Analysis of two mechanisms of telomere maintenance based on the theory of g-Networks and stochastic automata networks
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RESEARCH
Open Access
Analysis of two mechanisms of telomere maintenance based on the theory of g-Networks and stochastic automata networks Kyung Hyun Lee1† and Marek Kimmel1,2*† From The Sixth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2019) Niagara Falls, NY, USA. 07 September 2019
Abstract Background Telomeres, which are composed of repetitive nucleotide sequences at the end of chromosomes, behave as a division clock that measures replicative senescence. Under the normal physiological condition, telomeres shorten with each cell division, and cells use the telomere lengths to sense the number of divisions. Replicative senescence has been shown to occur at approximately 50–70 cell divisions, which is termed the Hayflick’s limit. However, in cancer cells telomere lengths are stabilized, thereby allowing continual cell replication by two known mechanisms: activation of telomerase and Alternative Lengthening of Telomeres (ALT). The connections between the two mechanisms are complicated and still poorly understood. Results In this research, we propose that two different approaches, G-Networks and Stochastic Automata Networks, which are stochastic models motivated by queueing theory, are useful to identify a set of genes that play an important role in the state of interest and to infer their previously unknown correlation by obtaining both stationary and joint transient distributions of the given system. Our analysis using G-Network detects five statistically significant genes (CEBPA, FOXM1, E2F1, c-MYC, hTERT) with either mechanism, contrasted to normal cells. A new algorithm is introduced to show how the correlation between two genes of interest varies in the transient state according not only to each mechanism but also to each cell condition. Conclusions This study expands our existing knowledge of genes associated with mechanisms of telomere maintenance and provides a platform to understand similarities and differences between telomerase and ALT in terms of the correlation between two genes in the system. This is particularly important because telomere dynamics plays a major role in many physiological and disease processes, including hematopoiesis. Keywords: Queueing network theory, G-networks, Stochastic automata networks, Correlation analysis, Gene regulatory networks, Telomeres
*Correspondence: [email protected] Kyung Hyun Lee is the first author. † Kyung Hyun Lee and Marek Kimmel contributed equally to this work. 1 Department of Statistics, Rice University, 6100 Main Street, Houston 77057, TX, USA 2 Department of Systems Biology and Engineering Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland © The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and
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