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Title: Boolean network modeling of β-cell apoptosis and insulin resistance in type 2 diabetes mellitus
Authors: Dutta, Pritha
Ma, Lichun
Ali, Yusuf
Sloot, Peter M.A.
Zheng, Jie
Keywords: Boolean Model
Type 2 Diabetes Mellitus
Issue Date: 2019
Source: Dutta, P., Ma, L., Ali, Y., Sloot, P. M. A., & Zheng, J. (2019). Boolean network modeling of β-cell apoptosis and insulin resistance in type 2 diabetes mellitus. BMC Systems Biology, 13(S2), 36-. doi:10.1186/s12918-019-0692-0
Series/Report no.: BMC Systems Biology
Abstract: Background : Major alteration in lifestyle of human population has promoted Type 2 diabetes mellitus (T2DM) to the level of an epidemic. This metabolic disorder is characterized by insulin resistance and pancreaticβ-cell dysfunction and apoptosis, triggered by endoplasmic reticulum (ER) stress, oxidative stress and cytokines. Computational modeling is necessary to consolidate information from various sources in order to obtain a comprehensive understanding of the pathogenesis of T2DM and to investigate possible interventions by performing in silico simulations. Results : In this paper, we propose a Boolean network model integrating the insulin resistance pathway with pancreatic β-cell apoptosis pathway which are responsible for T2DM. The model has five input signals, i.e. ER stress, oxidative stress, tumor necrosis factor α (TNFα), Fas ligand (FasL), and interleukin-6 (IL-6). We performed dynamical simulations using random order asynchronous update and with different combinations of the input signals. From the results, we observed that the proposed model made predictions that closely resemble the expression levels of genes in T2DM as reported in the literature. Conclusion : The proposed model can make predictions about expression levels of genes in T2DM that are in concordance with literature. Although experimental validation of the model is beyond the scope of this study, the model can be useful for understanding the aetiology of T2DM and discovery of therapeutic intervention for this prevalent complex disease. The files of our model and results are available at
DOI: 10.1186/s12918-019-0692-0
Rights: © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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