Adaptive optimal operation of a parallel robotic liquid handling station

Results are presented from the optimal operation of a fully automated robotic liquid handling station where parallel experiments are performed for calibrating a kinetic fermentation model. To increase the robustness against uncertainties and/or wrong assumptions about the parameter values, an iterative calibration and experiment design approach is adopted. Its implementation yields a stepwise reduction of parameter uncertainties together with an adaptive redesign of reactor feeding strategies whenever new measurement information is available. The case study considers the adaptive optimal design of 4 parallel fed-batch strategies implemented in 8 mini-bioreactors. Details are given on the size and complexity of the problem and the challenges related to calibration of over-parameterized models and scarce and non-informative measurement data. It is shown how methods for parameter identifiability analysis and numerical regularization can be used for monitoring the progress of the experimental campaigns in terms of generated information regarding parameters and selection of the best fitting parameter subset.BMBF, 02PJ1150, Verbundprojekt: Plattformtechnologien für automatisierte Bioprozessentwicklung (AutoBio); Teilprojekt: Automatisierte Bioprozessentwicklung am Beispiel von neuen Nukleosidphosphorylase

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

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

Modelling overflow metabolism in Escherichia coli by acetate cycling

A new set of mathematical equations describing overflow metabolism and acetate accumulation in E. coli cultivation is presented. The model is a significant improvement of already existing models in the literature, with modifications based on the more recent concept of acetate cycling in E. coli, as revealed by proteomic studies of overflow routes. This concept opens up new questions regarding the speed of response of the acetate production and its consumption mechanisms in E. coli. The model is formulated as a set of continuous differentiable equations, which significantly improves model tractability and facilitates the computation of dynamic sensitivities in all relevant stages of fermentation (batch, fed-batch, starvation). The model is fitted to data from a simple 2 L fed-batch cultivation of E. coli W3110 M, where twelve (12) out of the sixteen (16) parameters were exclusively identified with relative standard deviation less than 10%. The framework presented gives valuable insight into the acetate dilemma in industrial fermentation processes, and serves as a tool for the development, optimization and control of E. coli fermentation processes.EC/H2020/643056/EU/Rapid Bioprocess Development/Biorapi

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.BMBF, 031L0018A, ERASysApp2 - Verbundprojekt: LEANPROT - Entwicklung einer Systembiologie-Plattform für die Entwicklung von lean-proteome-Escherichia coli-Stämmen - Deutsches Teilprojekt

Handling nonlinearities and uncertainties of fed-batch cultivations with difference of convex functions tube MPC

Bioprocesses are often characterized by nonlinear and uncertain dynamics. This poses particular challenges in the context of model predictive control (MPC). Several approaches have been proposed to solve this problem, such as robust or stochastic MPC, but they can be computationally expensive when the system is nonlinear. Recent advances in optimal control theory have shown that concepts from convex optimization, tube-based MPC, and difference of convex functions (DC) enable stable and robust online process control. The approach is based on systematic DC decompositions of the dynamics and successive linearizations around feasible trajectories. By convexity, the linearization errors can be bounded tightly and treated as bounded disturbances in a robust tube-based MPC framework. However, finding the DC composition can be a difficult task. To overcome this problem, we used a neural network with special convex structure to learn the dynamics in DC form and express the uncertainty sets using simplices to maximize the product formation rate of a cultivation with uncertain substrate concentration in the feed. The results show that this is a promising approach for computationally tractable data-driven robust MPC of bioprocesses.Comment: Corrected typos in equatio

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.BMBF, 031L0018A, ERASysApp2 - Verbundprojekt: LEANPROT - Entwicklung einer Systembiologie-Plattform für die Entwicklung von lean-proteome-Escherichia coli-Stämmen - Deutsches Teilprojekt ADFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

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.BMBF, 01DD20002A, Verbundprojekt: Internationales Zukunftslabor für KI-gestützte Bioprozessentwicklung "KIWI-biolab"; Teilvorhaben: Koordination und Aufbau eines KI-Exzellenzzentrum

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

Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities

We present an integrated framework for the online optimal experimental re-design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro-kinetic differential equation model for Escherichia coli fed-batch processes after 6 h of cultivation. The system includes two fully-automated liquid handling robots; one containing eight mini-bioreactors and another used for automated at-line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re-designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re-computation of the optimal experiment are proven by a 50-fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610–619. © 2016 Wiley Periodicals, Inc.BMBF, 02PJ1150, Verbundprojekt: Plattformtechnologien für automatisierte Bioprozessentwicklung (AutoBio); Teilprojekt: Automatisierte Bioprozessentwicklung am Beispiel von neuen Nukleosidphosphorylase