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Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism

We consider two approaches to modelling the cell metabolism of 6-mercaptopurine, one of the important chemotherapy drugs used for treating acute lymphocytic leukaemia: kinetic ordinary differential equations, and Boolean networks supplied with one controlling node, which takes continual values. We a...

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Autores principales: Lavrova, Anastasia I., Postnikov, Eugene B., Zyubin, Andrey Yu., Babak, Svetlana V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414245/
https://www.ncbi.nlm.nih.gov/pubmed/28484608
http://dx.doi.org/10.1098/rsos.160872
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author Lavrova, Anastasia I.
Postnikov, Eugene B.
Zyubin, Andrey Yu.
Babak, Svetlana V.
author_facet Lavrova, Anastasia I.
Postnikov, Eugene B.
Zyubin, Andrey Yu.
Babak, Svetlana V.
author_sort Lavrova, Anastasia I.
collection PubMed
description We consider two approaches to modelling the cell metabolism of 6-mercaptopurine, one of the important chemotherapy drugs used for treating acute lymphocytic leukaemia: kinetic ordinary differential equations, and Boolean networks supplied with one controlling node, which takes continual values. We analyse their interplay with respect to taking into account ATP concentration as a key parameter of switching between different pathways. It is shown that the Boolean networks, which allow avoiding the complexity of general kinetic modelling, preserve the possibility of reproducing the principal switching mechanism.
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spelling pubmed-54142452017-05-08 Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism Lavrova, Anastasia I. Postnikov, Eugene B. Zyubin, Andrey Yu. Babak, Svetlana V. R Soc Open Sci Biochemistry and Biophysics We consider two approaches to modelling the cell metabolism of 6-mercaptopurine, one of the important chemotherapy drugs used for treating acute lymphocytic leukaemia: kinetic ordinary differential equations, and Boolean networks supplied with one controlling node, which takes continual values. We analyse their interplay with respect to taking into account ATP concentration as a key parameter of switching between different pathways. It is shown that the Boolean networks, which allow avoiding the complexity of general kinetic modelling, preserve the possibility of reproducing the principal switching mechanism. The Royal Society Publishing 2017-04-12 /pmc/articles/PMC5414245/ /pubmed/28484608 http://dx.doi.org/10.1098/rsos.160872 Text en © 2017 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Biochemistry and Biophysics
Lavrova, Anastasia I.
Postnikov, Eugene B.
Zyubin, Andrey Yu.
Babak, Svetlana V.
Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism
title Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism
title_full Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism
title_fullStr Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism
title_full_unstemmed Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism
title_short Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism
title_sort ordinary differential equations and boolean networks in application to modelling of 6-mercaptopurine metabolism
topic Biochemistry and Biophysics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414245/
https://www.ncbi.nlm.nih.gov/pubmed/28484608
http://dx.doi.org/10.1098/rsos.160872
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