Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure

Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher-order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher-order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven-member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three-member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher-order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher-order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond-pairwise combinations.

High-throughput process development from gene cloning to protein production

In the post-genomic era, the demand for faster and more efficient protein production has increased, both in public laboratories and industry. In addition, with the expansion of protein sequences in databases, the range of possible enzymes of interest for a given application is also increasing. Faced with peer competition, budgetary, and time constraints, companies and laboratories must find ways to develop a robust manufacturing process for recombinant protein production. In this review, we explore high-throughput technologies for recombinant protein expression and present a holistic high-throughput process development strategy that spans from genes to proteins. We discuss the challenges that come with this task, the limitations of previous studies, and future research directions.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review

By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.

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.

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

Automation assisted anaerobic phenotyping for metabolic engineering

BACKGROUND

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.

RESULTS

In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale.

CONCLUSIONS

Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

Microbioreactor (micro-Matrix) potential in aerobic and anaerobic conditions with different industrially relevant microbial strains

Microscale fermentation systems are important high throughput tools in clone selection, and bioprocess set up and optimization, since they provide several parallel experiments in controlled conditions of pH, temperature, agitation, and gas flow rate. In this work we evaluated the performance of biotechnologically relevant strains with different respiratory requirements in the micro‐Matrix microbioreactor. In particular Escherichia coli K4 requires well aerated fermentation conditions to improve its native production of chondroitin‐like capsular polysaccharide, a biomedically attractive polymer. Results from batch and fed‐batch experiments demonstrated high reproducibility with those obtained on 2 L reactors, although highlighting a pronounced volume loss for longer‐term experiments. Basfia succiniciproducens and Actinobacillus succinogenes need CO₂ addition for the production of succinic acid, a building block with several industrial applications. Different CO₂ supply modes were tested for the two strains in 24 h batch experiments and results well compared with those obtained on lab‐scale bioreactors. Overall, it was demonstrated that the micro‐Matrix is a useful scale‐down tool that is suitable for growing metabolically different strains in simple batch process, however, a series of issues should still be addressed in order to fully exploit its potential.

Parallelized microscale fed-batch cultivation in online-monitored microtiter plates: implications of media composition and feed strategies for process design and performance

Limited throughput represents a substantial drawback during bioprocess development. In recent years, several commercial microbioreactor systems have emerged featuring parallelized experimentation with optical monitoring. However, many devices remain limited to batch mode and do not represent the fed-batch strategy typically applied on an industrial scale. A workflow for 32-fold parallelized microscale cultivation of protein secreting Corynebacterium glutamicum in microtiter plates incorporating online monitoring, pH control and feeding was developed and validated. Critical interference of the essential media component protocatechuic acid with pH measurement was revealed, but was effectively resolved by 80% concentration reduction without affecting biological performance. Microfluidic pH control and feeding (pulsed, constant and exponential) were successfully implemented: Whereas pH control improved performance only slightly, feeding revealed a much higher optimization potential. Exponential feeding with µ = 0.1 h-1 resulted in the highest product titers. In contrast, other performance indicators such as biomass-specific or volumetric productivity resulted in different optimal feeding regimes.

A FRET-based biosensor for the quantification of glucose in culture supernatants of mL scale microbial cultivations

BACKGROUND

In most microbial cultivations D-glucose is the main carbon and energy source. However, quantification of D-glucose especially in small scale is still challenging. Therefore, we developed a FRET-based glucose biosensor, which can be applied in microbioreactor-based cultivations. This sensor consists of a glucose binding protein sandwiched between two fluorescent proteins, constituting a FRET pair. Upon D-glucose binding the sensor undergoes a conformational change which is translated into a FRET-ratio change.

RESULTS

The selected sensor shows an apparent Kd below 1.5 mM D-glucose and a very high sensitivity of up to 70% FRET-ratio change between the unbound and the glucose-saturated state. The soluble sensor was successfully applied online to monitor the glucose concentration in an Escherichia coli culture. Additionally, this sensor was utilized in an at-line process for a Corynebacterium glutamicum culture as an example for a process with cell-specific background (e.g. autofluorescence) and medium-induced quenching. Immobilization of the sensor via HaloTag® enabled purification and covalent immobilization in one step and increased the stability during application, significantly.

CONCLUSION

A FRET-based glucose sensor was used to quantify D-glucose consumption in microtiter plate based cultivations. To the best of our knowledge, this is the first method reported for online quantification of D-glucose in microtiter plate based cultivations. In comparison to D-glucose analysis via an enzymatic assay and HPLC, the sensor performed equally well, but enabled much faster measurements, which allowed to speed up microbial strain development significantly.

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.

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.

High throughput automated microbial bioreactor system used for clone selection and rapid scale-down process optimization

High throughput automated fermentation systems have become a useful tool in early bioprocess development. In this study, we investigated a 24 x 15 mL single use microbioreactor system, ambr 15f, designed for microbial culture. We compared the fed‐batch growth and production capabilities of this system for two Escherichia coli strains, BL21 (DE3) and MC4100, and two industrially relevant molecules, hGH and scFv. In addition, different carbon sources were tested using bolus, linear or exponential feeding strategies, showing the capacity of the ambr 15f system to handle automated feeding. We used power per unit volume (P/V) as a scale criterion to compare the ambr 15f with 1 L stirred bioreactors which were previously scaled‐up to 20 L with a different biological system, thus showing a potential 1,300 fold scale comparability in terms of both growth and product yield. By exposing the cells grown in the ambr 15f system to a level of shear expected in an industrial centrifuge, we determined that the cells are as robust as those from a bench scale bioreactor. These results provide evidence that the ambr 15f system is an efficient high throughput microbial system that can be used for strain and molecule selection as well as rapid scale‐up. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:58–68, 2018

Rapid process synthesis supported by a unified modular software framework

Although known to be very powerful, the widespread application of model-based techniques is still significantly hampered in the area of bio-processes. Reasons for this situation can be found along the whole chain to set up and implement such approaches. In a time-consuming step, models are typically hand-crafted. Whether alternatives of better models exist to actually fulfill the final goals is undocumented, most often even unknown. In a next step, model-based process control methods are hand-coded in an error-prone procedure. For many of these methods given in the literature, only simulation studies are shown, leaving the interested reader with the unanswered question whether the implementation of a specific method in a real process is viable. As the potentially time-consuming implementation of such a method presents a risk for a rapid process development, promising candidates may be overlooked. To remediate this unsatisfactory situation, a combination of theoretical methods and information technology is proposed here. By an exemplarily realized software tool, it is shown how such an environment helps to promote model-based optimization, supervision, and control of bio-processes and allows for an inexpensive test of new ideas as well in real-life experiments. The contribution concentrates on an overview of a possible software architecture with respect to necessary methods and a meaningful information strategy, highlighting some of the more crucial building blocks. Experimental results exploiting parts of the proposed methods are given for a yeast strain synthesizing a product of industrial interest.

Cellulolytic RoboLector - towards an automated high-throughput screening platform for recombinant cellulase expression

Background

Cellulases are key player in the hydrolyzation of cellulose. Unfortunately, this reaction is slow and a bottleneck in the process chain from biomass to intermediates and biofuels due to low activities of the enzymes. To overcome this draw back, a lot of effort is put into the area of protein engineering, to modify these enzymes by directed evolution or rational design. Huge clone libraries are constructed and have to be screened for improved variants. High-throughput screening is the method of choice to tackle this experimental effort, but up to now only a few process steps are adapted to automated platforms and little attention has been turned to the reproducibility of clone rankings.

Results

In this study, an extended robotic platform is presented to conduct automated high-throughput screenings of clone libraries including preculture synchronization and biomass specific induction. Automated upstream, downstream and analytical process steps are described and evaluated using E. coli and K. lactis as model organisms. Conventional protocols for media preparation, cell lysis, Azo-CMC assay and PAHBAH assay are successfully adapted to automatable high-throughput protocols. Finally, a recombinant E. coli celA2 clone library was screened and a reliable clone ranking could be realized.

Conclusion

The RoboLector device is a suitable platform to perform all process steps of an automated high-throughput clone library screening for improved cellulases. On-line biomass growth measurement controlling liquid handling actions enables fair comparison of clone variants.

Electronic supplementary material

The online version of this article (doi:10.1186/s13036-016-0043-2) contains supplementary material, which is available to authorized users.

Online bioprocess data generation, analysis, and optimization for parallel fed-batch fermentations in milliliter scale

Bioprocess development, optimization, and control in mini-bioreactor systems require information about essential process parameters, high data densities, and the ability to dynamically change process conditions. We present an integration approach combining a parallel mini-bioreactor system integrated into a liquid handling station (LHS) with a second LHS for offline analytics. Non-invasive sensors measure pH and DO online. Offline samples are collected every 20 min and acetate, glucose, and OD₆₂₀ subsequently analyzed offline. All data are automatically collected, analyzed, formalized, and used for process control and optimization. Fed-batch conditions are realized via a slow enzymatic glucose release system. The integration approach was successfully used to apply an online experimental re-design method to eight Escherichia coli fed-batch cultivations. The method utilizes generated data to select the following experimental actions online in order to reach the optimization goal of estimating E. coli fed-batch model parameters with as high accuracy as possible. Optimal experimental designs were re-calculated online based on the experimental data and implemented by introducing pulses via the LHS to the running fermentations. The LHS control allows for various implementations of advanced control and optimization strategies in milliliter scale.

A scalable software framework for data integration in bioprocess development

Effectiveness in lab workflows-despite progresses made in automation and lab informatics-is often hindered by insufficient integration of devices and data. The iLAB software framework, a middleware connecting and integrating devices and data, provides a plugin architecture that can be adapted to individual lab environments. Integration of devices is preferably based on standardized data and communication protocols. In addition to device integration, process data and result data from different sources (e.g. readers) are converted to a standard format and administered by a powerful database for further processing. In this paper, the use of iLAB in a bioprocess development application is described. Process parameters and measured values of two high-throughput bioreactor systems (96-well plates and 10 mL reactor vessels) are collected in the iLAB database. Data from screening experiments and offline data are visualized, analyzed, and compared. A filter algorithm allows searching for matching parameters in experiments as well as the comparison of correlated datasets, independently from the used bioreactor system. The database model enables the consolidation of data, the transition from data to information, and a solid base for management decisions on enterprise level.

The fed-batch principle for the molecular biology lab: controlled nutrient diets in ready-made media improve production of recombinant proteins in Escherichia coli

While the nutrient limited fed-batch technology is the standard of the cultivation of microorganisms and production of heterologous proteins in industry, despite its advantages in view of metabolic control and high cell density growth, shaken batch cultures are still the standard for protein production and expression screening in molecular biology and biochemistry laboratories. This is due to the difficulty and expenses to apply a controlled continuous glucose feed to shaken cultures. New ready-made growth media, e.g. by biocatalytic release of glucose from a polymer, offer a simple solution for the application of the fed-batch principle in shaken plate and flask cultures. Their wider use has shown that the controlled diet not only provides a solution to obtain significantly higher cell yields, but also in many cases folding of the target protein is improved by the applied lower growth rates; i.e. final volumetric yields for the active protein can be a multiple of what is obtained in complex medium cultures. The combination of the conventional optimization approaches with new and easy applicable growth systems has revolutionized recombinant protein production in Escherichia coli in view of product yield, culture robustness as well as significantly increased cell densities. This technical development establishes the basis for successful miniaturization and parallelization which is now an important tool for synthetic biology and protein engineering approaches. This review provides an overview of the recent developments, results and applications of advanced growth systems which use a controlled glucose release as substrate supply.

Engineering microbial consortia for controllable outputs

Much research has been invested into engineering microorganisms to perform desired biotransformations; nonetheless, these efforts frequently fall short of expected results due to the unforeseen effects of biofeedback regulation and functional incompatibility. In nature, metabolic function is compartmentalized into diverse organisms assembled into robust consortia, in which the division of labor is thought to lead to increased community efficiency and productivity. Here we consider whether and how consortia can be designed to perform bioprocesses of interest beyond the metabolic flexibility limitations of a single organism. Advances in post-genomic analysis of microbial consortia and application of high-resolution global measurements now offer the promise of systems-level understanding of how microbial consortia adapt to changes in environmental variables and inputs of carbon and energy. We argue that, when combined with appropriate modeling frameworks, systems-level knowledge can markedly improve our ability to predict the fate and functioning of consortia. Here we articulate our collective perspective on the current and future state of microbial community engineering and control while placing specific emphasis on ecological principles that promote control over community function and emergent properties.

Bioprocess automation on a Mini Pilot Plant enables fast quantitative microbial phenotyping

BACKGROUND

The throughput of cultivation experiments in bioprocess development has drastically increased in recent years due to the availability of sophisticated microliter scale cultivation devices. However, as these devices still require time-consuming manual work, the bottleneck was merely shifted to media preparation, inoculation and finally the analyses of cultivation samples. A first step towards solving these issues was undertaken in our former study by embedding a BioLector in a robotic workstation. This workstation already allowed for the optimization of heterologous protein production processes, but remained limited when aiming for the characterization of small molecule producer strains. In this work, we extended our workstation to a versatile Mini Pilot Plant (MPP) by integrating further robotic workflows and microtiter plate assays that now enable a fast and accurate phenotyping of a broad range of microbial production hosts.

RESULTS

A fully automated harvest procedure was established, which repeatedly samples up to 48 wells from BioLector cultivations in response to individually defined trigger conditions. The samples are automatically clarified by centrifugation and finally frozen for subsequent analyses. Sensitive metabolite assays in 384-well plate scale were integrated on the MPP for the direct determination of substrate uptake (specifically D-glucose and D-xylose) and product formation (specifically amino acids). In a first application, we characterized a set of Corynebacterium glutamicum L-lysine producer strains and could rapidly identify a unique strain showing increased L-lysine titers, which was subsequently confirmed in lab-scale bioreactor experiments. In a second study, we analyzed the substrate uptake kinetics of a previously constructed D-xylose-converting C. glutamicum strain during cultivation on mixed carbon sources in a fully automated experiment.

CONCLUSIONS

The presented MPP is designed to face the challenges typically encountered during early-stage bioprocess development. Especially the bottleneck of sample analyses from fast and parallelized microtiter plate cultivations can be solved using cutting-edge robotic automation. As robotic workstations become increasingly attractive for biotechnological research, we expect our setup to become a template for future bioprocess development.