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Stochastic modelling of PTEN regulation in brain tumors: A model for glioblastoma multiforme

  • Received: 01 October 2014 Accepted: 29 June 2018 Published: 01 June 2015
  • MSC : Primary: 92D10, 65C99, 92-08, 92C45.

  • This work is the outcome of the partnership between the mathematical group of Department DISBEF and the biochemical group of Department DISB of the University of Urbino "Carlo Bo" in order to better understand some crucial aspects of brain cancer oncogenesis.Throughout our collaboration we discovered that biochemists are mainly attracted to the instantaneous behaviour of the whole cell, while mathematicians are mostly interested in the evolution along time of small and different parts of it. This collaboration has thus been very challenging. Starting from [23,24,25], we introduce a competitive stochastic model for post-transcriptional regulation of PTEN, including interactions with the miRNA and concurrent genes. Our model also covers protein formation and the backward mechanism going from the protein back to the miRNA.The numerical simulations show that the model reproduces the expected dynamics of normal glial cells. Moreover, the introduction of translational and transcriptional delays offers some interesting insights for the PTEN low expression as observed in brain tumor cells.

    Citation: Margherita Carletti, Matteo Montani, Valentina Meschini, Marzia Bianchi, Lucia Radici. Stochastic modelling of PTEN regulation in brain tumors: A model for glioblastoma multiforme[J]. Mathematical Biosciences and Engineering, 2015, 12(5): 965-981. doi: 10.3934/mbe.2015.12.965

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  • This work is the outcome of the partnership between the mathematical group of Department DISBEF and the biochemical group of Department DISB of the University of Urbino "Carlo Bo" in order to better understand some crucial aspects of brain cancer oncogenesis.Throughout our collaboration we discovered that biochemists are mainly attracted to the instantaneous behaviour of the whole cell, while mathematicians are mostly interested in the evolution along time of small and different parts of it. This collaboration has thus been very challenging. Starting from [23,24,25], we introduce a competitive stochastic model for post-transcriptional regulation of PTEN, including interactions with the miRNA and concurrent genes. Our model also covers protein formation and the backward mechanism going from the protein back to the miRNA.The numerical simulations show that the model reproduces the expected dynamics of normal glial cells. Moreover, the introduction of translational and transcriptional delays offers some interesting insights for the PTEN low expression as observed in brain tumor cells.


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    3. Gionmattia Carancini, Margherita Carletti, Giulia Spaletta, Modeling and Simulation of a miRNA Regulatory Network of the PTEN Gene, 2021, 9, 2227-7390, 1803, 10.3390/math9151803
    4. Margherita Carletti, Giulia Spaletta, Stochastic modelling and simulation of PTEN regulatory networks with miRNAs and ceRNAs, 2022, 68, 0430-3202, 645, 10.1007/s11565-022-00416-7
    5. Mohammed Salman, Pradeep Kumar Das, Sanjay Kumar Mohanty, A Systematic Review on Recent Advancements in Deep Learning and Mathematical Modeling for Efficient Detection of Glioblastoma, 2024, 73, 0018-9456, 1, 10.1109/TIM.2024.3476544
    6. Roberto Macrelli, Margherita Carletti, Vincenzo Fano, A Preliminary Comparison Between Simulations for an Altruistic Simple Model on the Backward Technique, 2024, 14, 2075-1680, 9, 10.3390/axioms14010009
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  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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