Adaptive predictive control of bioprocesses with constraint-based modeling and estimation

Jabarivelisdeh, Banafsheh; Carius, Lisa; Findeisen, Rolf; Waldherr, Steffen

doi:10.1016/j.compchemeng.2020.106744

Cited by 27 publications

(37 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, an iterative recalculation of the feed is necessary to cope with disturbances in the experiments and inaccuracies in the model prediction. Moving horizon approaches can also increase the model prediction accuracy by allowing different parameter sets in different cultivation phases for a single clone [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

et al. 2020

View full text Add to dashboard Cite

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.

show abstract

Section: Discussionmentioning

confidence: 99%

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Its primary requirement is a predictive process model, through which the dynamic and static interactions among the input, output, and disturbance variables can be apprehended and the control estimate can be synchronized with the optimum set points calculations [59,60]. Successful implementation of MPC to track the variable trajectory [49,61] and to maximize process variables has been reported in biomanufacturing [62,63]. Owing to the complexities and variabilities in mammalian bioprocesses, non-linear MPC (NMPC) with dynamic models has been successfully applied but with larger computational time [64,65].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

View full text Add to dashboard Cite

Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

show abstract

“…The in silico metabolic mapping was performed offline using the free software Optflux 3.3.0 (Rocha et al, 2010), the pFBA (parsimonious flux balance analysis) optimization method (Lewis et al, 2010), and the iND750 GSM model (Duarte et al, 2004). The maximized objective function was biomass growth, as it provided a better representation of the real behavior of the micro-organism under growth conditions (Chang et al, 2016;Jabarivelisdeh et al, 2020;Mesquita et al, 2019). •h −1 (fully aerobic) (Franzén, 2003).…”

Section: Mapping Metabolism Using the Genome-scale MMmentioning

confidence: 99%

“…However, several techniques can be used for inserting GSM information in control strategies. One viable approach consists of using a simpler metabolic model (MM) that can be optimized online without drawbacks (Chang et al, 2016; Jabarivelisdeh et al, 2020), but requires prior knowledge of the main metabolic routes involved in the formation of the desired product. Another method consists of using surrogate or meta‐models, which are simpler but preserve the same basic responses of a full model (Papadopoulou et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors

Mesquita

Campani

Giordano

et al. 2021

Biotech & Bioengineering

View full text Add to dashboard Cite

Various bio‐based processes depend on controlled micro‐aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro‐organism employed, while for industrial applications, there is no cost‐effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux‐based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome‐scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro‐aerobic fermentation strategy (MF‐ANN). The meta‐model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro‐aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro‐aeration strategies, including respiratory quotient‐based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF‐ANN provided volumetric ethanol productivity of 4.16 g·L−1·h−1 and a yield of 0.48 gethanol.gsubstrate−1, which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L−1·h−1 and 0.35–0.40 gethanol·gsubstrate−1, respectively). Due to its modular character, the MF‐ANN strategy could be adapted to other micro‐aerated bioprocesses.

show abstract

Adaptive predictive control of bioprocesses with constraint-based modeling and estimation

Cited by 27 publications

References 28 publications

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

Bioprocess Control: Current Progress and Future Perspectives

Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors

Contact Info

Product

Resources

About