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A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging

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Parameter adaptations during phenotype transitions in progressive diseases.

Christian A Tiemann¹, Joep Vanlier, Peter A J Hilbers

¹Department of BioMedical Engineering, Eindhoven University of Technology, Den Dolech 2, Eindhoven, 5612 AZ, The Netherlands. c.a.tiemann@tue.nl

BMC Systems Biology

|October 28, 2011

Summary

This study introduces a computational method to track how biological systems change phenotypes, identifying key molecular adaptations. The approach helps understand diseases like fatty liver by predicting metabolic shifts.

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Area of Science:

Systems Biology
Computational Biology
Metabolic Engineering

Background:

Phenotype transitions are crucial for understanding progressive diseases such as diabetes mellitus, metabolic syndrome, and cardiovascular diseases.
Explaining phenotype transitions via molecular adaptations in biological systems remains a significant challenge.

Purpose of the Study:

To develop and apply a generic computational approach for analyzing biological systems undergoing phenotype transitions.
To identify molecular processes and parameter adaptations responsible for phenotypical changes over time.

Main Methods:

Utilizing mathematical modeling to integrate experimental data on metabolic pools and fluxes.
Identifying trajectories of parameter adaptations that drive phenotypical changes.
Applying the approach to study liver X receptor (LXR)-induced hepatic steatosis.

Main Results:

Model analysis predicted molecular adaptations in response to LXR activation.
Observed increased hepatic triglyceride fluxes and cytosolic storage over endoplasmic reticulum storage.
Identified potential scenarios for cholesterol metabolism adaptations, with one flux quantification sufficient to refine hypotheses.

Conclusions:

A novel computational approach is proposed for analyzing evolving biological systems and predicting molecular drivers of phenotype transitions.
The method provided insights into triglyceride and cholesterol redistribution in LXR-induced hepatic steatosis, not evident from raw data.
Model analysis guides future research by highlighting specific molecular processes for detailed investigation.