History and Evolution of Modeling in Biotechnology: Modeling &amp; Simulation, Application and Hardware Performance

Noll, Philipp; Henkel, Marius

doi:10.1016/j.csbj.2020.10.018

Cited by 40 publications

(36 citation statements)

References 149 publications

(167 reference statements)

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“…For this purpose, mathematical modeling of the bioprocess combining a metabolic model of the nitrogen metabolism, oxygen availability and consumption, along with mechanisms of genetic regulation can be used to provide further insights into this complex interplay. Using this toolset, investigation and evaluation of a dual-limitation fed-batch process becomes feasible, which is a very demanding task for process control, as both limitations need to be tightly regulated (Noll and Henkel 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an oxygen‐dependent self‐inducible surfactin synthesis in B. subtilis by substitution of native promoter PsrfA by anaerobically active PnarG and PnasD

et al. 2021

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A novel approach targeting self-inducible surfactin synthesis under oxygen-limited conditions is presented. Because both the nitrate (NarGHI) and nitrite (NasDE) reductase are highly expressed during anaerobic growth of B. subtilis, the native promoter PsrfA of the surfactin operon in strain B. subtilis JABs24 was replaced by promoters PnarG and PnasD to induce surfactin synthesis anaerobically. Shake flask cultivations with varying oxygen availabilities indicated no significant differences in native PsrfA expression. As hypothesized, activity of PnarG and PnasD increased with lower oxygen levels and surfactin was not produced by PsrfA::PnarG as well as PsrfA::PnasD mutant strains under conditions with highest oxygen availability. PnarG showed expressions similar to PsrfA at lowest oxygen availability, while maximum value of PnasD was more than 5.5-fold higher. Although the promoter exchange PsrfA::PnarG resulted in a decreased surfactin titer at lowest oxygen availability, the strain carrying PsrfA::PnasD reached a 1.4-fold increased surfactin concentration with 696 mg/L and revealed an exceptional high overall YP/X of 1.007 g/g. This value also surpassed the YP/X of the reference strain JABs24 at highest and moderate oxygen availability. Bioreactor cultivations illustrated that significant cell lysis occurred when the process of “anaerobization” was performed too fast. However, processes with a constantly low agitation and aeration rate showed promising potential for process improvement, especially by employing the strain carrying PsrfA::PnasD promoter exchange. Additionally, replacement of other native promoters by nitrite reductase promoter PnasD represents a promising tool for anaerobic-inducible bioprocesses in Bacillus.

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Section: Discussionmentioning

confidence: 99%

Evaluation of an oxygen‐dependent self‐inducible surfactin synthesis in B. subtilis by substitution of native promoter PsrfA by anaerobically active PnarG and PnasD

et al. 2021

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“…Apart from this, single input single output (SISO) MPC and multiple input multiple output (MIMO) MPC when used for online estimation and control of the fed batch reactor have demonstrated better results than proportional integral controller and feedback/feedforward controller. Steady state stabilisation in oscillating cell culture bioreactors can be achieved by implementing MPC designed based on cell population balance models [69].…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…In the case of data driven model, a large number of data points are required [48,57,71]. Further, the MPC approach is considered as computationally expensive in comparison to other control strategies, especially when optimization is required at each time step [69,72]. Thus, despite MPC being well established in chemical industries, its acceptance in biotech sector requires further development of robust process models.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

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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.

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“…In the case of chemical process optimization, in [3], measured operating data are processed via an artificial network and integrated into a flow-sheet simulator of a chemical synthesis problem. Moreover, hybrid models are also an essential key technology to realize Industry 4.0 and digital twin concepts in the pharmaceutical industry and biotechnology for process intensification and monitoring [4,5]. For instance, Krippl et al highlight the added value of hybrid models for improved prediction of degradation processes in separation technology [6], and Cardillo et al discuss the relevant role of hybrid models in in silico process development for vaccine manufacturing [7].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Process Models in Electrochemical Syntheses under Deep Uncertainty

2021

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Chemical process engineering and machine learning are merging rapidly, and hybrid process models have shown promising results in process analysis and process design. However, uncertainties in first-principles process models have an adverse effect on extrapolations and inferences based on hybrid process models. Parameter sensitivities are an essential tool to understand better the underlying uncertainty propagation and hybrid system identification challenges. Still, standard parameter sensitivity concepts may fail to address comprehensive parameter uncertainty problems, i.e., deep uncertainty with aleatoric and epistemic contributions. This work shows a highly effective and reproducible sampling strategy to calculate simulation uncertainties and global parameter sensitivities for hybrid process models under deep uncertainty. We demonstrate the workflow with two electrochemical synthesis simulation studies, including the synthesis of furfuryl alcohol and 4-aminophenol. Compared with Monte Carlo reference simulations, the CPU-time was significantly reduced. The general findings of the hybrid model sensitivity studies under deep uncertainty are twofold. First, epistemic uncertainty has a significant effect on uncertainty analysis. Second, the predicted parameter sensitivities of the hybrid process models add value to the interpretation and analysis of the hybrid models themselves but are not suitable for predicting the real process/full first-principles process model’s sensitivities.

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History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance

Abstract: Graphical abstract

Cited by 40 publications

References 149 publications

Evaluation of an oxygen‐dependent self‐inducible surfactin synthesis in B. subtilis by substitution of native promoter PsrfA by anaerobically active PnarG and PnasD

Evaluation of an oxygen‐dependent self‐inducible surfactin synthesis in B. subtilis by substitution of native promoter PsrfA by anaerobically active PnarG and PnasD

Bioprocess Control: Current Progress and Future Perspectives

Hybrid Process Models in Electrochemical Syntheses under Deep Uncertainty

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