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Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems
A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist ther...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3524183/ https://www.ncbi.nlm.nih.gov/pubmed/23284654 http://dx.doi.org/10.1371/journal.pone.0051006 |
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author | Saeed, Mehreen Ijaz, Maliha Javed, Kashif Babri, Haroon Atique |
author_facet | Saeed, Mehreen Ijaz, Maliha Javed, Kashif Babri, Haroon Atique |
author_sort | Saeed, Mehreen |
collection | PubMed |
description | A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results. |
format | Online Article Text |
id | pubmed-3524183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35241832013-01-02 Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems Saeed, Mehreen Ijaz, Maliha Javed, Kashif Babri, Haroon Atique PLoS One Research Article A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results. Public Library of Science 2012-12-17 /pmc/articles/PMC3524183/ /pubmed/23284654 http://dx.doi.org/10.1371/journal.pone.0051006 Text en © 2012 Saeed et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Saeed, Mehreen Ijaz, Maliha Javed, Kashif Babri, Haroon Atique Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems |
title | Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems |
title_full | Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems |
title_fullStr | Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems |
title_full_unstemmed | Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems |
title_short | Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems |
title_sort | reverse engineering boolean networks: from bernoulli mixture models to rule based systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3524183/ https://www.ncbi.nlm.nih.gov/pubmed/23284654 http://dx.doi.org/10.1371/journal.pone.0051006 |
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