Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms

IF 3.7 3区生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biochemical Engineering Journal Pub Date : 2025-03-19 DOI:10.1016/j.bej.2025.109729

Martin F. Luna , Federico M. Mione , Lucas Kaspersetz , Peter Neubauer , Ernesto C. Martinez , M. Nicolas Cruz Bournazou

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引用次数: 0

Abstract

Mathematical models of bioreactors are powerful tools that aid in the analysis and prediction of process operation. However, the complex behavior of microorganisms makes modelling of biological processes a particularly challenging task, especially in the early developmental stages when data and knowledge are scarce. As a result, bioreactor models may perform poorly due to structural errors or high uncertainty in their parameterization. Here, we present a method for automated dynamic model regression based on a Bayesian approach that can be applied in the operation of laboratory robotic platforms to perform both parameter estimation and state predictions for a given experimental design. Starting with wide distributions over parameters (prior knowledge), the model is updated as new data is generated and is then used to predict the evolution of the experiment. The proposed method is tested with data from several parallel cultivations from a 24 mini-bioreactors platform containing an Escherichia coli strain operating in fed-batch mode. The results highlight both the versatility of the approach to estimate parameter distribution as well as to predict the state evolution.

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来源期刊

Biochemical Engineering Journal 工程技术-工程：化工

CiteScore

7.10

自引率

5.10%

发文量

380

审稿时长

34 days

期刊介绍： The Biochemical Engineering Journal aims to promote progress in the crucial chemical engineering aspects of the development of biological processes associated with everything from raw materials preparation to product recovery relevant to industries as diverse as medical/healthcare, industrial biotechnology, and environmental biotechnology. The Journal welcomes full length original research papers, short communications, and review papers* in the following research fields: Biocatalysis (enzyme or microbial) and biotransformations, including immobilized biocatalyst preparation and kinetics Biosensors and Biodevices including biofabrication and novel fuel cell development Bioseparations including scale-up and protein refolding/renaturation Environmental Bioengineering including bioconversion, bioremediation, and microbial fuel cells Bioreactor Systems including characterization, optimization and scale-up Bioresources and Biorefinery Engineering including biomass conversion, biofuels, bioenergy, and optimization Industrial Biotechnology including specialty chemicals, platform chemicals and neutraceuticals Biomaterials and Tissue Engineering including bioartificial organs, cell encapsulation, and controlled release Cell Culture Engineering (plant, animal or insect cells) including viral vectors, monoclonal antibodies, recombinant proteins, vaccines, and secondary metabolites Cell Therapies and Stem Cells including pluripotent, mesenchymal and hematopoietic stem cells; immunotherapies; tissue-specific differentiation; and cryopreservation Metabolic Engineering, Systems and Synthetic Biology including OMICS, bioinformatics, in silico biology, and metabolic flux analysis Protein Engineering including enzyme engineering and directed evolution.