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Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline
This protocol explains the pipeline for condition-dependent metabolite yield prediction using Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER targets metabolic engineering applications via a hybrid model integrating transcription factor (TF)-gene regulatory network (TR...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866898/ https://www.ncbi.nlm.nih.gov/pubmed/35243375 http://dx.doi.org/10.1016/j.xpro.2022.101184 |
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author | Niu, Puhua Soto, Maria J. Yoon, Byung-Jun Dougherty, Edward R. Alexander, Francis J. Blaby, Ian Qian, Xiaoning |
author_facet | Niu, Puhua Soto, Maria J. Yoon, Byung-Jun Dougherty, Edward R. Alexander, Francis J. Blaby, Ian Qian, Xiaoning |
author_sort | Niu, Puhua |
collection | PubMed |
description | This protocol explains the pipeline for condition-dependent metabolite yield prediction using Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER targets metabolic engineering applications via a hybrid model integrating transcription factor (TF)-gene regulatory network (TRN) with a Bayesian network (BN) inferred from transcriptomic expression data to effectively regulate metabolic reactions. For E. coli and yeast, TRIMER achieves reliable knockout phenotype and flux predictions from the deletion of one or more TFs at the genome scale. For complete details on the use and execution of this protocol, please refer to Niu et al. (2021). |
format | Online Article Text |
id | pubmed-8866898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-88668982022-03-02 Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline Niu, Puhua Soto, Maria J. Yoon, Byung-Jun Dougherty, Edward R. Alexander, Francis J. Blaby, Ian Qian, Xiaoning STAR Protoc Protocol This protocol explains the pipeline for condition-dependent metabolite yield prediction using Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER targets metabolic engineering applications via a hybrid model integrating transcription factor (TF)-gene regulatory network (TRN) with a Bayesian network (BN) inferred from transcriptomic expression data to effectively regulate metabolic reactions. For E. coli and yeast, TRIMER achieves reliable knockout phenotype and flux predictions from the deletion of one or more TFs at the genome scale. For complete details on the use and execution of this protocol, please refer to Niu et al. (2021). Elsevier 2022-02-21 /pmc/articles/PMC8866898/ /pubmed/35243375 http://dx.doi.org/10.1016/j.xpro.2022.101184 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Niu, Puhua Soto, Maria J. Yoon, Byung-Jun Dougherty, Edward R. Alexander, Francis J. Blaby, Ian Qian, Xiaoning Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline |
title | Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline |
title_full | Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline |
title_fullStr | Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline |
title_full_unstemmed | Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline |
title_short | Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline |
title_sort | protocol for condition-dependent metabolite yield prediction using the trimer pipeline |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866898/ https://www.ncbi.nlm.nih.gov/pubmed/35243375 http://dx.doi.org/10.1016/j.xpro.2022.101184 |
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