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

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:

ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles

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

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.

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