title: Sensitivity and control analysis of periodically forced reaction networks using the Green's function method.
abstract: A general sensitivity and control analysis of periodically forced reaction networks with respect to small perturbations in arbitrary network parameters is presented. A well-known property of sensitivity coefficients for periodic processes in dynamical systems is that the coefficients generally become unbounded as time tends to infinity. To circumvent this conceptual obstacle, a relative time or phase variable is introduced so that the periodic sensitivity coefficients can be calculated. By employing the Green's function method, the sensitivity coefficients can be defined using integral control operators that relate small perturbations in the network's parameters and forcing frequency to variations in the metabolite concentrations and reaction fluxes. The properties of such operators do not depend on a particular parameter perturbation and are described by the summation and connectivity relationships within a control-matrix operator equation. The aim of this paper is to derive such a general control-matrix operator equation for periodically forced reaction networks, including metabolic pathways. To illustrate the general method, the two limiting cases of high and low forcing frequency are considered. We also discuss a practically important case where enzyme activities and forcing frequency are modulated simultaneously. We demonstrate the developed framework by calculating the sensitivity and control coefficients for a simple two reaction pathway where enzyme activities enter reaction rates linearly and specifically.
authors.affiliation: Department of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA. en46@cornell.edu
authors.forename: Evgeni V
authors.initials: EV
authors.lastname: Nikolaev
authors.forename: Jordan C
authors.initials: JC
authors.lastname: Atlas
authors.forename: Michael L
authors.initials: ML
authors.lastname: Shuler
mesh_terms: D000818:Animals; D004798:Enzymes / Q000378:metabolism; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010507:Periodicity
chemical_list: D004798:Enzymes