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Complexity-reduction by first-order approximation of non-linear kinetics

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  • Becka, Michael

Abstract

Ecological, toxicological, and pharmacological research is often concerned with the answer to the question of how a substance is processed within a biological system. The exact knowledge of the corresponding kinetic pattern forms the basis for a useful answer. In order to identify non-linear kinetics, a first-order approximation method is proposed for complexity-reduction. A simulation study is presented to investigate the error of the approximation in case of a simple Michaelis-Menten kinetic process. The proposed method shows to give useful results which allow to characterize the underlying kinetic pattern. Furthermore it could be shown that in simulating kinetic processes the applied numerical methods may perform with considerable numerical instabilities.

Suggested Citation

  • Becka, Michael, 1998. "Complexity-reduction by first-order approximation of non-linear kinetics," Technical Reports 1998,04, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:199804
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    File URL: https://www.econstor.eu/bitstream/10419/77200/2/1998-04.pdf
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    Cited by:

    1. Schirm, Florian A. & Selinski, Silvia, 2000. "Interindividual and interoccasion variability of toxicokinetic parameters of uptake, exhalation, and metabolism of ethylene," Technical Reports 2000,07, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Selinski, Silvia & Urfer, Wolfgang, 1998. "Interindividual and interoccasion variability of toxicokinetic parameters in population models," Technical Reports 1998,38, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Selinski, Silvia, 2000. "Estimation of toxicokinetic population parameters in a four-stage hierarchical model," Technical Reports 2000,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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