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We discuss parameter dependent polynomial ordinary differential equations that model chemical reaction networks. By classical quasisteady state (QSS) reduction we understand the following familiar (heuristically motivated) mathematical procedure: Set the rate of change for certain (a priori chosen) variables equal to zero and use the resulting algebraic equations to obtain a system of smaller dimension for the remaining variables. This procedure will generally be valid only for certain parameter ranges. We start by showing that the reduction is accurate if and only if the corresponding parameter is what we call a QSS parameter value, and that the reduction is approximately accurate if and only if the corresponding parameter is close to a QSS parameter value. The QSS parameter values can be characterized by polynomial equations and inequations, hence parameter ranges for which QSS reduction is valid are accessible in an algorithmic manner. A defining characteristic of a QSS parameter value is that the algebraic variety * Corresponding author. Email walcher@matha.rwth-aachen.de, Phone +49 241 809 8132, Fax +49 241 809 2212. defined by the QSS relations is invariant for the differential equation.A closer investigation of the associated systems shows the existence of further invariant sets; here singular perturbations enter the picture in a natural manner. We compare QSS reduction and singular perturbation reduction, and show that, while they do not agree in general, they do, up to lowest order in a small parameter, for a quite large and relevant class of examples. This observation, in turn, allows the computation of QSS reductions even in cases where an explicit resolution of the polynomial equations is not possible. MSC (2010): 92C45, 34E15, 80A30, 13P10

We first introduce and analyze a variant of the deterministic single-substrate chemostat model. In this model, microbe removal and growth rates depend on biomass concentration, with removal terms increasing faster than growth terms. Using a comparison principle we show that persistence of all species is possible in this scenario. Then we turn to modelling the influence of random fluctuations by setting up and analyzing a stochastic differential equation. In particular, we show that random effects may lead to extinction in scenarios where the deterministic model predicts persistence. On the other hand, we also establish some stochastic persistence results.

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