Open AccessTwo‐dimensional polynomial type canonical relaxation oscillator model for p53 dynamics
Author(s) -
Demirkıran Gökhan,
Kalaycı Demir Güleser,
Güzeliş Cüneyt
Publication year - 2018
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2017.0077
Subject(s) - interpretability , biological system , oscillation (cell signaling) , topology (electrical circuits) , relaxation (psychology) , cell cycle checkpoint , polynomial , dynamics (music) , gene regulatory network , cell cycle , computer science , computational biology , mutation , biology , gene , physics , genetics , microbiology and biotechnology , control theory (sociology) , mathematics , gene expression , neuroscience , combinatorics , artificial intelligence , mathematical analysis , control (management) , acoustics
p53 network, which is responsible for DNA damage response of cells, exhibits three distinct qualitative behaviours; low state, oscillation and high state, which are associated with normal cell cycle progression, cell cycle arrest and apoptosis, respectively. The experimental studies demonstrate that these dynamics of p53 are due to the ATM and Wip1 interaction. This paper proposes a simple two‐dimensional canonical relaxation oscillator model based on the identified topological structure of ATM and Wip1 interaction underlying these qualitative behaviours of p53 network. The model includes only polynomial terms that have the interpretability of known ATM and Wip1 interaction. The introduced model is useful for understanding relaxation oscillations in gene regulatory networks. Through mathematical analysis, we investigate the roles of ATM and Wip1 in forming of these three essential behaviours, and show that ATM and Wip1 constitute the core mechanism of p53 dynamics. In agreement with biological findings, we show that Wip1 degradation term is a highly sensitive parameter, possibly related to mutations. By perturbing the corresponding parameters, our model characterizes some mutations such as ATM deficiency and Wip1 overexpression. Finally, we provide intervention strategies considering our observation that Wip1 seems to be an important target to conduct therapies for these mutations.