An important part of system modeling is determining parameter values, particularly for biomolecular systems, where direct measurements of individual parameters is often hard. While extended Kalman filters have been used for this purpose, the choice of the process noise covariance is generally unclear. Here , we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise  on the states and parameters, as given in the Langevin framework. We generalize a hybrid extended Kalman filtering technique by updating the estimate-dependent process noise covariance at each time step  . We compare the performance of this framework with different fixed values of process noise covariance in biomolecular system models, including an oscillator model, as well as in experimentally measured data for a negative transcriptional feedback circuit. We find that the extended Kalman filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving  balance between the mean square estimation error and parameter convergence time. These results may help in the use of extended Kalman filters for systems with process noise covariance that depends on states and/or parameters.
An important part of system modeling is determining parameter values, particularly for biomolecular systems, where direct measurements of individual parameters are typically hard. While Extended Kalman Filters have been used for this purpose, the choice of the process noise covariance is generally unclear. In this chapter , we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise covariance on the states and parameters, as given in the Langevin framework. We adapt a Hybrid Extended Kalman Filtering technique by updating the process noise covariance at each time step based on estimates . We compare the performance of this framework with different fixed values of process noise covariance in biomolecular system models, including an oscillator model, as well as in experimentally measured data for a negative transcriptional feedback circuit. We find that the Extended Kalman Filter with such process noise covariance update is closer to the optimality condition in the sense that the innovation sequence becomes white and in achieving a balance between the mean square estimation error and parameter convergence time. The results of this chapter may help in the use of Extended Kalman Filters for systems where process noise covariance depends on states and/or parameters.
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