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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of mathematical biology 19 (1984), S. 43-62 
    ISSN: 1432-1416
    Keywords: Cellular proliferation ; cell cycle time ; linear operators ; distribution of mitogen level
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Mathematics
    Notes: Abstract This paper presents a general model for the cell division cycle in a population of cells. Three hypotheses are used: (1) There is a substance (mitogen) produced by cells which is necessary for mitosis; (2) The probability of mitosis is a function of mitogen levels; and (3) At mitosis each daughter cell receives exactly one-half of the mitogen present in the mother cell. With these hypotheses we derive expressions for the α and β curves, the distributions of mitogen and cell cycle times, and the correlation coefficients between mother-daughter (ρmd) and sister-sister (ρss) cell cycle times. The distribution of mitogen levels is shown to be given by the solution to an integral equation, and under very mild assumptions we prove that this distribution is globally asymptotically stable. We further show that the limiting logarithmic slopes of α(t) and β(t) are equal and constant, and that ρmd⩽0 while ρss⩾0. These results are in accord with the experimental results in many different cell lines. Further, the transition probability model of the cell cycle is shown to be a simple special case of the model presented here.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of mathematical biology 28 (1990), S. 33-48 
    ISSN: 1432-1416
    Keywords: Survival times ; Clinical trials ; Cancer ; Asymptotic stability ; Fractal distributions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Mathematics
    Notes: Abstract Survival functions of the form p(t) = exp[−(λt)γ], γ 〉 0 can be generated by deterministic nonlinear, asymptotically stable (chaotic) dynamical systems. These systems thus provide an alternative to stochastic interpretations of failure time data. We use this approach to analyze cancer patient survival statistics. In this manner we are able to obtain fresh insights into the implications of negative and positive clinical trials.
    Type of Medium: Electronic Resource
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