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Multi-tissue modeling analyzes pathophysiology of Type 2 Diabetes in MKR mice

Friday, November 08, 2013 — Poster Session IV

2:00 p.m. – 4:00 p.m.

FAES Academic Center (Upper-Level Terrace)




  • A Kumar
  • T Harrelson
  • E Gallagher
  • D LeRoith
  • J Shiloach
  • MJ Betenbaugh


Computational models for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM) can provide better understanding of disease pathophysiology and avoid high experimentation costs. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat tissues in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were determined and applied to the multi-tissue model to test the effect. Microarray data showed low gene expression in MKR mice versus healthy mice in branched-chain amino acids degradation and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acids and fatty-acid oxidation were statistically significantly downregulated in MKR mice versus healthy mice. This model probably represents the first robust multi-tissue in silico model for T2DM and can be used to improve understanding of T2DM further, and can be applied to study other metabolic disorders.

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