Publications

What is a Publication?
5 Publications visible to you, out of a total of 5
A protocol for recombinant protein quantification by densitometry

Abstract (Expand)

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

Abstract (Expand)

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

Abstract (Expand)

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

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

Abstract (Expand)

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

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

Powered by
 (v.1.17.1)
About IBISBA | About IBISBA Knowledge Hub | Funding and Programmes | Credits | Terms & Conditions
Copyright © 2008 - 2025 The University of Manchester and HITS gGmbH
IBISBA is a pan-European research infrastructure currently funded through multiple EU projects: IBISBA 1.0 (H2020 grant agreement No. 730976), PREP-IBISBA (H2020 grant agreement No. 871118) and the follow-on project IBISBA-DIALS (grant agreement No. 101131085) and BIOINDUSTRY 4.0 (Grant agreement No. 101094287). Registering data or other knowledge assets on this platform is the sole responsibility of Users. IBISBA cannot be held responsible for misuse or misappropriation of data and assets belonging to a Third Party.