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Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in ord...

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Autores principales: Bersanelli, Matteo, Mosca, Ettore, Milanesi, Luciano, Bazzani, Armando, Castellani, Gastone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7021762/
https://www.ncbi.nlm.nih.gov/pubmed/32060296
http://dx.doi.org/10.1038/s41598-020-59036-w
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author Bersanelli, Matteo
Mosca, Ettore
Milanesi, Luciano
Bazzani, Armando
Castellani, Gastone
author_facet Bersanelli, Matteo
Mosca, Ettore
Milanesi, Luciano
Bazzani, Armando
Castellani, Gastone
author_sort Bersanelli, Matteo
collection PubMed
description In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.
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spelling pubmed-70217622020-02-24 Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach Bersanelli, Matteo Mosca, Ettore Milanesi, Luciano Bazzani, Armando Castellani, Gastone Sci Rep Article In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context. Nature Publishing Group UK 2020-02-14 /pmc/articles/PMC7021762/ /pubmed/32060296 http://dx.doi.org/10.1038/s41598-020-59036-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Bersanelli, Matteo
Mosca, Ettore
Milanesi, Luciano
Bazzani, Armando
Castellani, Gastone
Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach
title Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach
title_full Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach
title_fullStr Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach
title_full_unstemmed Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach
title_short Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach
title_sort frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7021762/
https://www.ncbi.nlm.nih.gov/pubmed/32060296
http://dx.doi.org/10.1038/s41598-020-59036-w
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