Classical quasi-steady state reduction : A mathematical characterization

Abstract We discuss parameter dependent polynomial ordinary differential equations that model chemical reaction networks. By classical quasi-steady 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 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.

[1]  Ioannis G. Kevrekidis,et al.  Constraint-Defined Manifolds: a Legacy Code Approach to Low-Dimensional Computation , 2005, J. Sci. Comput..

[2]  Sebastian Walcher,et al.  Tikhonov's Theorem and Quasi-Steady State , 2011 .

[3]  Holger Fröhlich,et al.  Analysis of Reaction Network Systems Using Tropical Geometry , 2015, CASC.

[4]  P. Hartman Ordinary Differential Equations , 1965 .

[5]  Eva Zerz,et al.  Determining “small parameters” for quasi-steady state , 2015 .

[6]  Dima Grigoriev,et al.  Model reduction of biochemical reactions networks by tropical analysis methods , 2015, 1503.01414.

[7]  M. Schauer,et al.  Analysis of the quasi-steady-state approximation for an enzymatic one-substrate reaction. , 1979, Journal of theoretical biology.

[8]  H. M. Tsuchiya,et al.  On the mathematical status of the pseudo-steady state hypothesis of biochemical kinetics☆ , 1967 .

[9]  Sebastian Walcher,et al.  A constructive approach to quasi-steady state reductions , 2014, Journal of Mathematical Chemistry.

[10]  S. Walcher,et al.  Side conditions for ordinary differential equations , 2014, 1406.4111.

[11]  Inverse problems for multiple invariant curves , 2007, Proceedings of the Royal Society of Edinburgh: Section A Mathematics.

[12]  Matthias Stiefenhofer Quasi-steady-state approximation for chemical reaction networks , 1998 .

[13]  Eva Zerz,et al.  Quasi-Steady State - Intuition, Perturbation Theory and Algorithmic Algebra , 2015, CASC.

[14]  David A. Cox,et al.  Using Algebraic Geometry , 1998 .

[15]  Robert A. Alberty,et al.  Kinetics of the Reversible Michaelis-Menten Mechanism and the Applicability of the Steady-state Approximation1 , 1958 .

[16]  Neil Fenichel Geometric singular perturbation theory for ordinary differential equations , 1979 .

[17]  Neil Fenichel Persistence and Smoothness of Invariant Manifolds for Flows , 1971 .

[18]  Sebastian Walcher,et al.  Quasi-Steady State: Searching for and Utilizing Small Parameters , 2013 .

[19]  Hans Schönemann,et al.  SINGULAR: a computer algebra system for polynomial computations , 2001, ACCA.

[20]  L. Segel,et al.  Extending the quasi-steady state approximation by changing variables. , 1996, Bulletin of mathematical biology.

[21]  K. Šišková,et al.  Extension and Justification of Quasi-Steady-State Approximation for Reversible Bimolecular Binding , 2015, Bulletin of mathematical biology.

[22]  L. Pontryagin,et al.  Ordinary differential equations , 1964 .

[23]  M. Bennett,et al.  Transient dynamics of genetic regulatory networks. , 2007, Biophysical journal.

[24]  Krešimir Josić,et al.  Reduced models of networks of coupled enzymatic reactions. , 2011, Journal of theoretical biology.

[25]  L. A. Segel,et al.  The Quasi-Steady-State Assumption: A Case Study in Perturbation , 1989, SIAM Rev..

[26]  Holger Fröhlich,et al.  A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions , 2015, Bulletin of Mathematical Biology.

[27]  James P. Keener,et al.  Comprar Mathematical Physiology · I: Cellular Physiology | Keener, James | 9780387758466 | Springer , 2009 .

[28]  Dimitris A. Goussis,et al.  Quasi steady state and partial equilibrium approximations: their relation and their validity , 2012 .

[29]  Hans G. Kaper,et al.  Two perspectives on reduction of ordinary differential equations , 2005 .

[30]  Sebastian Walcher,et al.  Quasi-Steady State and Nearly Invariant Sets , 2009, SIAM J. Appl. Math..

[31]  G. Briggs,et al.  A Note on the Kinetics of Enzyme Action. , 1925, The Biochemical journal.

[32]  Sebastian Walcher,et al.  Reduktion und asymptotische Reduktion von Reaktionsgleichungen , 2013 .

[33]  James B. Rawlings,et al.  The QSSA in Chemical Kinetics: As Taught and as Practiced , 2014 .

[34]  S. Lam,et al.  The CSP method for simplifying kinetics , 1994 .

[35]  I. Shafarevich Basic algebraic geometry , 1974 .

[36]  H. W. Wiley LOIS GÉNÉRALES DE L'ACTION DES DIASTASES. , 1903 .

[37]  E. Kunz Introduction to commutative algebra and algebraic geometry , 1984 .

[38]  Eva Zerz,et al.  Computing quasi-steady state reductions , 2012, Journal of Mathematical Chemistry.