Cargando…
Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks
Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene ex...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565562/ https://www.ncbi.nlm.nih.gov/pubmed/26356485 http://dx.doi.org/10.1186/1752-0509-9-S5-S2 |
_version_ | 1782389589689761792 |
---|---|
author | Mizeranschi, Alexandru Zheng, Huiru Thompson, Paul Dubitzky, Werner |
author_facet | Mizeranschi, Alexandru Zheng, Huiru Thompson, Paul Dubitzky, Werner |
author_sort | Mizeranschi, Alexandru |
collection | PubMed |
description | Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverse-engineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverse-engineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverse-engineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverse-engineering studies on data generated from artificial and experimentally determined from real GRN systems. |
format | Online Article Text |
id | pubmed-4565562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45655622015-09-18 Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks Mizeranschi, Alexandru Zheng, Huiru Thompson, Paul Dubitzky, Werner BMC Syst Biol Research Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverse-engineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverse-engineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverse-engineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverse-engineering studies on data generated from artificial and experimentally determined from real GRN systems. BioMed Central 2015-09-01 /pmc/articles/PMC4565562/ /pubmed/26356485 http://dx.doi.org/10.1186/1752-0509-9-S5-S2 Text en Copyright © 2015 Mizeranschi 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 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Mizeranschi, Alexandru Zheng, Huiru Thompson, Paul Dubitzky, Werner Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks |
title | Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks |
title_full | Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks |
title_fullStr | Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks |
title_full_unstemmed | Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks |
title_short | Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks |
title_sort | evaluating a common semi-mechanistic mathematical model of gene-regulatory networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565562/ https://www.ncbi.nlm.nih.gov/pubmed/26356485 http://dx.doi.org/10.1186/1752-0509-9-S5-S2 |
work_keys_str_mv | AT mizeranschialexandru evaluatingacommonsemimechanisticmathematicalmodelofgeneregulatorynetworks AT zhenghuiru evaluatingacommonsemimechanisticmathematicalmodelofgeneregulatorynetworks AT thompsonpaul evaluatingacommonsemimechanisticmathematicalmodelofgeneregulatorynetworks AT dubitzkywerner evaluatingacommonsemimechanisticmathematicalmodelofgeneregulatorynetworks |