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PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data
The inference of gene regulatory networks (GRNs) from expression data is a challenging problem in systems biology. The stochasticity or fluctuations in the biochemical processes that regulate the transcription process poses as one of the major challenges. In this paper, we propose a novel GRN infere...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121333/ https://www.ncbi.nlm.nih.gov/pubmed/33989333 http://dx.doi.org/10.1371/journal.pone.0251666 |
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author | Vatsa, Deepika Agarwal, Sumeet |
author_facet | Vatsa, Deepika Agarwal, Sumeet |
author_sort | Vatsa, Deepika |
collection | PubMed |
description | The inference of gene regulatory networks (GRNs) from expression data is a challenging problem in systems biology. The stochasticity or fluctuations in the biochemical processes that regulate the transcription process poses as one of the major challenges. In this paper, we propose a novel GRN inference approach, named the Probabilistic Extended Petri Net for Gene Regulatory Network (PEPN-GRN), for the inference of gene regulatory networks from noisy expression data. The proposed inference approach makes use of transition of discrete gene expression levels across adjacent time points as different evidence types that relate to the production or decay of genes. The paper examines three variants of the PEPN-GRN method, which mainly differ by the way the scores of network edges are computed using evidence types. The proposed method is evaluated on the benchmark DREAM4 in silico data sets and a real time series data set of E. coli from the DREAM5 challenge. The PEPN-GRN_v3 variant (the third variant of the PEPN-GRN approach) sought to learn the weights of evidence types in accordance with their contribution to the activation and inhibition gene regulation process. The learned weights help understand the time-shifted and inverted time-shifted relationship between regulator and target gene. Thus, PEPN-GRN_v3, along with the inference of network edges, also provides a functional understanding of the gene regulation process. |
format | Online Article Text |
id | pubmed-8121333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81213332021-05-24 PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data Vatsa, Deepika Agarwal, Sumeet PLoS One Research Article The inference of gene regulatory networks (GRNs) from expression data is a challenging problem in systems biology. The stochasticity or fluctuations in the biochemical processes that regulate the transcription process poses as one of the major challenges. In this paper, we propose a novel GRN inference approach, named the Probabilistic Extended Petri Net for Gene Regulatory Network (PEPN-GRN), for the inference of gene regulatory networks from noisy expression data. The proposed inference approach makes use of transition of discrete gene expression levels across adjacent time points as different evidence types that relate to the production or decay of genes. The paper examines three variants of the PEPN-GRN method, which mainly differ by the way the scores of network edges are computed using evidence types. The proposed method is evaluated on the benchmark DREAM4 in silico data sets and a real time series data set of E. coli from the DREAM5 challenge. The PEPN-GRN_v3 variant (the third variant of the PEPN-GRN approach) sought to learn the weights of evidence types in accordance with their contribution to the activation and inhibition gene regulation process. The learned weights help understand the time-shifted and inverted time-shifted relationship between regulator and target gene. Thus, PEPN-GRN_v3, along with the inference of network edges, also provides a functional understanding of the gene regulation process. Public Library of Science 2021-05-14 /pmc/articles/PMC8121333/ /pubmed/33989333 http://dx.doi.org/10.1371/journal.pone.0251666 Text en © 2021 Vatsa, Agarwal https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Vatsa, Deepika Agarwal, Sumeet PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data |
title | PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data |
title_full | PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data |
title_fullStr | PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data |
title_full_unstemmed | PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data |
title_short | PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data |
title_sort | pepn-grn: a petri net-based approach for the inference of gene regulatory networks from noisy gene expression data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121333/ https://www.ncbi.nlm.nih.gov/pubmed/33989333 http://dx.doi.org/10.1371/journal.pone.0251666 |
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