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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
Descripción
Sumario: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).