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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...

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Detalles Bibliográficos
Autores principales: Niu, Puhua, Soto, Maria J., Yoon, Byung-Jun, Dougherty, Edward R., Alexander, Francis J., Blaby, Ian, Qian, Xiaoning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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).
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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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