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1.
Math Biosci Eng ; 16(5): 5062-5091, 2019 06 03.
Article in English | MEDLINE | ID: mdl-31499704

ABSTRACT

On the basis of recent work by Cardin and Teixeira on ordinary differential equations with more than two time scales, we devise a coordinate-independent reduction for systems with three time scales; thus no a priori separation of variables into fast, slow etc. is required. Moreover we consider arbitrary parameter dependent systems and extend earlier work on Tikhonov-Fenichel parameter values - i.e. parameter values from which singularly perturbed systems emanate upon small perturbations - to the three time-scale setting. We apply our results to two standard systems from biochemistry.

2.
J Math Biol ; 78(1-2): 413-439, 2019 01.
Article in English | MEDLINE | ID: mdl-30094616

ABSTRACT

The Rosenzweig-MacArthur system is a particular case of the Gause model, which is widely used to describe predator-prey systems. In the classical derivation, the interaction terms in the differential equation are essentially derived from considering handling time vs. search time, and moreover there exist derivations in the literature which are based on quasi-steady state assumptions. In the present paper we introduce a derivation of this model from first principles and singular perturbation reductions. We first establish a simple stochastic mass action model which leads to a three-dimensional ordinary differential equation, and systematically determine all possible singular perturbation reductions (in the sense of Tikhonov and Fenichel) to two-dimensional systems. Among the reductions obtained we find the Rosenzweig-MacArthur system for a certain choice of small parameters as well as an alternative to the Rosenzweig-MacArthur model, with density dependent death rates for predators. The arguments to obtain the reductions are intrinsically mathematical; no heuristics are employed.


Subject(s)
Food Chain , Models, Biological , Predatory Behavior , Algorithms , Animals , Computational Biology , Ecosystem , Mathematical Concepts , Stochastic Processes , Systems Biology
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