Enzyme-Mediated Exponential Glucose Release: A Model-Based Strategy for Continuous Defined Fed-Batch in Small-Scale Cultivations

Miniaturized cultivation systems offer the potential to enhance experimental throughput in bioprocess development. However, they usually lack the miniaturized pumps necessary for fed-batch mode, which is commonly employed in industrial bioprocesses. An alternative are enzyme-mediated glucose release systems from starch-derived polymers, facilitating continuous glucose supply. Nevertheless, while the glucose release, and thus the feed rate, is controlled by the enzyme concentration, it also strongly depends on the type of starch derivative, and the culture conditions as well as pH and temperature. So far it was not possible to implement controlled feeding strategies (e.g., exponential feeding). In this context, we propose a model-based approach to achieve precise control over enzyme-mediated glucose release in cultivations. To this aim, an existing mathematical model was integrated into a computational framework to calculate setpoints for enzyme additions. We demonstrate the ability of the tool to maintain different pre-defined exponential growth rates during Escherichia coli cultivations in parallel mini-bioreactors integrated into a robotic facility. Although in this case study, the intermittent additions of enzyme and dextrin were performed by a liquid handler, the approach is adaptable to manual applications. Thus, we present a straightforward and robust approach for implementing defined continuous fed-batch processes in small-scale systems, where continuous feeding was only possible with low accuracy or high technical efforts until now.

Nonlinear state estimation as tool for online monitoring and adaptive feed in high throughput cultivations

Robotic facilities that can perform advanced cultivations (e.g., fed-batch or continuous) in high throughput have drastically increased the speed and reliability of the bioprocess development pipeline. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation for the implementation of feedback control methods in miniaturized bioreactor systems, where observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma-Point Kalman Filter in a high throughput platform with 24 parallel experiments at the mL-scale to demonstrate its viability and added value in high throughput experiments. The filter exploits the information generated by the ammonia-based pH control to enable the continuous estimation of the biomass concentration, a critical state to monitor the specific rates of production and consumption in the process. The objective in the selected case study is to ensure that the selected specific substrate consumption rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of an antibody fragment.

Model-Based Characterization of E. coli Strains with Impaired Glucose Uptake

The bacterium Escherichia coli is a widely used organism in biotechnology. For high space-time yields, glucose-limited fed-batch technology is the industry standard; this is because an overflow metabolism of acetate occurs at high glucose concentrations. As an interesting alternative, various strains with limited glucose uptake have been developed. However, these have not yet been characterized under process conditions. To demonstrate the efficiency of our previously developed high-throughput robotic platform, in the present work, we characterized three different exemplary E. coli knockout (KO) strains with limited glucose uptake capacities at three different scales (microtiter plates, 10 mL bioreactor system and 100 mL bioreactor system) under excess glucose conditions with different initial glucose concentrations. The extensive measurements of growth behavior, substrate consumption, respiration, and overflow metabolism were then used to determine the appropriate growth parameters using a mechanistic mathematical model, which allowed for a comprehensive comparative analysis of the strains. The analysis was performed coherently with these different reactor configurations and the results could be successfully transferred from one platform to another. Single and double KO mutants showed reduced specific rates for substrate uptake q_Smax and acetate production q_Apmax; meanwhile, higher glucose concentrations had adverse effects on the biomass yield coefficient Y_XSem. Additional parameters compared to previous studies for the oxygen uptake rate and carbon dioxide production rate indicated differences in the specific oxygen uptake rate q_Omax. This study is an example of how automated robotic equipment, together with mathematical model-based approaches, can be successfully used to characterize strains and obtain comprehensive information more quickly, with a trade-off between throughput and analytical capacity.

High-Throughput Expression of Inclusion Bodies on an Automated Platform

In bioprocesses, which target the production of recombinant proteins as inclusion bodies, the upstream process has a decisive influence on the downstream operations, especially regarding cell disruption, inclusion body purity and composition, and refolding yield. Therefore, optimization of the processes in fed-batch mode is a major issue, and screening for strains and process conditions are performed in highly labor, time and cost intensive shake flasks or multiwell plates. Thus, high-throughput experiments performed similar to the industrial operating conditions offer a possibility to develop efficient and robust upstream processes. We present here an automated platform for Escherichia coli fed-batch cultivations in parallelized minibioreactors. The platform allows execution of experiments under multiple conditions while allowing for real-time monitoring of critical process parameters and a controlled fermentation environment. By this, the main factors that affect yields and quality of inclusion bodies can be investigated, speeding up the development process significantly.

Deep reinforcement learning for optimal experimental design in biology

The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.

Promoting Sustainability through Next-Generation Biologics Drug Development

The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.

Automated Bioprocess Feedback Operation in a High-Throughput Facility via the Integration of a Mobile Robotic Lab Assistant

The development of biotechnological processes is challenging due to the diversity of process parameters. For efficient upstream development, parallel cultivation systems have proven to reduce costs and associated timelines successfully while offering excellent process control. However, the degree of automation of such small-scale systems is comparatively low, and necessary sample analysis requires manual steps. Although the subsequent analysis can be performed in a high-throughput manner, the integration of analytical devices remains challenging, especially when cultivation and analysis laboratories are spatially separated. Mobile robots offer a potential solution, but their implementation in research laboratories is not widely adopted. Our approach demonstrates the integration of a small-scale cultivation system into a liquid handling station for an automated cultivation and sample procedure. The samples are transported via a mobile robotic lab assistant and subsequently analyzed by a high-throughput analyzer. The process data are stored in a centralized database. The mobile robotic workflow guarantees a flexible solution for device integration and facilitates automation. Restrictions regarding spatial separation of devices are circumvented, enabling a modular platform throughout different laboratories. The presented cultivation platform is evaluated on the basis of industrially relevant E. coli BW25113 high cell density fed-batch cultivation. The necessary magnesium addition for reaching high cell densities in mineral salt medium is automated via a feedback operation loop between the analysis station located in the adjacent room and the cultivation system. The modular design demonstrates new opportunities for advanced control options and the suitability of the platform for accelerating bioprocess development. This study lays the foundation for a fully integrated facility, where the physical connection of laboratory equipment is achieved through the successful use of a mobile robotic lab assistant, and different cultivation scales can be coupled through the common data infrastructure.

bletl - A Python package for integrating BioLector microcultivation devices in the Design-Build-Test-Learn cycle

Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rateμ ⃗ t based on unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switch-points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

Recent advances in fed-batch microscale bioreactor design

Advanced fed-batch microbioreactors mitigate scale up risks and more closely mimic industrial cultivation practices. Recently, high throughput microscale feeding strategies have been developed which improve the accessibility of microscale fed-batch cultivation irrespective of experimental budget. This review explores such technologies and their role in accelerating bioprocess development. Diffusion- and enzyme-controlled feeding achieve a continuous supply of substrate while being simple and affordable. More complex feed profiles and greater process control require additional hardware. Automated liquid handling robots may be programmed to predefined feed profiles and have the sensitivity to respond to deviations in process parameters. Microfluidic technologies have been shown to facilitate both continuous and precise feeding. Holistic approaches, which integrate automated high-throughput fed-batch cultivation with strategic design of experiments and model-based optimisation, dramatically enhance process understanding whilst minimising experimental burden. The incorporation of real-time data for online optimisation of feed conditions can further refine screening. Although the technologies discussed in this review hold promise for efficient, low-risk bioprocess development, the expense and complexity of automated cultivation platforms limit their widespread application. Future attention should be directed towards the development of open-source software and reducing the exclusivity of hardware.

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model-driven process analysis

Given its geometric similarity to large-scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom-made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale.

Model-based workflow for scale-up of process strategies developed in miniaturized bioreactor systems

Miniaturized bioreactor (MBR) systems are routinely used in the development of mammalian cell culture processes. However, scale-up of process strategies obtained in MBR- to larger scale is challenging due to mainly non-holistic scale-up approaches. In this study, a model-based workflow is introduced to quantify differences in the process dynamics between bioreactor scales and thus enable a more knowledge-driven scale-up. The workflow is applied to two case studies with antibody-producing Chinese hamster ovary cell lines. With the workflow, model parameter distributions are estimated first under consideration of experimental variability for different scales. Second, the obtained individual model parameter distributions are tested for statistical differences. In case of significant differences, model parametric distributions are transferred between the scales. In case study I, a fed-batch process in a microtiter plate (4 ml working volume) and lab-scale bioreactor (3750 ml working volume) was mathematically modeled and evaluated. No significant differences were identified for model parameter distributions reflecting process dynamics. Therefore, the microtiter plate can be applied as scale-down tool for the lab-scale bioreactor. In case study II, a fed-batch process in a 24-Deep-Well-Plate (2 ml working volume) and shake flask (40 ml working volume) with two feed media was investigated. Model parameter distributions showed significant differences. Thus, process strategies were mathematically transferred, and model predictions were simulated for a new shake flask culture setup and confirmed in validation experiments. Overall, the workflow enables a knowledge-driven evaluation of scale-up for a more efficient bioprocess design and optimization.

Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down

Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.

Usage of Digital Twins Along a Typical Process Development Cycle

Digital methods for process design, monitoring, and control can convert classical trial-and-error bioprocess development to a quantitative engineering approach. By interconnecting hardware, software, data, and humans currently untapped process optimization potential can be accessed. The key component within such a framework is a digital twin interacting with its physical process counterpart. In this chapter, we show how digital twin guided process development can be applied on an exemplary microbial cultivation process. The usage of digital twins is described along a typical process development cycle, ranging from early strain characterization to real-time control applications. Along an illustrative case study on microbial upstream bioprocessing, we emphasize that digital twins can integrate entire process development cycles if the digital twin itself and the underlying models are continuously adapted to newly available data. Therefore, the digital twin can be regarded as a powerful knowledge management tool and a decision support system for efficient process development. Its full potential can be deployed in a real-time environment where targeted control actions can further improve process performance.

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.

Screening of a genome-reduced Corynebacterium glutamicum strain library for improved heterologous cutinase secretion

The construction of microbial platform organisms by means of genome reduction is an ongoing topic in biotechnology. In this study, we investigated whether the deletion of single or multiple gene clusters has a positive effect on the secretion of cutinase from Fusarium solani pisi in the industrial workhorse Corynebacterium glutamicum. A total of 22 genome-reduced strain variants were compared applying two Sec signal peptides from Bacillus subtilis. High-throughput phenotyping using robotics-integrated microbioreactor technology with automated harvesting revealed distinct cutinase secretion performance for a specific combination of signal peptide and genomic deletions. The biomass-specific cutinase yield for strain GRS41_51_NprE was increased by ~ 200%, although the growth rate was reduced by ~ 60%. Importantly, the causative deletions of genomic clusters cg2801-cg2828 and rrnC-cg3298 could not have been inferred a priori. Strikingly, bioreactor fed-batch cultivations at controlled growth rates resulted in a complete reversal of the screening results, with the cutinase yield for strain GRS41_51_NprE dropping by ~ 25% compared to the reference strain. Thus, the choice of bioprocess conditions may turn a 'high-performance' strain from batch screening into a 'low-performance' strain in fed-batch cultivation. In conclusion, future studies are needed in order to understand metabolic adaptations of C. glutamicum to both genomic deletions and different bioprocess conditions.

NMPC-Based Workflow for Simultaneous Process and Model Development Applied to a Fed-Batch Process for Recombinant C. glutamicum

For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. By using the NMPC algorithm, the process is designed with respect to a target function (product yield, biomass concentration) with a drastically decreased number of experiments. A workflow for the usage of the NMPC algorithm as a process development tool is outlined. The NMPC algorithm is capable of improving various process states, such as product yield and biomass concentration. It uses on-line and at-line data and controls and optimizes the process by model-based process extrapolation. In this study, the algorithm is applied to a Corynebacterium glutamicum process. In conclusion, the potency of the NMPC algorithm as a powerful tool for process development is demonstrated. In particular, the benefits of the system regarding the characterization and optimization of a fed-batch process are outlined. With the NMPC algorithm, process development can be run simultaneously to strain development, resulting in a shortened time to market for novel products.

Adaptive Monitoring of Biotechnological Processes Kinetics

In this paper, an approach for the monitoring of biotechnological process kinetics is proposed. The kinetics of each process state variable is presented as a function of two time-varying unknown parameters. For their estimation, a general software sensor is derived with on-line measurements as inputs that are accessible in practice. The stability analysis with a different number of inputs shows that stability can be guaranteed for fourth- and fifth-order software sensors only. As a case study, the monitoring of the kinetics of processes carried out in stirred tank reactors is investigated. A new tuning procedure is derived that results in a choice of only one design parameter. The effectiveness of the proposed procedure is demonstrated with experimental data from Bacillus subtilis fed-batch cultivations.

Decision Making and Risk Management in Biopharmaceutical Engineering-Opportunities in the Age of Covid-19 and Digitalization

In 2020, the Covid-19 pandemic resulted in a worldwide challenge without an evident solution. Many persons and authorities involved befriended the value of available data and established expertise to make decisions under time pressure. This omnipresent example is used to illustrate the decision-making procedure in biopharmaceutical manufacturing. This commentary addresses important challenges and opportunities to support risk management in biomanufacturing through a process-centered digitalization approach combining two vital worlds-formalized engineering fundamentals and data empowerment through customized machine learning. With many enabling technologies already available and first success stories reported, it will depend on the interaction of different groups of stakeholders how and when the huge potential of the discussed technologies will be broadly and systematically realized.

Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa

In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO2 concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization.

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

The Process Analytical Technology initiative and Quality by Design paradigm have led to changes in the guidelines and views of how to develop drug manufacturing processes. On this occasion the concept of the design space, which describes the impact of process parameters and material attributes on the attributes of the product, was introduced in the ICH Q8 guideline. The way the design space is defined and can be presented for regulatory approval seems to be left to the applicants, among who at least a consensus on how to characterize the design space seems to have evolved. The large majority of design spaces described in publications seem to follow a "static" statistical experimentation and modeling approach. Given that temporal deviations in the process parameters (i.e., moving within the design space) are of a dynamic nature, static approaches might not suffice for the consideration of the implications of variations in the values of the process parameters. In this paper, different forms of design space representations are discussed and the current consensus is challenged, which in turn, establishes the need for a dynamic representation and characterization of the design space. Subsequently, selected approaches for a dynamic representation, characterization and validation which are proposed in the literature are discussed, also showcasing the opportunity to integrate the activities of process characterization, process monitoring and process control strategy development.

Monitoring Parallel Robotic Cultivations with Online Multivariate Analysis

In conditional microbial screening, a limited number of candidate strains are tested at different conditions searching for the optimal operation strategy in production (e.g., temperature and pH shifts, media composition as well as feeding and induction strategies). To achieve this, cultivation volumes of >10 mL and advanced control schemes are required to allow appropriate sampling and analyses. Operations become even more complex when the analytical methods are integrated into the robot facility. Among other multivariate data analysis methods, principal component analysis (PCA) techniques have especially gained popularity in high throughput screening. However, an important issue specific to high throughput bioprocess development is the lack of so-called golden batches that could be used as a basis for multivariate analysis. In this study, we establish and present a program to monitor dynamic parallel cultivations in a high throughput facility. PCA was used for process monitoring and automated fault detection of 24 parallel running experiments using recombinant E. coli cells expressing three different fluorescence proteins as the model organism. This approach allowed for capturing events like stirrer failures and blockage of the aeration system and provided a good signal to noise ratio. The developed application can be easily integrated in existing data- and device-infrastructures, allowing automated and remote monitoring of parallel bioreactor systems.

FeedER: a feedback-regulated enzyme-based slow-release system for fed-batch cultivation in microtiter plates

With the advent of modern genetic engineering methods, microcultivation systems have become increasingly important tools for accelerated strain phenotyping and bioprocess engineering. While these systems offer sophisticated capabilities to screen batch processes, they lack the ability to realize fed-batch processes, which are used more frequently in industrial bioprocessing. In this study, a novel approach to realize a feedback-regulated enzyme-based slow-release system (FeedER), allowing exponential fed-batch for microscale cultivations, was realized by extending our existing Mini Pilot Plant technology with a customized process control system. By continuously comparing the experimental growth rates with predefined set points, the automated dosage of Amyloglucosidase enzyme for the cleavage of dextrin polymers into d-glucose monomers is triggered. As a prerequisite for stable fed-batch operation, a constant pH is maintained by automated addition of ammonium hydroxide. We show the successful application of FeedER to study fed-batch growth of different industrial model organisms including Corynebacterium glutamicum, Pichia pastoris, and Escherichia coli. Moreover, the comparative analysis of a C. glutamicum GFP producer strain, cultivated under microscale batch and fed-batch conditions, revealed two times higher product yields under slow growing fed-batch operation. In summary, FeedER enables to run 48 parallel fed-batch experiments in an automated and miniaturized manner, and thereby accelerates industrial bioprocess development at the screening stage.

Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control

During process development, the experimental search space is defined by the number of experiments that can be performed in specific time frames but also by its sophistication (e.g., inputs, sensors, sampling frequency, analytics). High-throughput liquid-handling stations can perform a large number of automated experiments in parallel. Nevertheless, the experimental data sets that are obtained are not always relevant for development of industrial bioprocesses, leading to a high rate of failure during scale-up. We present an automated mini bioreactor platform that enables parallel cultivations in the milliliter scale with online monitoring and control, well-controlled conditions, and advanced feeding strategies similar to industrial processes. The combination of two liquid handlers allows both automated mini bioreactor operation and at-line analysis in parallel. A central database enables end-to-end data exchange and fully integrated device and process control. A model-based operation algorithm allows for the accurate performance of complex cultivations for scale-down studies and strain characterization via optimal experimental redesign, significantly increasing the reliability and transferability of data throughout process development. The platform meets the tradeoff between experimental throughput and process control and monitoring comparable to laboratory-scale bioreactors.

Implementation of a Fully Automated Microbial Cultivation Platform for Strain and Process Screening

Advances in molecular biotechnology have resulted in the generation of numerous potential production strains. Because every strain can be screened under various process conditions, the number of potential cultivations is multiplied. Exploiting this potential without increasing the associated timelines requires a cultivation platform that offers increased throughput and flexibility to perform various bioprocess screening protocols. Currently, there is no commercially available fully automated cultivation platform that can operate multiple microbial fed-batch processes, including at-line sampling, deep freezer off-line sample storage, and complete data handling. To enable scalable high-throughput early-stage microbial bioprocess development, a commercially available microbioreactor system and a laboratory robot are combined to develop a fully automated cultivation platform. By making numerous modifications, as well as supplementation with custom-built hardware and software, fully automated milliliter-scale microbial fed-batch cultivation, sample handling, and data storage are realized. The initial results of cultivations with two different expression systems and three different process conditions are compared using 5 L scale benchmark cultivations, which provide identical rankings of expression systems and process conditions. Thus, fully automated high-throughput cultivation, including automated centralized data storage to significantly accelerate the identification of the optimal expression systems and process conditions, offers the potential for automated early-stage bioprocess development.

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.

An autonomous microreactor platform for the rapid identification of kinetic models

Rapid estimation of kinetic parameters with high precision is facilitated by automation combined with online Model-Based Design of Experiments.

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.

Less Sacrifice, More Insight: Repeated Low-Volume Sampling of Microbioreactor Cultivations Enables Accelerated Deep Phenotyping of Microbial Strain Libraries

With modern genetic engineering tools, high number of potentially improved production strains can be created in a short time. This results in a bottleneck in the succeeding step of bioprocess development, which can be handled by accelerating quantitative microbial phenotyping. Miniaturization and automation are key technologies to achieve this goal. In this study, a novel workflow for repeated low-volume sampling of BioLector-based cultivation setups is presented. Six samples of 20 μL each can be taken automatically from shaken 48-well microtiter plates without disturbing cell population growth. The volume is sufficient for quantification of substrate and product concentrations by spectrophotometric-based enzyme assays. From transient concentration data and replicate cultures, valid performance indicators (titers, rates, yields) are determined through process modeling and random error propagation analysis. Practical relevance of the workflow is demonstrated with a set of five genome-reduced Corynebacterium glutamicum strains that are engineered for Sec-mediated heterologous cutinase secretion. Quantitative phenotyping of this strain library led to the identification of a strain with a 1.6-fold increase in cutinase yield. The prophage-free strain carries combinatorial deletions of three gene clusters (Δ3102-3111, Δ3263-3301, and Δ3324-3345) of which the last two likely contain novel target genes to foster rational engineering of heterologous cutinase secretion in C. glutamicum.

Automated Cell Treatment for Competence and Transformation of Escherichia coli in a High-Throughput Quasi-Turbidostat Using Microtiter Plates

Metabolic engineering and genome editing strategies often lead to large strain libraries of a bacterial host. Nevertheless, the generation of competent cells is the basis for transformation and subsequent screening of these strains. While preparation of competent cells is a standard procedure in flask cultivations, parallelization becomes a challenging task when working with larger libraries and liquid handling stations as transformation efficiency depends on a distinct physiological state of the cells. We present a robust method for the preparation of competent cells and their transformation. The strength of the method is that all cells on the plate can be maintained at a high growth rate until all cultures have reached a defined cell density regardless of growth rate and lag phase variabilities. This allows sufficient transformation in automated high throughput facilities and solves important scheduling issues in wet-lab library screenings. We address the problem of different growth rates, lag phases, and initial cell densities inspired by the characteristics of continuous cultures. The method functions on a fully automated liquid handling platform including all steps from the inoculation of the liquid cultures to plating and incubation on agar plates. The key advantage of the developed method is that it enables cell harvest in 96 well plates at a predefined time by keeping fast growing cells in the exponential phase as in turbidostat cultivations. This is done by a periodic monitoring of cell growth and a controlled dilution specific for each well. With the described methodology, we were able to transform different strains in parallel. The transformants produced can be picked and used in further automated screening experiments. This method offers the possibility to transform any combination of strain- and plasmid library in an automated high-throughput system, overcoming an important bottleneck in the high-throughput screening and the overall chain of bioprocess development.

Microbioreactor Systems for Accelerated Bioprocess Development

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess engineering tools. They provide a unique solution to combine higher experimental throughput with extensive bioprocess monitoring and control, which is indispensable to develop economically and ecologically competitive bioproduction processes. MBR systems are based either on down-scaled stirred tank reactors or on advanced shaken microtiter plate cultivation devices. Importantly, MBR systems make use of optical measurements for non-invasive, online monitoring of important process variables like biomass concentration, dissolved oxygen, pH, and fluorescence. The application range of MBR systems can be further increased by integration into liquid handling robots, enabling automatization and, thus standardization, of various handling and operation procedures. Finally, the tight integration of quantitative strain phenotyping with bioprocess development under industrially relevant conditions greatly increases the probability of finding the right combination of producer strain and bioprocess control strategy. This review will discuss the current state of the art in the field of MBR systems and we can readily conclude that their importance for industrial biotechnology will further increase in the near future.

Corynebacterium glutamicum Chassis C1*: Building and Testing a Novel Platform Host for Synthetic Biology and Industrial Biotechnology

Targeted top-down strategies for genome reduction are considered to have a high potential for providing robust basic strains for synthetic biology and industrial biotechnology. Recently, we created a library of 26 genome-reduced strains of Corynebacterium glutamicum carrying broad deletions in single gene clusters and showing wild-type-like biological fitness. Here, we proceeded with combinatorial deletions of these irrelevant gene clusters in two parallel orders, and the resulting library of 28 strains was characterized under various environmental conditions. The final chassis strain C1* carries a genome reduction of 13.4% (412 deleted genes) and shows wild-type-like growth behavior in defined medium with d-glucose as carbon and energy source. Moreover, C1* proves to be robust against several stresses (including oxygen limitation) and shows long-term growth stability under defined and complex medium conditions. In addition to providing a novel prokaryotic chassis strain, our results comprise a large strain library and a revised genome annotation list, which will be valuable sources for future systemic studies of C. glutamicum.