Construction of a Waddington-like landscape model that can guide clinical exploration of p53-dynamics-activating parameters in the face of divergent p53 dynamics

dc.contributor.author Gokhan Demirkiran
dc.contributor.author Demirkıran, Gökhan
dc.date MAY
dc.date.accessioned 2025-10-06T16:19:35Z
dc.date.issued 2024
dc.description.abstract The primary strategy of radiotherapies is to manipulate cell fate decision a process mainly regulated by a spectrum of p53 dynamics. Based on their biological relevance we analytically categorize the range of p53 levels into seven distinct level -forms leading to the identification of eleven non -chaotic phenomena of p53 level -form dynamics. The superimposing of cell fate attractors on the co -dimension two bifurcation diagram of eleven p53 level -form dynamics under quasi -steady state assumption provides a mechanistic tool that can be posted as a Waddingtonlike landscape model for cell fate regulation by p53 dynamics. In the proposed model the (location of) cell is represented as a control point in the bifurcation diagram representing a flattened landscape composed of 11 distinct behavioral regions and the effort that moves the cell on the landscape is exerted by accumulating death factors upon DNA damage. Further analysis reveals that intrinsically -resistant cancer attractors inevitably exist on the landscape and cells might have evolved to use a safe operational area whose cusp bifurcation shape is contributing to robustness via hysteresis. We further reveal specific mechanisms through which tumors acquire resistance under therapy. The proposed landscape model can be put to productive use via a reverse control methodology. The reconstruction of cancer -specific landscape can inform the design of personalized p53 -dynamics -based drug combination strategies suffering from the combinatorial explosion of target parameters and the divergent p53 dynamics. We conclude that the reverse control of the proposed landscape model has the potential to bridge the gap between theoretical and clinical studies of p53 dynamics for p53 -dynamics -based cancer therapies.
dc.identifier.doi 10.1016/j.cnsns.2024.107893
dc.identifier.issn 1007-5704
dc.identifier.issn 1878-7274
dc.identifier.scopus 2-s2.0-85185843859
dc.identifier.uri http://dx.doi.org/10.1016/j.cnsns.2024.107893
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/5906
dc.identifier.uri https://doi.org/10.1016/j.cnsns.2024.107893
dc.language.iso English
dc.publisher ELSEVIER
dc.relation.ispartof Communications in Nonlinear Science and Numerical Simulation
dc.rights info:eu-repo/semantics/closedAccess
dc.source COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
dc.subject Waddington 's landscape metaphor, P53 dynamics, Cusp bifurcation, Cancer attractor
dc.subject EPIGENETIC LANDSCAPE, CANCER-CELLS, THERAPY, CHAOS, ATM
dc.subject P53 Dynamics
dc.subject Cancer Attractor
dc.subject Cusp Bifurcation
dc.subject Waddington’s Landscape Metaphor
dc.subject Waddington ’s Landscape Metaphor
dc.title Construction of a Waddington-like landscape model that can guide clinical exploration of p53-dynamics-activating parameters in the face of divergent p53 dynamics
dc.type Article
dspace.entity.type Publication
gdc.author.institutional Demirkıran, Gökhan (57200319546)
gdc.author.scopusid 57200319546
gdc.author.wosid Demirkiran, Gokhan/JAC-5273-2023
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gdc.description.department
gdc.description.departmenttemp [Demirkiran, Gokhan] Yasar Univ, Dept Elect Elect Engn, TR-35100 Bornova, Izmir, Turkiye
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
gdc.description.startpage 107893
gdc.description.volume 132
gdc.description.woscitationindex Science Citation Index Expanded
gdc.identifier.openalex W4391722667
gdc.identifier.wos WOS:001202555500001
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gdc.oaire.keywords cancer attractor
gdc.oaire.keywords Biochemistry, molecular biology
gdc.oaire.keywords Medical applications (general)
gdc.oaire.keywords Waddington's landscape metaphor
gdc.oaire.keywords p53 dynamics
gdc.oaire.keywords cusp bifurcation
gdc.oaire.popularity 2.9541973E-9
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gdc.virtual.author Demirkiran, Gökhan
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