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8 Publications visible to you, out of a total of 8
A protocol for recombinant protein quantification by densitometry

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The protein purity is generally checked using SDS-PAGE, where densitometry could be used to quantify the protein bands. In literature, few studies have been reported using image analysis for the … quantification of protein in SDS-PAGE: that is, imaged with Stain-Free™ technology. This study presents a protocol of image analysis for electrophoresis gels that allows the quantification of unknown proteins using the molecular weight markers as protein standards. Escherichia coli WK6/pHEN6 encoding the bispecific nanobody CH10-12 engineered by the Pasteur Institute of Tunisia was cultured in a bioreactor and induced with isopropyl β-D-1-thiogalactopyranoside (IPTG) at 28°C for 12 hr. Periplasmic proteins extracted by osmotic shock were purified by immobilized metal affinity chromatography (IMAC). Images of the SDS-PAGE gels were analyzed using ImageJ, and the lane profiles were obtained in grayscale and uncalibrated optical density. Protein load and peak area were linearly correlated, and optimal image processing was then performed by background subtraction using the rolling ball algorithm with radius size 250 pixels. No brightness and contrast adjustment was applied. The production of the nanobody CH10-12 was obtained through a fed-batch strategy and quantified using the band of 50 kDa in the marker as reference for 750 ng of recombinant protein. The molecular weight marker was used as a sole protein standard for protein quantification in SDS-PAGE gel images.

Authors: Susana María Alonso Villela, Hazar Kraïem, Balkiss Bouhaouala‐Zahar, Carine Bideaux, César Arturo Aceves Lara, Luc Fillaudeau

Date Published: 1st Jun 2020

Publication Type: Journal

Effect of temperature on the production of a recombinant antivenom in fed-batch mode

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In the pharmaceutical industry, nanobodies show promising properties for its application in serotherapy targeting the highly diffusible scorpion toxins. The production of recombinant nanobodies in … Escherichia coli has been widely studied in shake flask cultures in rich medium. However, there are no upstream bioprocess studies of nanobody production in defined minimal medium and the effect of the induction temperature on the production kinetics. In this work, the effect of the temperature during the expression of the chimeric bispecific nanobody CH10-12 form, showing high scorpion antivenom potential, was studied in bioreactor cultures of E. coli. High biomass concentrations (25 g cdw/L) were achieved in fed-batch mode, and the expression of the CH10-12 nanobody was induced at temperatures 28, 29, 30, 33, and 37°C with a constant glucose feed. For the bispecific form NbF12-10, the induction was performed at 29°C. Biomass and carbon dioxide yields were reported for each culture phase, and the maintenance coefficient was obtained for each strain. Nanobody production in the CH10-12 strain was higher at low temperatures (lower than 30°C) and declined with the increase of the temperature. At 29°C, the CH10-12, NbF12-10, and WK6 strains were compared. Strains CH10-12 and NbF12-10 had a productivity of 0.052 and 0.021 mg/L/h of nanobody, respectively, after 13 h of induction. The specific productivity of the nanobodies was modeled as a function of the induction temperature and the specific growth rates. Experimental results confirm that low temperatures increase the productivity of the nanobody. Key points • Nanobodies with scorpion antivenom activity produced using two recombinant strains. • Nanobodies production was achieved in fed-batch cultures at different induction temperatures. • Low induction temperatures result in high volumetric productivities of the nanobody CH10-12.

Authors: Susana María Alonso Villela, Hazar Ghezal-Kraïem, Balkiss Bouhaouala-Zahar, Carine Bideaux, César Arturo Aceves Lara, Luc Fillaudeau

Date Published: 1st Feb 2021

Publication Type: Journal

From legacy systems to data pipelines modernization in fermentation process

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Developments in biotechnology using high throughput systems are increasingly and consequently the creation and consumption of data continue to grow rapidly. Data migration is an essential part of legacy … system modernization in bioprocess. Migration process involves transferring data from outdated platforms or unknown data schemas to more advanced and secure systems. Data migration can be represented through data pipelines including data extraction, transformation and loading (ETL). The data pipelines are implemented in order to increase the overall efficiency of data-flow from the source (raw data) to the knowledge generation (Mohanty et al., 2013). Legacy systems in fermentation generally occur in bioreactor components as sensors, protocols, software or databases. These issues can limit the integration with modern tools and systems as Process Analytical Technology (PAT) instruments (Gerzon et al., 2022), avoiding real-time data on process parameters and thereby fail to assist operators in maintain optimal conditions for cell growth and production. The aim of this research is to present a guided process for designing data pipelines in bioreactors legacy systems. We present as use case a set of 24 mini-bioreactors of 50 mL. We conducted unit testing for components of the ETL process in order to ensure the integration and migration process of the legacy DB.

Editor:

Date Published: 2024

Publication Type: Journal

MIFE and MIFD: Minimum information for fermentation experiments and devices

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Abstract Background As the technological advancements of the early 21st century are pushing industrial biotechnology (IB) into the realm of Big Data–driven innovation, the requirement for trustworthy …Big Data–driven innovation, the requirement for trustworthy data management, annotation, and standardization is emerging as a necessity. Minimum information models (MIMs) have long been used across disciplines as the backbone of good data management practices by providing the scaffold upon which standardized recording of metadata can adequately and succinctly describe an understudied phenomenon. Findings Here we present a minimum set of metadata, named the minimum information for fermentation experiments (MIFE) and devices (MIFD), that has been specifically designed to accommodate the data management and annotation needs of IB-related fermentation experiments. Although the proposed schema is tailored to IB applications, MIFE and MIFD build upon well-established models and community standards to facilitate easier integration with existing infrastructure and easier adoption by the community, as well as aim to integrate Findable, Accessible, Interoperable, and Reproducible (FAIR) principles in the IB field. In addition, the integration with FAIR Data Station (FAIR DS), a tool that offers metadata validation and enables the automated uptake of (meta)data from data management repositories such as FAIRDOM-SEEK, is showcased. The proposed models are accompanied by a Python package that enables their programmatic use by creating a Linked Data Modeling Language (LinkML) schema that can fuel subsequent analyses. Conclusions Through the promotion and simplification of knowledge discovery, we believe that MIFE and MIFD can accelerate the application of state-of-the-art artificial intelligence (AI) methods and the adoption of explainable AI to better understand bioprocesses at scale.

Authors: Georgios K Georgakilas, Brett Metcalfe, Ariane Bize, Matthew Crowther, Emilie Fernandez, Susana Maria Alonso Villela, Stuart Owen, Rudolf Wittner, David Camilo Corrales, Anselm von Gladiss, Peter Blomberg, Munazah Andrabi, Cesar Arturo Aceves Lara, Hans Mattila, Marily Wiebe, Theodore Dalamagas, Jasper J Koehorst

Date Published: 2026

Publication Type: Journal

Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process

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This paper outlines real-world control challenges faced by modern-day biopharmaceutical facilities through the extension of a previously developed industrial-scale penicillin fermentation simulation ( … IndPenSim ). The extensions include the addition of a simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. IndPenSim can be operated in fixed or operator controlled mode and gener- ates all the available on-line, off-line and Raman spectra for each batch. The capabilities of IndPenSim were initially demonstrated through the implementation of a QbD methodology utilising the three stages of the PAT framework. Furthermore, IndPenSim evaluated a fault detection algorithm to detect process faults occurring on different batches recorded throughout a yearly campaign. The simulator and all data presented here are available to download at www.industrialpenicillinsimulation.com and acts as a benchmark for researchers to analyse, improve and optimise the current control strategy implemented on this facility. Additionally, a highly valuable data resource containing 100 batches with all available process and Raman spectroscopy measurements is freely available to download. This data is highly suitable for the development of big data analytics, machine learning (ML) or artificial intelligence (AI) algorithms applicable to the biopharmaceutical industry.

Authors: Stephen Goldrick, Carlos A. Duran-Villalobos, Karolis Jankauskas, David Lovett, Suzanne S. Farid, Barry Lennox

Date Published: 1st Nov 2019

Publication Type: Journal

Soft sensors based on interpretable learners for industrial-scale fed-batch fermentation: Learning from simulations

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Monitoring bioprocesses is a challenging task where most of the variables of interest can only be measured offline. Soft sensors have emerged as a solution to provide online estimations. This work … compares interpretable learners such as CART, M5, CUBIST, and Random Forest as soft sensors for industrial-scale fed-batch fermentation of penicillin production. A structured model of industrial-scale penicillin fermentation is implemented to generate the dataset and train the interpretable learners. Variables such as substrate feed rate, agitation, temperature, pH, dissolved oxygen, vessel volume, CO2, and O2 percent in off-gas are considered as independent (predictors). The CUBIST model has achieved the best results with values of 0.908, 9.916, 3.149, and 1.920 for the coefficient of determination, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, respectively. These results demonstrate the feasibility of developing soft sensors based on interpretable models to predict penicillin concentration at an industrial scale.

Authors: Juan Camilo Acosta-Pavas, Carlos Eduardo Robles-Rodriguez, David Griol, Fayza Daboussi, Cesar Arturo Aceves-Lara, David Camilo Corrales

Date Published: 1st Aug 2024

Publication Type: Journal

The development of an industrial-scale fed-batch fermentation simulation

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Abstract This paper describes a simulation of an industrial-scale fed-batch fermentation that can be used as a benchmark in process systems analysis and control studies. The simulation was developed … using a mechanistic model and validated using historical data collected from an industrial-scale penicillin fermentation process. Each batch was carried out in a 100,000 L bioreactor that used an industrial strain of Penicillium chrysogenum. The manipulated variables recorded during each batch were used as inputs to the simulator and the predicted outputs were then compared with the on-line and off-line measurements recorded in the real process. The simulator adapted a previously published structured model to describe the penicillin fermentation and extended it to include the main environmental effects of dissolved oxygen, viscosity, temperature, pH and dissolved carbon dioxide. In addition the effects of nitrogen and phenylacetic acid concentrations on the biomass and penicillin production rates were also included. The simulated model predictions of all the on-line and off-line process measurements, including the off-gas analysis, were in good agreement with the batch records. The simulator and industrial process data are available to download at www.industrialpenicillinsimulation.com and can be used to evaluate, study and improve on the current control strategy implemented on this facility.

Authors: Stephen Goldrick, Andrei Ştefan, David Lovett, Gary Montague, Barry Lennox

Date Published: 2015

Publication Type: Journal

Towards a machine learning operations (MLOps) soft sensor for real-time predictions in industrial-scale fed-batch fermentation

Abstract (Expand)

Real-time predictions in fermentation processes are crucial because they enable continuous monitoring and control of bioprocessing. However, the availability of online measurements is limited by the … availability and feasibility of sensing technology. Soft sensors - or software sensors that convert available measurements into measurements of interest (product yield, quality, etc.) - have the potential to improve efficiency and product quality. Machine learning (ML) based soft sensors have gained increased popularity over the years since they can incorporate variables that are measured in real-time, and exploit the intricate patterns embedded in such voluminous datasets. However, ML-based soft sensor requires more than just a classical ML learner with an unseen test set to evaluate the quality prediction of the model. When a ML model is deployed in production, its performance can deteriorate rapidly leading to an unanticipated decline in the quality of the output and predictions. Here a proof concept of Machine Learning Operations (MLOps) to automate the end-to-end soft sensor lifecycle in industrial scale fed-batch fermentation, from development and deployment to maintenance and monitoring is proposed. Using the industrial-scale penicillin fermentation (IndPenSim) dataset that includes 100 fermentation batches, to build a soft sensor based on Long Short Term Memory (LSTM) for penicillin concentration prediction. The batches containing deviations in the processes (91–100) were used to assess concept drift of the LSTM soft sensor. The evaluation of concept drift is evidenced by the soft sensor performance falling below the set threshold based on the Population Stability Index (PSI), which automatically triggers an alert to run the retraining pipeline.

Authors: Brett Metcalfe, Juan Camilo Acosta-Pavas, Carlos Eduardo Robles-Rodriguez, George K. Georgakilas, Theodore Dalamagas, Cesar Arturo Aceves-Lara, Fayza Daboussi, Jasper J Koehorst, David Camilo Corrales

Date Published: 1st Mar 2025

Publication Type: Journal

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