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Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks

BACKGROUND: Cellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes. An increasing amount of experimental results show that despite the large number of genes participating in transcriptional programs of cellu...

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Detalles Bibliográficos
Autores principales: Crespo, Isaac, Perumal, Thanneer M, Jurkowski, Wiktor, del Sol, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878265/
https://www.ncbi.nlm.nih.gov/pubmed/24350678
http://dx.doi.org/10.1186/1752-0509-7-140
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author Crespo, Isaac
Perumal, Thanneer M
Jurkowski, Wiktor
del Sol, Antonio
author_facet Crespo, Isaac
Perumal, Thanneer M
Jurkowski, Wiktor
del Sol, Antonio
author_sort Crespo, Isaac
collection PubMed
description BACKGROUND: Cellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes. An increasing amount of experimental results show that despite the large number of genes participating in transcriptional programs of cellular phenotypes, only few key genes, which are coined here as reprogramming determinants, are required to be directly perturbed in order to induce cellular reprogramming. However, identification of reprogramming determinants still remains a combinatorial problem, and the state-of-art methods addressing this issue rests on exhaustive experimentation or prior knowledge to narrow down the list of candidates. RESULTS: Here we present a computational method, without any preliminary selection of candidate genes, to identify reduced subsets of genes, which when perturbed can induce transitions between cellular phenotypes. The method relies on the expression profiles of two stable cellular phenotypes along with a topological analysis stability elements in the gene regulatory network that are necessary to cause this multi-stability. Since stable cellular phenotypes can be considered as attractors of gene regulatory networks, cell fate and cellular reprogramming involves transition between these attractors, and therefore current method searches for combinations of genes that are able to destabilize a specific initial attractor and stabilize the final one in response to the appropriate perturbations. CONCLUSIONS: The method presented here represents a useful framework to assist researchers in the field of cellular reprogramming to design experimental strategies with potential applications in the regenerative medicine and disease modelling.
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spelling pubmed-38782652014-01-03 Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks Crespo, Isaac Perumal, Thanneer M Jurkowski, Wiktor del Sol, Antonio BMC Syst Biol Methodology Article BACKGROUND: Cellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes. An increasing amount of experimental results show that despite the large number of genes participating in transcriptional programs of cellular phenotypes, only few key genes, which are coined here as reprogramming determinants, are required to be directly perturbed in order to induce cellular reprogramming. However, identification of reprogramming determinants still remains a combinatorial problem, and the state-of-art methods addressing this issue rests on exhaustive experimentation or prior knowledge to narrow down the list of candidates. RESULTS: Here we present a computational method, without any preliminary selection of candidate genes, to identify reduced subsets of genes, which when perturbed can induce transitions between cellular phenotypes. The method relies on the expression profiles of two stable cellular phenotypes along with a topological analysis stability elements in the gene regulatory network that are necessary to cause this multi-stability. Since stable cellular phenotypes can be considered as attractors of gene regulatory networks, cell fate and cellular reprogramming involves transition between these attractors, and therefore current method searches for combinations of genes that are able to destabilize a specific initial attractor and stabilize the final one in response to the appropriate perturbations. CONCLUSIONS: The method presented here represents a useful framework to assist researchers in the field of cellular reprogramming to design experimental strategies with potential applications in the regenerative medicine and disease modelling. BioMed Central 2013-12-19 /pmc/articles/PMC3878265/ /pubmed/24350678 http://dx.doi.org/10.1186/1752-0509-7-140 Text en Copyright © 2013 Crespo et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Crespo, Isaac
Perumal, Thanneer M
Jurkowski, Wiktor
del Sol, Antonio
Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
title Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
title_full Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
title_fullStr Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
title_full_unstemmed Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
title_short Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
title_sort detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878265/
https://www.ncbi.nlm.nih.gov/pubmed/24350678
http://dx.doi.org/10.1186/1752-0509-7-140
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