On the identification of biochemical systems from intermittent and noisy data

Abstract

Recently, system identification of biochemical circuits and systems has gained much research attention. In this paper we show how the problem of identifying a biochemical circuit of interest can be recast as a minimax state estimation problem. Building upon this framework, we present algorithmic procedures which not only estimate the biochemical parameters of a system/circuit of interest from noisy and intermittent data, but also the functions of the underlying dynamical model.