PHENSIM: Phenotype Simulator.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation...

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Autores principales:	Salvatore Alaimo, Rosaria Valentina Rapicavoli, Gioacchino P Marceca, Alessandro La Ferlita, Oksana B Serebrennikova, Philip N Tsichlis, Bud Mishra, Alfredo Pulvirenti, Alfredo Ferro
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/afc4a2233c524ecf9bcb92d8fc065807
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id	oai:doaj.org-article:afc4a2233c524ecf9bcb92d8fc065807
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spelling	oai:doaj.org-article:afc4a2233c524ecf9bcb92d8fc0658072021-11-25T05:40:34ZPHENSIM: Phenotype Simulator.1553-734X1553-735810.1371/journal.pcbi.1009069https://doaj.org/article/afc4a2233c524ecf9bcb92d8fc0658072021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009069https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool's applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach's reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.Salvatore AlaimoRosaria Valentina RapicavoliGioacchino P MarcecaAlessandro La FerlitaOksana B SerebrennikovaPhilip N TsichlisBud MishraAlfredo PulvirentiAlfredo FerroPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 6, p e1009069 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Salvatore Alaimo Rosaria Valentina Rapicavoli Gioacchino P Marceca Alessandro La Ferlita Oksana B Serebrennikova Philip N Tsichlis Bud Mishra Alfredo Pulvirenti Alfredo Ferro PHENSIM: Phenotype Simulator.
description	Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool's applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach's reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.
format	article
author	Salvatore Alaimo Rosaria Valentina Rapicavoli Gioacchino P Marceca Alessandro La Ferlita Oksana B Serebrennikova Philip N Tsichlis Bud Mishra Alfredo Pulvirenti Alfredo Ferro
author_facet	Salvatore Alaimo Rosaria Valentina Rapicavoli Gioacchino P Marceca Alessandro La Ferlita Oksana B Serebrennikova Philip N Tsichlis Bud Mishra Alfredo Pulvirenti Alfredo Ferro
author_sort	Salvatore Alaimo
title	PHENSIM: Phenotype Simulator.
title_short	PHENSIM: Phenotype Simulator.
title_full	PHENSIM: Phenotype Simulator.
title_fullStr	PHENSIM: Phenotype Simulator.
title_full_unstemmed	PHENSIM: Phenotype Simulator.
title_sort	phensim: phenotype simulator.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/afc4a2233c524ecf9bcb92d8fc065807
work_keys_str_mv	AT salvatorealaimo phensimphenotypesimulator AT rosariavalentinarapicavoli phensimphenotypesimulator AT gioacchinopmarceca phensimphenotypesimulator AT alessandrolaferlita phensimphenotypesimulator AT oksanabserebrennikova phensimphenotypesimulator AT philipntsichlis phensimphenotypesimulator AT budmishra phensimphenotypesimulator AT alfredopulvirenti phensimphenotypesimulator AT alfredoferro phensimphenotypesimulator
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PHENSIM: Phenotype Simulator.

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