Identifiability and Kalman Filter Parameter Estimation Applied to Biomolecular Controller Motifs

Abstract

In this paper we apply Augmented Extended Kalman filters (AEKFs) to performparameter estimation in two different biological controller motifs under both noise-free andnoisy conditions. Based on measurements of the two states of the controller motifs, we showthat under both noise conditions it is possible to estimate all 5 and 6 parameters, respectively,which is in accordance with previously published results that investigated the theoretical conceptof structural identifiability. We further investigate how the level of process/measurement noiseand the initial estimates of both the parameters and states in the AEKFs affect the estimationperformance, and the results indicate that the degree of non-linearity affects filter performance.