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NETISCE: a network-based tool for cell fate reprogramming
The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogrammi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209484/ https://www.ncbi.nlm.nih.gov/pubmed/35725577 http://dx.doi.org/10.1038/s41540-022-00231-y |
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author | Marazzi, Lauren Shah, Milan Balakrishnan, Shreedula Patil, Ananya Vera-Licona, Paola |
author_facet | Marazzi, Lauren Shah, Milan Balakrishnan, Shreedula Patil, Ananya Vera-Licona, Paola |
author_sort | Marazzi, Lauren |
collection | PubMed |
description | The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task. |
format | Online Article Text |
id | pubmed-9209484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92094842022-06-22 NETISCE: a network-based tool for cell fate reprogramming Marazzi, Lauren Shah, Milan Balakrishnan, Shreedula Patil, Ananya Vera-Licona, Paola NPJ Syst Biol Appl Article The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task. Nature Publishing Group UK 2022-06-20 /pmc/articles/PMC9209484/ /pubmed/35725577 http://dx.doi.org/10.1038/s41540-022-00231-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Marazzi, Lauren Shah, Milan Balakrishnan, Shreedula Patil, Ananya Vera-Licona, Paola NETISCE: a network-based tool for cell fate reprogramming |
title | NETISCE: a network-based tool for cell fate reprogramming |
title_full | NETISCE: a network-based tool for cell fate reprogramming |
title_fullStr | NETISCE: a network-based tool for cell fate reprogramming |
title_full_unstemmed | NETISCE: a network-based tool for cell fate reprogramming |
title_short | NETISCE: a network-based tool for cell fate reprogramming |
title_sort | netisce: a network-based tool for cell fate reprogramming |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209484/ https://www.ncbi.nlm.nih.gov/pubmed/35725577 http://dx.doi.org/10.1038/s41540-022-00231-y |
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