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...

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Autores principales:	Deepika Vatsa, Sumeet Agarwal
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d704d6a144d74dddba90bf834e8a6246
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spelling	oai:doaj.org-article:d704d6a144d74dddba90bf834e8a62462021-11-25T06:19:15ZPEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.1932-620310.1371/journal.pone.0251666https://doaj.org/article/d704d6a144d74dddba90bf834e8a62462021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251666https://doaj.org/toc/1932-6203The 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.Deepika VatsaSumeet AgarwalPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251666 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Deepika Vatsa Sumeet Agarwal PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.
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	article
author	Deepika Vatsa Sumeet Agarwal
author_facet	Deepika Vatsa Sumeet Agarwal
author_sort	Deepika Vatsa
title	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_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_sort	pepn-grn: a petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d704d6a144d74dddba90bf834e8a6246
work_keys_str_mv	AT deepikavatsa pepngrnapetrinetbasedapproachfortheinferenceofgeneregulatorynetworksfromnoisygeneexpressiondata AT sumeetagarwal pepngrnapetrinetbasedapproachfortheinferenceofgeneregulatorynetworksfromnoisygeneexpressiondata
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PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.

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