Comparison of Michaelis-Menten kinetics modeling alternatives in cancer chemotherapy modeling

Daniel Andras Drexler, Tamas Ferenci, Anna Lovrics, Levente Kovacs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Model-based optimization and personalization of tumor therapies require tumor growth models that reliably describe the effect of the drug used during the therapy. A key phenomenon in the drug effect mechanism is the pharmacodynamics of the drugs which limits the maximal effect of the drug. The pharmacodynamics can be modeled with Michaelis-Menten kinetics, that can be realized in the differential equations of the model as a Hill function or bilinear functions with one extra state variable if we consider the quasi steady-state approximation or use triplet motifs, respectively. We use experimental data for a chemotherapeutic drug and carry out parametric identification of our tumor model with both Michealis-Menten kinetics models. The results show that the quasi steady-state approximation has better modeling power and less complexity.

Original languageEnglish
Title of host publicationSACI 2019 - IEEE 13th International Symposium on Applied Computational Intelligence and Informatics, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages27-32
Number of pages6
ISBN (Electronic)9781728106854
DOIs
Publication statusPublished - May 2019
Event13th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2019 - Timisoara, Romania
Duration: May 29 2019May 31 2019

Publication series

NameSACI 2019 - IEEE 13th International Symposium on Applied Computational Intelligence and Informatics, Proceedings

Conference

Conference13th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2019
CountryRomania
CityTimisoara
Period5/29/195/31/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Health Informatics

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  • Cite this

    Drexler, D. A., Ferenci, T., Lovrics, A., & Kovacs, L. (2019). Comparison of Michaelis-Menten kinetics modeling alternatives in cancer chemotherapy modeling. In SACI 2019 - IEEE 13th International Symposium on Applied Computational Intelligence and Informatics, Proceedings (pp. 27-32). [9111543] (SACI 2019 - IEEE 13th International Symposium on Applied Computational Intelligence and Informatics, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SACI46893.2019.9111543