Modelling concentration gradients in fed‐batch cultivations of E. coli – towards the flexible design of scale‐down experiments

BACKGROUND: The impact of concentration gradients in large industrial-scale bioreactors on microbial physiology can be studied in scale-down bioreactors. However, scale-down systems pose several challenges in construction, operation and footprint. Therefore, it is challenging to implement them in emerging technologies for bioprocess development, such as in high throughput cultivation platforms. In this study, a mechanistic model of a two-compartment scale-down bioreactor is developed. Simulations from this model are then used as bases for a pulse-based scale-down bioreactor suitable for application in parallel cultivation systems. RESULTS: As an application, the pulse-based system model was used to study the misincorporation of non-canonical branched-chain amino acids into recombinant pre-proinsulin expressed in Escherichia coli, as a response to oscillations in glucose and dissolved oxygen concentrations. The results show significant accumulation of overflow metabolites, up to 18.3 % loss in product yield and up to 10 fold accumulation of the non-canonical amino acids norvaline and norleucine in the product in the pulse-based cultivation, compared to a reference cultivation. CONCLUSIONS: Our results indicate that the combination of a pulse-based scale-down approach with mechanistic models is a very suitable method to test strain robustness and physiological constraints at the early stages of bioprocess development.EC/H2020/643056/EU/Rapid Bioprocess Development/Biorapi

Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design

Especially in biomanufacturing, methods to design optimal experiments are a valuable technique to fully exploit the potential of the emerging technical possibilities that are driving experimental miniaturization and parallelization. The general objective is to reduce the experimental effort while maximizing the information content of an experiment, speeding up knowledge gain in R&D. The approach of model-based design of experiments (known as MBDoE) utilizes the information of an underlying mathematical model describing the system of interest. A common method to predict the accuracy of the parameter estimates uses the Fisher information matrix to approximate the 90% confidence intervals of the estimates. However, for highly non-linear models, this method might lead to wrong conclusions. In such cases, Monte Carlo sampling gives a more accurate insight into the parameter's estimate probability distribution and should be exploited to assess the reliability of the approximations made through the Fisher information matrix. We first introduce the model-based optimal experimental design for parameter estimation including parameter identification and validation by means of a simple non-linear Michaelis-Menten kinetic and show why Monte Carlo simulations give a more accurate depiction of the parameter uncertainty. Secondly, we propose a very robust and simple method to find optimal experimental designs using Monte Carlo simulations. Although computational expensive, the method is easy to implement and parallelize. This article focuses on practical examples of bioprocess engineering but is generally applicable in other fields

Accelerated Bioprocess Development of Endopolygalacturonase-Production with Saccharomyces cerevisiae Using Multivariate Prediction in a 48 Mini-Bioreactor Automated Platform

Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations are a promising alternative to accelerate and optimize bioprocess development allowing for sophisticated cultivation experiments in high throughput. These include fed-batch and continuous cultivations with multiple options of process control and sample analysis which deliver valuable screening tools for industrial production. However, the model-based methods needed to operate these robotic facilities efficiently considering the complexity of biological processes are missing. We present an automated experiment facility that integrates online data handling, visualization and treatment using multivariate analysis approaches to design and operate dynamical experimental campaigns in up to 48 mini-bioreactors (8–12 mL) in parallel. In this study, the characterization of Saccharomyces cerevisiae AH22 secreting recombinant endopolygalacturonase is performed, running and comparing 16 experimental conditions in triplicate. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable to different strains and cultivation strategies, and suitable for automatized process development reducing the experimental times and costs