Soft sensor for monitoring dynamic changes in cell composition

Abstract

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia coli’s metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens.