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Free keywords:
Estimation and control in systems biology, Estimation and fault detection, Process modeling and identification
Abstract:
Deriving a predictive model in systems biology is a complex task. One major problem is the typically large network size, which renders the analysis with standard methods di cult. Symmetry, as omnipresent in nature, was used in many applications to encounter this problem. In this work, we investigate the in uence of symmetry on set-based parameter estimation. We show that the presence of symmetry in a model can be used to signi cantly simplify the parameter estimation problem. This is done by determining a symmetry-adapted basis, corresponding to a linear representation of a nite group, in which the problem size is of smaller dimension. We demonstrate the applicability of this approach for several common network motifs, as e.g. the Michaelis-Menten reaction and the feedforward motif.
