Characterizing Noise in a Mathematical Model of the Adipogenic Transcriptional Network

By Alexandra Jilkine

Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN

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Abstract

Adipogenesis is the process by which precursor cells develop into mature adipocytes, or fat-storing cells. From a 2012 study by Park et al., we expanded a deterministic model of the transcriptional network of adipogenesis to include a module for adiponectin (AdipoQ) production, an insulin-sensitizing hormone secreted by adipocytes. We analyzed two possible implementations for the adiponectin module to determine if variability within the system parameters alone is sufficient to explain the adipocyte heterogeneity observed in a study by Loo et al. For each model, we first characterized overall susceptibility to noise by calculating the relative local sensitivity of AdipoQ and fat to various parameters. We then simulated the experiment done by Loo et al. with 30% added noise to determine if our system could replicate their data. Our results show that only the model where fat increases the degradation of adiponectin fits the trends observed in the Loo study, indicating that it is more likely than the model where fat decreases the synthesis of adiponectin.

Bio

Alexandra Jilkine Dr. Jilkine obtained her Ph.D. from the University of British Columbia, then did her postdoctoral work at UT Southwestern Medical Center in Dallas and University of Arizona in Tucson before joining Department of Applied and Computational Mathematics and Statistics at Notre Dame.

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Researchers should cite this work as follows:

  • Alexandra Jilkine (2016), "Characterizing Noise in a Mathematical Model of the Adipogenic Transcriptional Network," http://nanohub.org/resources/25395.

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Location

MJIS 1001, Purdue University, West Lafayette, IN

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