Simultaneous real-time estimation of maximum substrate uptake capacity and yield coefficient in induced microbial cultures

Müller, Don Fabian; Wibbing, Daniel; Herwig, Christoph; Kager, Julian

doi:10.1016/j.compchemeng.2023.108203

Cited by 9 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, we estimate uptake rates during production based on experimental data. As uptake can be significantly reduced during production [39] this avoids possible overfeeding in the predicted optimum. In the case study, we observe a reduced uptake rate, but uptake is not a limiting factor for optimization.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Schloegel,

Lueck,

Kittler

et al. 2024

Preprint

View full text Add to dashboard Cite

Biotechnological production of a recombinant molecules relies heavily on fed-batch processes. However, as the cells' growth, substrate uptake, and production kinetics are often unclear, the fed-batches are frequently operated under sub-optimal conditions. For example, process designs are based on simple feed profiles (e.g., constant or exponential), operator experience, and basic statistical tools like response surface methodology (RSM), which are unable to harvest the full potential of the production processes. To address this challenge, we propose a general modeling framework, OptFed, which utilizes experimental data from non-optimal fed-batch processes to predict an optimal process. In detail, we assume the cell-specific production rate depends on all state variables and their changes over time. Using measurements of bioreactor volume, biomass, and product, we train an ordinary differential equation model. To avoid overfitting, we use a regression model to reduce the number of kinetic parameters. Then, we predict the optimal process conditions (temperature and feed rate) by solving an optimal control problem using orthogonal collocation and nonlinear programming. We apply OptFed to a recombinant protein L fed-batch production process. We determine optimal controls for feed rate and reactor temperature to maximize the product-to-biomass yield and successfully validate our predictions experimentally. Notably, our framework outperforms RSM in both simulation and experiments, capturing an optimum previously missed. We improve the experimental product-to-biomass ratio by 19 % and showcase OptFed's potential for enhancing process optimization in biotechnology.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Schloegel,

Lueck,

Kittler

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…State observers (soft sensors) are widely used to estimate nonobserved process state variables from available measurements (Soroush, 1997). The first implementation of the Kalman Filter (KF) (Chui & Chen, 2017; Kalman, 1960) was later extended to Particle Filters (Chen et al, 2004; del Moral, 1996), which are arguably the most commonly applied observers in bioprocess engineering (Krämer et al, 2020; Müller et al, 2023; Neddermeyer et al, 2016; Sinner et al, 2022). While initially designed for linear systems, numerous alternatives have been proposed to the general KF approach (Kalman, 1960) to better capture nonlinearities in the system, for example, the derivative‐free unscented KF (Julier et al, 1995), and the more generalized central differences KF (Ito & Xiong, 2000; Nørgaard et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear state estimation as tool for online monitoring and adaptive feed in high throughput cultivations

Sawatzki

Fischer

Wilms

et al. 2023

Biotech & Bioengineering

View full text Add to dashboard Cite

Robotic facilities that can perform advanced cultivations (e.g., fed‐batch or continuous) in high throughput have drastically increased the speed and reliability of the bioprocess development pipeline. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation for the implementation of feedback control methods in miniaturized bioreactor systems, where observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma‐Point Kalman Filter in a high throughput platform with 24 parallel experiments at the mL‐scale to demonstrate its viability and added value in high throughput experiments. The filter exploits the information generated by the ammonia‐based pH control to enable the continuous estimation of the biomass concentration, a critical state to monitor the specific rates of production and consumption in the process. The objective in the selected case study is to ensure that the selected specific substrate consumption rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of an antibody fragment.

show abstract

Extension of first principle elemental balancing soft-sensors by nonlinear reaction kinetics for increased robustness in bioprocess monitoring

Müller,

Wibbing,

Kager

2024

Bioprocess Biosyst Eng

View full text Add to dashboard Cite

Simultaneous real-time estimation of maximum substrate uptake capacity and yield coefficient in induced microbial cultures

Cited by 9 publications

References 37 publications

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Nonlinear state estimation as tool for online monitoring and adaptive feed in high throughput cultivations

Extension of first principle elemental balancing soft-sensors by nonlinear reaction kinetics for increased robustness in bioprocess monitoring

Contact Info

Product

Resources

About