Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm

Peng, Jiansheng; Meng, Fanbao; Ai, Yuncan

doi:10.1016/j.biortech.2013.03.194

Cited by 19 publications

(13 citation statements)

References 34 publications

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“…This is in agreement with the report of Kleerebezem [32]. Time-dependent control strategies for enhancing synthesis of bacteriocin have been studied by using artificial neural networks and genetic algorithms [33]. The optimization of dose-dependent and time-dependent of bacteriocin Lac-B23 is currently in progress.…”

Section: Resultssupporting

confidence: 84%

Effect of Exogenous Factors on Bacteriocin Production from Lactobacillus paracasei J23 by Using a Resting Cell System

Han

Yang

et al. 2013

IJMS

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A resting cell system was developed for bacteriocin Lac-B23 production from Lactobacillus paracasei J23. The resting cell medium contained (g/L): Glucose 20, Sodium acetate 5.0, MnSO4 0.25 MgSO4 0.5, Ammoniumhydrogencitrate 1.0, KH2PO4 1.0. The resting cell incubation time and temperature were 20 h and 37 °C and the effects of exogenous factors, including amino acids, glycerol, pyruvic acid, and α-ketoglutaric acid were investigated. Cys and Gly could stimulate the production of bacteriocin, while no stimulus effect was observed for Glu, Tyr and Ala. Glycerol and pyruvic acid increased bacteriocin production and the optimum concentrations were 1% and 30 g/L, respectively. Bacteriocin could act as an inducer of its own biosynthesis. These findings are of importance for the further study of bacteriocin biosynthesis regulation and for the improvement of bacteriocin production yields.

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Section: Resultssupporting

confidence: 84%

Effect of Exogenous Factors on Bacteriocin Production from Lactobacillus paracasei J23 by Using a Resting Cell System

Han

Yang

et al. 2013

IJMS

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“…In order to maximize the product mass, it is possible to either increase the product concentration or to increase the mass in the system. There are many control strategies which specifically aim to increase the product concentration obtained from a fed‐batch fermentation process (Chang et al, ; Kovárová‐Kovar et al, ; Peng et al, ). Increasing the product concentration is often focused on reducing by‐product formation, which is a waste of substrate, and a burden for the process.…”

Section: Introductionmentioning

confidence: 99%

A novel model‐based control strategy for aerobic filamentous fungal fed‐batch fermentation processes

Mears

Stocks

Albæk

et al. 2017

Biotech & Bioengineering

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A novel model-based control strategy has been developed for filamentous fungal fed-batch fermentation processes. The system of interest is a pilot scale (550 L) filamentous fungus process operating at Novozymes A/S. In such processes, it is desirable to maximize the total product achieved in a batch in a defined process time. In order to achieve this goal, it is important to maximize both the product concentration, and also the total final mass in the fed-batch system. To this end, we describe the development of a control strategy which aims to achieve maximum tank fill, while avoiding oxygen limited conditions. This requires a two stage approach: (i) calculation of the tank start fill; and (ii) on-line control in order to maximize fill subject to oxygen transfer limitations. First, a mechanistic model was applied off-line in order to determine the appropriate start fill for processes with four different sets of process operating conditions for the stirrer speed, headspace pressure, and aeration rate. The start fills were tested with eight pilot scale experiments using a reference process operation. An on-line control strategy was then developed, utilizing the mechanistic model which is recursively updated using on-line measurements. The model was applied in order to predict the current system states, including the biomass concentration, and to simulate the expected future trajectory of the system until a specified end time. In this way, the desired feed rate is updated along the progress of the batch taking into account the oxygen mass transfer conditions and the expected future trajectory of the mass. The final results show that the target fill was achieved to within 5% under the maximum fill when tested using eight pilot scale batches, and over filling was avoided. The results were reproducible, unlike the reference experiments which show over 10% variation in the final tank fill, and this also includes over filling. The variance of the final tank fill is reduced by over 74%, meaning that it is possible to target the final maximum fill reproducibly. The product concentration achieved at a given set of process conditions was unaffected by the control strategy. Biotechnol. Bioeng. 2017;114: 1459-1468. © 2017 Wiley Periodicals, Inc.

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“…For the control strategy fermentation parameters were optimized stagewise using the combination of ANN and genetic algorithm to create an optimal control trajectory. By that the authors increased the production of marine bacteriocin by 26% [103] . In 2019 Beiroti et al developed a control strategy based on a recurrent neural network (RNN) for the production of hepatitis B surface antigen in a methanol fed batch with Pichia pastoris.…”

Section: Modeling In Process Controlmentioning

confidence: 99%

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

Noll

Henkel

2020

Computational and Structural Biotechnology Journal

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Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm

Cited by 19 publications

References 34 publications

Effect of Exogenous Factors on Bacteriocin Production from Lactobacillus paracasei J23 by Using a Resting Cell System

Effect of Exogenous Factors on Bacteriocin Production from Lactobacillus paracasei J23 by Using a Resting Cell System

A novel model‐based control strategy for aerobic filamentous fungal fed‐batch fermentation processes

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

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