technical report Inferring kinetic parameters and their variances from a time series of concentrations


Methods for parameter estimation, that are robust to the experimental uncertainties, stochastic and biological noise and requiring a minimum a priori input knowledge, are of key importance in computational systems biology. Moreover, in the ambit of dynamic modelling and analysis of biochemical networks, a model-based approach in the experimental design is attracting the attention and challenging the efforts of the theoreticians and experimentalists in developing mathematical models of parameter and data inference that can be used to estimate the accuracy of an experimental outcome, and, eventually guide experimental procedure to increase it In this paper we present present a method for deducing kinetic rates, their variance measures and their region of confidence from noisy time series of concentration. This new method was applied to a challenging parameter estimation problems of nonlinear dynamic biological systems: the twelve-state model of the SERCA pump.

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P. Lecca,  A. Palmisano,  C. Priami