA target. Because of this imperfection, each miRNA can bind multiple targets, and multiple miRNAs can bind the same mRNA target; although digital, the miRNAs control mechanism is characterized by an imprecise action, naturally understandable in the theoretical framework of fuzzy logic. A major practical application of fuzzy logic is represented by the design and the realization of efficient and robust control systems, even when the processes to be controlled show chaotic, deterministic as well unpredictable, behaviours. The vagueness of miRNA action, when considered together with the controlled and chaotic gene expression, is a hint of a cellular fuzzy control system. As a demonstration of the possibility and the effectiveness of miRNA based fuzzy mechanism, a fuzzy cognitive map -a mathematical formalism combining neural network and fuzzy logic- has been developed to study the apoptosis/proliferation control performed by the miRNA-17-92 cluster/E2F1/cMYC circuitry. When experimentally demonstrated, the concept of fuzzy control could modify the way we analyse and model gene expression, with a possible impact on the way we imagine and design therapeutic intervention based on miRNA silencing.
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spelling pubmed-29450002011-02-01 The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity Ripoli, Andrea Rainaldi, Giuseppe Rizzo, Milena Mercatanti, Alberto Pitto, Letizia Curr Genomics Article Genomic and clinical evidence suggest a major role of microRNAs (miRNAs) in the regulatory mechanisms of gene expression, with a clear impact on development and physiology; miRNAs are a class of endogenous 22-25 nt single-stranded RNA molecules, that negatively regulate gene expression post-transcriptionally, by imperfect base pairing with the 3’ UTR of the corresponding mRNA target. Because of this imperfection, each miRNA can bind multiple targets, and multiple miRNAs can bind the same mRNA target; although digital, the miRNAs control mechanism is characterized by an imprecise action, naturally understandable in the theoretical framework of fuzzy logic. A major practical application of fuzzy logic is represented by the design and the realization of efficient and robust control systems, even when the processes to be controlled show chaotic, deterministic as well unpredictable, behaviours. The vagueness of miRNA action, when considered together with the controlled and chaotic gene expression, is a hint of a cellular fuzzy control system. As a demonstration of the possibility and the effectiveness of miRNA based fuzzy mechanism, a fuzzy cognitive map -a mathematical formalism combining neural network and fuzzy logic- has been developed to study the apoptosis/proliferation control performed by the miRNA-17-92 cluster/E2F1/cMYC circuitry. When experimentally demonstrated, the concept of fuzzy control could modify the way we analyse and model gene expression, with a possible impact on the way we imagine and design therapeutic intervention based on miRNA silencing. Bentham Science Publishers Ltd 2010-08 /pmc/articles/PMC2945000/ /pubmed/21286312 http://dx.doi.org/10.2174/138920210791616707 Text en ©2010 Bentham Science Publishers Ltd. http://creativecommons.org/licenses/by/2.5/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/), which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Ripoli, Andrea
Rainaldi, Giuseppe
Rizzo, Milena
Mercatanti, Alberto
Pitto, Letizia
The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity
title The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity
title_full The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity
title_fullStr The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity
title_full_unstemmed The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity
title_short The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity
title_sort fuzzy logic of microrna regulation: a key to control cell complexity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2945000/
https://www.ncbi.nlm.nih.gov/pubmed/21286312
http://dx.doi.org/10.2174/138920210791616707
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