Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty
Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene’s state can have multiple values due to biological proper...
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| Format: | Online |
| Language: | English |
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Public Library of Science
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514755/ |
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pubmed-45147552015-07-29 Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty Grieb, Melanie Burkovski, Andre Sträng, J. Eric Kraus, Johann M. Groß, Alexander Palm, Günther Kühl, Michael Kestler, Hans A. Research Article Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene’s state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes. Public Library of Science 2015-07-24 /pmc/articles/PMC4514755/ /pubmed/26207376 http://dx.doi.org/10.1371/journal.pone.0131832 Text en © 2015 Grieb 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. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Grieb, Melanie Burkovski, Andre Sträng, J. Eric Kraus, Johann M. Groß, Alexander Palm, Günther Kühl, Michael Kestler, Hans A. |
| spellingShingle |
Grieb, Melanie Burkovski, Andre Sträng, J. Eric Kraus, Johann M. Groß, Alexander Palm, Günther Kühl, Michael Kestler, Hans A. Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty |
| author_facet |
Grieb, Melanie Burkovski, Andre Sträng, J. Eric Kraus, Johann M. Groß, Alexander Palm, Günther Kühl, Michael Kestler, Hans A. |
| author_sort |
Grieb, Melanie |
| title |
Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty |
| title_short |
Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty |
| title_full |
Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty |
| title_fullStr |
Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty |
| title_full_unstemmed |
Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty |
| title_sort |
predicting variabilities in cardiac gene expression with a boolean network incorporating uncertainty |
| description |
Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene’s state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes. |
| publisher |
Public Library of Science |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514755/ |
| _version_ |
1613252045174734848 |