An Approach for the Estimation of Concentrations of Soluble Compounds in E. coli Bioprocesses.

Accurate estimations of the concentrations of soluble compounds are crucial for optimizing bioprocesses involving Escherichia coli (E. coli). This study proposes a hybrid model structure that leverages off-gas analysis data and physiological parameters, including the average biomass age and specific growth rate, to estimate soluble compounds such as acetate and glutamate in fed-batch cultivations We used a hybrid recurrent neural network to establish the relationships between these parameters. To enhance the precision of the estimates, the model incorporates ensemble averaging and information gain. Ensemble averaging combines varying model inputs, leading to more robust representations of the underlying dynamics in E. coli bioprocesses. Our hybrid model estimates acetates with 1% and 8% system precision using data from the first site and the second site at GSK plc, respectively. Using the data from the second site, the precision of the approach for other solutes was as fallows: isoleucine -8%, lactate and glutamate -9%, and a 13% error for glutamine., These results, demonstrate its practical potential.

High-level production of recombinant HBcAg virus-like particles in a mathematically modelled P. pastoris GS115 Mut+ bioreactor process under controlled residual methanol concentration.

Recombinant hepatitis B core antigen (HBcAg) molecules, produced in heterologous expression systems, self-assemble into highly homogenous and non-infectious virus-like particles (VLPs) that are under extensive research for biomedical applications. HBcAg production in the methylotrophic yeast P. pastoris has been well documented; however, productivity screening under various residual methanol levels has not been reported for bioreactor processes. HBcAg production under various excess methanol levels of 0.1, 1.0 and 2.0 g L-1 was investigated in this research. Results indicate that, under these particular conditions, the total process and specific protein yields of 876-1308 mg L-1 and 7.9-11.2 mg gDCW-1, respectively, were achieved after 67-75 h of cultivation. Produced HBcAg molecules were efficiently purified and the presence of highly immunogenic, correctly formed and homogenous HBcAg-VLPs with an estimated purity of 90% was confirmed by electron microscopy. The highest reported HBcAg yield of 1308 mg L-1 and 11.2 mg gDCW-1 was achieved under limiting residual methanol concentration, which is about 2.5 times higher than the next highest reported result. A PI-algorithm-based residual methanol concentration feed rate controller was employed to maintain a set residual methanol concentration. Finally, mathematical process models to characterise the vegetative, dead and total cell biomass (Xv, Xd and X), substrate (Glycerol and Methanol) concentration, reactor volume (V), and product (HBcAg) dynamics during cultivation, were identified. A rare attempt to model the residual methanol concentration during induction is also presented.

Kinetic and Stoichiometric Modeling-Based Analysis of Docosahexaenoic Acid (DHA) Production Potential by Crypthecodinium cohnii from Glycerol, Glucose and Ethanol.

Docosahexaenoic acid (DHA) is one of the most important long-chain polyunsaturated fatty acids (LC-PUFAs), with numerous health benefits. Crypthecodinium cohnii, a marine heterotrophic dinoflagellate, is successfully used for the industrial production of DHA because it can accumulate DHA at high concentrations within the cells. Glycerol is an interesting renewable substrate for DHA production since it is a by-product of biodiesel production and other industries, and is globally generated in large quantities. The DHA production potential from glycerol, ethanol and glucose is compared by combining fermentation experiments with the pathway-scale kinetic modeling and constraint-based stoichiometric modeling of C. cohnii metabolism. Glycerol has the slowest biomass growth rate among the tested substrates. This is partially compensated by the highest PUFAs fraction, where DHA is dominant. Mathematical modeling reveals that glycerol has the best experimentally observed carbon transformation rate into biomass, reaching the closest values to the theoretical upper limit. In addition to our observations, the published experimental evidence indicates that crude glycerol is readily consumed by C. cohnii, making glycerol an attractive substrate for DHA production.

Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions.

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O2/CO2, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut+ strain) to significant (for MutS strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut+ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O2 uptake-based (10%), CO2 production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.

Generic estimator of biomass concentration for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures based on cumulative oxygen consumption rate.

BACKGROUND: The focus of this study is online estimation of biomass concentration in fed-batch cultures. It describes a bioengineering software solution, which is explored for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures. The experimental investigation of both cultures presents experimental validation results since the start of the bioprocess, i.e. since the injection of inoculant solution into bioreactor. In total, four strains were analyzed, and 21 experiments were performed under varying bioprocess conditions, out of which 7 experiments were carried out with dosed substrate feeding. Development of the microorganisms' culture invariant generic estimator of biomass concentration was the main goal of this research. RESULTS: The results show that stoichiometric parameters provide acceptable knowledge on the state of biomass concentrations during the whole cultivation process, including the exponential growth phase of both E. coli and S. cerevisiae cultures. The cell culture stoichiometric parameters are estimated by a procedure based on the Luedeking/Piret-model and maximization of entropy. The main input signal of the approach is cumulative oxygen uptake rate at fed-batch cultivation processes. The developed noninvasive biomass estimation procedure was intentionally made to not depend on the selection of corresponding bioprocess/bioreactor parameters. CONCLUSIONS: The precision errors, since the bioprocess start, when inoculant was injected to a bioreactor, confirmed that the approach is relevant for online biomass state estimation. This included the lag and exponential growth phases for both E. coli and S. cerevisiae. The suggested estimation procedure is identical for both cultures. This approach improves the precision achieved by other authors without compromising the simplicity of the implementation. Moreover, the suggested approach is a candidate method to be the microorganisms' culture invariant approach. It does not depend on any numeric initial optimization conditions, it does not require any of bioreactor parameters. No numeric stability issues of convergence occurred during multiple performance tests. All this makes this approach a potential candidate for industrial tasks with adaptive feeding control or automatic inoculations when substrate feeding profile and bioreactor parameters are not provided.

Kinetic modeling of human induced pluripotent stem cell expansion in suspension culture.

To date, practical application of mathematical models for model-based design of stem cell expansion processes is limited. Nevertheless, the first attempts show vast potential of this approach for the improvement of expansion process performance. This article presents the developed dynamic kinetic model of the human induced pluripotent stem cell expansion process in suspension culture. The model predicts cell growth, consumption of glucose and production of lactic acid, as well as the average aggregate size. The latter process variable is of particular importance for achieving high cell density. By adding botulinum hemagglutinin, an E-cadherin inhibitor and subsequent aggregate break-up, one can significantly increase performance of cell expansion process. After defining the appropriate optimization criteria and additional modification of the model, the latter can be further applied for model-based optimization of the final cell concentration by calculating optimal aggregate break-up and glucose/glutamine feeding strategies.

From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time.

For historic reasons, industrial knowledge of reproducibility and restrictions imposed by regulations, open-loop feeding control approaches dominate in industrial fed-batch cultivation processes. In this study, a generic gray box biomass modeling procedure uses relative entropy as a key to approach the posterior similarly to how prior distribution approaches the posterior distribution by the multivariate path of Lagrange multipliers, for which a description of a nuisance time is introduced. The ultimate purpose of this study was to develop a numerical semi-global convex optimization procedure that is dedicated to the calculation of feeding rate time profiles during the fed-batch cultivation processes. The proposed numerical semi-global convex optimization of relative entropy is neither restricted to the gray box model nor to the bioengineering application. From the bioengineering application perspective, the proposed bioprocess design technique has benefits for both the regular feed-forward control and the advanced adaptive control systems, in which the model for biomass growth prediction is compulsory. After identification of the gray box model parameters, the options and alternatives in controllable industrial biotechnological processes are described. The main aim of this work is to achieve high reproducibility, controllability, and desired process performance. Glucose concentration measurements, which were used for the development of the model, become unnecessary for the development of the desired microbial cultivation process.

Current state and perspectives in modeling and control of human pluripotent stem cell expansion processes in stirred-tank bioreactors.

Implementation of model-based practices for process development, control, automation, standardization, and validation are important factors for therapeutic and industrial applications of human pluripotent stem cells. As robust cultivation strategies for pluripotent stem cell expansion and differentiation have yet to be determined, process development could be enhanced by application of mathematical models and advanced control systems to optimize growth conditions. Therefore, it is important to understand both the potential of possible applications and the apparent limitations of existing mathematical models to improve pluripotent stem cell cultivation technologies. In the present review, the authors focus on these issues as they apply to stem cell expansion processes. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:355-364, 2017.

Hybrid modeling for quality by design and PAT-benefits and challenges of applications in biopharmaceutical industry.

This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant scientific and engineering backgrounds. Hybrid modeling is viewed in its broader sense, namely as the integration of different knowledge sources in form of parametric and nonparametric models into a hybrid semi-parametric model, for instance the integration of fundamental and data-driven models. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided. The report concludes with a number of recommendations to facilitate further developments and a wider industrial application of this modeling approach. These recommendations are limited to further exploiting the benefits of this methodology within process analytical technology (PAT) applications in biopharmaceutical industry.