A model of tumor growth based on cell cycle kinetics

Steven Piantadosi, Jane B. Hazelrig, Malcolm E. Turner

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This work describes a mathematical model of growth based on the kinetics of the cell cycle. A traditional model of the cell cycle has been used, with the addition of a resting (G0) state from which cells could reenter the reproductive cycle. The model assumes that a growth regulatory substance regulates the transition of cells to and from the resting state. Other transitions between the phases of the cycle were modeled as a first order process. Cell loss is an important feature of growth kinetics, and has been represented by a general but tractable mathematical form. The resulting model forms a system of ordinary nonlinear differential equations. Analytic methods are employed first in the study of this system. Simplifying assumptions regarding cell loss give rise to special cases for which equilibrium solutions can be found. One special case, which assumes first order loss from all cell cycle phases at equal rates, is presented here. For small time values, approximations corresponding to exponential growth were developed. The equations describing an intrinsic growth rate were derived. Simulation methods were used to further characterize the behavior of this model. Parameter values were chosen based on animal tumor cell cycle kinetic data, resulting in a set of 45 model simulations. Several tumor treatment protocols were simulated which illustrated the importance of the intrinsic growth rate and cell loss concepts. Although the qualitative behavior regarding absolute and relative growth is reasonable, this model awaits data for model fitting, parameter estimation, or revision of the equations.

    Original languageEnglish (US)
    Pages (from-to)283-306
    Number of pages24
    JournalMathematical Biosciences
    Volume66
    Issue number2
    DOIs
    StatePublished - Oct 1983

    ASJC Scopus subject areas

    • Statistics and Probability
    • Modeling and Simulation
    • Biochemistry, Genetics and Molecular Biology(all)
    • Immunology and Microbiology(all)
    • Agricultural and Biological Sciences(all)
    • Applied Mathematics

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