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MODEL1208060000 - Hoppe2012 - Predicting changes in metabolic function using transcript profiles (HepatoNet1b_mouse)


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Reference Publication
Publication ID: 10.4230/OASIcs.GCB.2...
Andreas Hoppe, Hermann-Georg Holzhütter
ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles
German Conference on Bioinformatics 2012. OpenAccess Series in Informatics (OASIcs). 2012; 26:1-11
Charité University Medicine Berlin, Institute for Biochemistry, Computational Systems Biochemistry Group, Germany  [more]
Original Model: MODEL1208060000.origin
Submitter: Andreas Hoppe
Submission Date: 06 Aug 2012 14:51:45 UTC
Last Modification Date: 17 Mar 2014 14:19:29 UTC
Creation Date: 17 Mar 2014 14:19:29 UTC
Non kinetic model: network icon.
bqbiol:occursIn Brenda Tissue Ontology hepatocyte
bqbiol:hasTaxon Taxonomy Mus musculus
bqbiol:isVersionOf Gene Ontology regulation of growth
bqmodel:isDerivedFrom PubMed 20823849
Hoppe2012 - Predicting changes in metabolic function using transcript profiles

Measuring metabolite concentrations, reaction fluxes, and enzyme activities on large scale are tricky tasks in the study of cellular metabolism. Here, a method that predicts activity changes of metabolic functions based on relative transcript profiles, has been presented. It provides a ranked list of most regulated functions. The method has been applied to TGF-beta treatment of hepatocyte cultures. This stoichiometric model of the mouse hepatocyte is based on a corrected and extended version of HepatoNet1.

This model is described in the article:

Andreas Hoppe and Hermann-Georg Holzhütter
German Conference on Bioinformatics 2012; Publ.13.09.2012


Genome-wide transcript profiles are often the only available quantitative data for a particular perturbation of a cellular system and their interpretation with respect to the metabolism is a major challenge in systems biology, especially beyond on/off distinction of genes. We present a method that predicts activity changes of metabolic functions by scoring reference flux distributions based on relative transcript profiles, providing a ranked list of most regulated functions. Then, for each metabolic function, the involved genes are ranked upon how much they represent a specific regulation pattern. Compared with the naïve pathway-based approach, the reference modes can be chosen freely, and they represent full metabolic functions, thus, directly provide testable hypotheses for the metabolic study. In conclusion, the novel method provides promising functions for subsequent experimental elucidation together with outstanding associated genes, solely based on transcript profiles.

This model is hosted on BioModels Database and identified by: MODEL1208060000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 .

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