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Modeling stochasticity and variability in gene regulatory networks
Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419641/ https://www.ncbi.nlm.nih.gov/pubmed/22673395 http://dx.doi.org/10.1186/1687-4153-2012-5 |
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author | Murrugarra, David Veliz-Cuba, Alan Aguilar, Boris Arat, Seda Laubenbacher, Reinhard |
author_facet | Murrugarra, David Veliz-Cuba, Alan Aguilar, Boris Arat, Seda Laubenbacher, Reinhard |
author_sort | Murrugarra, David |
collection | PubMed |
description | Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex. |
format | Online Article Text |
id | pubmed-3419641 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34196412012-08-20 Modeling stochasticity and variability in gene regulatory networks Murrugarra, David Veliz-Cuba, Alan Aguilar, Boris Arat, Seda Laubenbacher, Reinhard EURASIP J Bioinform Syst Biol Research Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex. BioMed Central 2012 2012-06-06 /pmc/articles/PMC3419641/ /pubmed/22673395 http://dx.doi.org/10.1186/1687-4153-2012-5 Text en Copyright ©2012 Murrugarra et al; licensee Springer. 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 | Research Murrugarra, David Veliz-Cuba, Alan Aguilar, Boris Arat, Seda Laubenbacher, Reinhard Modeling stochasticity and variability in gene regulatory networks |
title | Modeling stochasticity and variability in gene regulatory networks |
title_full | Modeling stochasticity and variability in gene regulatory networks |
title_fullStr | Modeling stochasticity and variability in gene regulatory networks |
title_full_unstemmed | Modeling stochasticity and variability in gene regulatory networks |
title_short | Modeling stochasticity and variability in gene regulatory networks |
title_sort | modeling stochasticity and variability in gene regulatory networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419641/ https://www.ncbi.nlm.nih.gov/pubmed/22673395 http://dx.doi.org/10.1186/1687-4153-2012-5 |
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