Kalman-based approaches for online estimation of bioreactor dynamics from fluorescent reporter measurements

TL;DR

This paper develops Kalman filter-based methods for real-time estimation of microbial growth in bioreactors using fluorescent reporter data, accounting for reporter dynamics and handling noisy measurements.

Contribution

It introduces a novel approach that reduces nonlinear estimation problems to linear time-varying ones for improved online bioreactor monitoring.

Findings

Kalman filtering effectively estimates growth dynamics from noisy data.

The method converges in noise-free simulations.

Performance is validated through simulation results.

Abstract

We address online estimation of microbial growth dynamics in bioreactors from measurements of a fluorescent reporter protein synthesized along with microbial growth. We consider an extended version of standard growth models that accounts for the dynamics of reporter synthesis. We develop state estimation from sampled, noisy measurements in the cases of known and unknown growth rate functions. Leveraging conservation laws and regularized estimation techniques, we reduce these nonlinear estimation problems to linear time-varying ones, and solve them via Kalman filtering. We establish convergence results in absence of noise and show performance on noisy data in simulation.