CINRG: Systems Biology of Glucocorticoids in Muscle Disease

Abstract

We developed two computational network reconstruction methods, linear state space model and dynamic Bayesian network to infer transcriptional networks using the rat acute transcriptional time series of bolus administration of glucocorticoids. And this time series was re-profiled using Illumina gene expression BeadChip for a much broader coverage. We have tested multiple repeated injection models for inducing asynchronous regeneration in normal mouse muscles. Based on the analysis of injured muscles, we found that the reinjury model with 10 days in between two injuries lead to pathological damages that most closely resemble the muscle pathology observed in untreated mdx muscles and human DMD muscles. We are using the 10 day reinjury model to induce asynchronous regeneration in normal mouse muscles, and are using glucocorticoids to treat the injured muscles. Based on part of the work accomplished in this grant, we have been awarded several new grants focusing on studying glucocorticoid mechanism in treating DMD and asthma.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2012
Accession Number
ADA575824

Entities

People

  • Zuyi Wang

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Cells
  • Circadian Rhythms
  • Computational Biology
  • Connective Tissue
  • Cytoskeleton
  • Data Sets
  • Diseases And Disorders
  • Epithelial Cells
  • Gene Expression
  • Markov Models
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Systems Biology
  • Tissues

Fields of Study

  • Biology

Readers

  • Cardiovascular Physiology
  • Molecular and genetic basis of cancer.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • Space