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Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET
High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. The...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4461287/ https://www.ncbi.nlm.nih.gov/pubmed/26058016 http://dx.doi.org/10.1371/journal.pone.0127216 |
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author | Rodriguez, Ana Crespo, Isaac Androsova, Ganna del Sol, Antonio |
author_facet | Rodriguez, Ana Crespo, Isaac Androsova, Ganna del Sol, Antonio |
author_sort | Rodriguez, Ana |
collection | PubMed |
description | High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states. |
format | Online Article Text |
id | pubmed-4461287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44612872015-06-16 Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET Rodriguez, Ana Crespo, Isaac Androsova, Ganna del Sol, Antonio PLoS One Research Article High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states. Public Library of Science 2015-06-09 /pmc/articles/PMC4461287/ /pubmed/26058016 http://dx.doi.org/10.1371/journal.pone.0127216 Text en © 2015 Rodriguez 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 Rodriguez, Ana Crespo, Isaac Androsova, Ganna del Sol, Antonio Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET |
title | Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET |
title_full | Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET |
title_fullStr | Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET |
title_full_unstemmed | Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET |
title_short | Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET |
title_sort | discrete logic modelling optimization to contextualize prior knowledge networks using prunet |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4461287/ https://www.ncbi.nlm.nih.gov/pubmed/26058016 http://dx.doi.org/10.1371/journal.pone.0127216 |
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