Literature DB >> 35243375

Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline.

Puhua Niu1, Maria J Soto2, Byung-Jun Yoon1,3, Edward R Dougherty1, Francis J Alexander3, Ian Blaby2,4, Xiaoning Qian1,3.   

Abstract

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).
© 2022 The Author(s).

Entities:  

Keywords:  Bioinformatics; Cell Biology; Gene Expression; Metabolism; Systems biology

Mesh:

Substances:

Year:  2022        PMID: 35243375      PMCID: PMC8866898          DOI: 10.1016/j.xpro.2022.101184

Source DB:  PubMed          Journal:  STAR Protoc        ISSN: 2666-1667


  16 in total

1.  Constraints-based models: regulation of gene expression reduces the steady-state solution space.

Authors:  Markus W Covert; Bernhard O Palsson
Journal:  J Theor Biol       Date:  2003-04-07       Impact factor: 2.691

2.  Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis.

Authors:  Sriram Chandrasekaran; Nathan D Price
Journal:  Proc Natl Acad Sci U S A       Date:  2010-09-27       Impact factor: 11.205

3.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli.

Authors:  Markus W Covert; Nan Xiao; Tiffany J Chen; Jonathan R Karr
Journal:  Bioinformatics       Date:  2008-07-10       Impact factor: 6.937

4.  Inferring regulatory networks from expression data using tree-based methods.

Authors:  Vân Anh Huynh-Thu; Alexandre Irrthum; Louis Wehenkel; Pierre Geurts
Journal:  PLoS One       Date:  2010-09-28       Impact factor: 3.240

5.  Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Authors:  Laurent Heirendt; Sylvain Arreckx; Thomas Pfau; Sebastián N Mendoza; Anne Richelle; Almut Heinken; Hulda S Haraldsdóttir; Jacek Wachowiak; Sarah M Keating; Vanja Vlasov; Stefania Magnusdóttir; Chiam Yu Ng; German Preciat; Alise Žagare; Siu H J Chan; Maike K Aurich; Catherine M Clancy; Jennifer Modamio; John T Sauls; Alberto Noronha; Aarash Bordbar; Benjamin Cousins; Diana C El Assal; Luis V Valcarcel; Iñigo Apaolaza; Susan Ghaderi; Masoud Ahookhosh; Marouen Ben Guebila; Andrejs Kostromins; Nicolas Sompairac; Hoai M Le; Ding Ma; Yuekai Sun; Lin Wang; James T Yurkovich; Miguel A P Oliveira; Phan T Vuong; Lemmer P El Assal; Inna Kuperstein; Andrei Zinovyev; H Scott Hinton; William A Bryant; Francisco J Aragón Artacho; Francisco J Planes; Egils Stalidzans; Alejandro Maass; Santosh Vempala; Michael Hucka; Michael A Saunders; Costas D Maranas; Nathan E Lewis; Thomas Sauter; Bernhard Ø Palsson; Ines Thiele; Ronan M T Fleming
Journal:  Nat Protoc       Date:  2019-03       Impact factor: 13.491

6.  The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo.

Authors:  Richard Bonneau; David J Reiss; Paul Shannon; Marc Facciotti; Leroy Hood; Nitin S Baliga; Vesteinn Thorsson
Journal:  Genome Biol       Date:  2006-05-10       Impact factor: 13.583

7.  A genome-scale computational study of the interplay between transcriptional regulation and metabolism.

Authors:  Tomer Shlomi; Yariv Eisenberg; Roded Sharan; Eytan Ruppin
Journal:  Mol Syst Biol       Date:  2007-04-17       Impact factor: 11.429

8.  Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

Authors:  Zhuo Wang; Samuel A Danziger; Benjamin D Heavner; Shuyi Ma; Jennifer J Smith; Song Li; Thurston Herricks; Evangelos Simeonidis; Nitin S Baliga; John D Aitchison; Nathan D Price
Journal:  PLoS Comput Biol       Date:  2017-05-17       Impact factor: 4.475

9.  TIGRESS: Trustful Inference of Gene REgulation using Stability Selection.

Authors:  Anne-Claire Haury; Fantine Mordelet; Paola Vera-Licona; Jean-Philippe Vert
Journal:  BMC Syst Biol       Date:  2012-11-22
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