Eldorado Collection:
http://hdl.handle.net/2003/39256
2024-03-29T02:39:34ZCell‐free protein synthesis for the screening of novel azoreductases and their preferred electron donor
http://hdl.handle.net/2003/42325
Title: Cell‐free protein synthesis for the screening of novel azoreductases and their preferred electron donor
Authors: Rolf, Jascha; Ngo, Anna Christina Reyes; Lütz, Stephan; Tischler, Dirk; Rosenthal, Katrin
Abstract: Azoreductases are potent biocatalysts for the cleavage of azo bonds. Various gene sequences coding for potential azoreductases are available in databases, but many of their gene products are still uncharacterized. To avoid the laborious heterologous expression in a host organism, we developed a screening approach involving cell-free protein synthesis (CFPS) combined with a colorimetric activity assay, which allows the parallel screening of putative azoreductases in a short time. First, we evaluated different CFPS systems and optimized the synthesis conditions of a model azoreductase. With the findings obtained, 10 azoreductases, half of them undescribed so far, were screened for their ability to degrade the azo dye methyl red. All novel enzymes catalyzed the degradation of methyl red and can therefore be referred to as azoreductases. In addition, all enzymes degraded the more complex and bulkier azo dye Brilliant Black and four of them also showed the ability to reduce p-benzoquinone. NADH was the preferred electron donor for the most enzymes, although the synthetic nicotinamide co-substrate analogue 1-benzyl-1,4-dihydronicotinamide (BNAH) was also accepted by all active azoreductases. This screening approach allows accelerated identification of potential biocatalysts for various applications.2022-05-20T00:00:00ZAgar plate-based screening approach for the identification of enzyme-catalyzed oxidations
http://hdl.handle.net/2003/42268
Title: Agar plate-based screening approach for the identification of enzyme-catalyzed oxidations
Authors: Kinner, Alina; Lütz, Stephan; Rosenthal, Katrin
Abstract: Biocatalytic oxidation reactions are in high demand. Among the applied enzymes, the heme-thiolate enzyme subfamily of unspecific peroxygenases (UPOs), which relies on hydrogen peroxide as cosubstrate and oxidant, has generated great interest. Almost two decades after their first description, databases provide thousands of putative UPO sequences, but only a few enzymes have been characterized. To address this gap, efficient screening methods for the identification of novel UPOs are necessary. Here, a new screening strategy is presented based on solid cultivation of wild-type fungi. The identification of a promising candidate strain highlights the applicability of this approach.2022-09-29T00:00:00ZComparative life cycle assessment of chemical and biocatalytic 2’3’-cyclic GMP-AMP synthesis
http://hdl.handle.net/2003/42162
Title: Comparative life cycle assessment of chemical and biocatalytic 2’3’-cyclic GMP-AMP synthesis
Authors: Becker, Martin; Ziemińska-Stolarska, Aleksandra; Markowska, Dorota; Lütz, Stephan; Rosenthal, Katrin
Abstract: Life cycle assessments (LCAs) can provide insights into the environmental impact of production processes. In this study, a comparative LCA was performed for the synthesis of 2’3’-cyclic GMP-AMP (2’3’-cGAMP) in an early development stage. The cyclic dinucleotide (CDN) is of interest for pharmaceutical applications such as cancer immunotherapy. CDNs can be synthesized either by enzymes or chemical catalysis. It is not known which of the routes is more sustainable as both routes have their advantages and disadvantages, such as a poor yield for the chemical synthesis and low titers for the biocatalytic synthesis. The synthesis routes were compared for the production of 200 g 2’3’-cGAMP based on laboratory data to assess the environmental impacts. The biocatalytic synthesis turned out to be superior to the chemical synthesis in all considered categories by at least one magnitude, for example, a global warming potential of 3055.6 kg CO2 equiv. for the enzymatic route and 56454.0 kg CO2 equiv. for the chemical synthesis, which is 18 times higher. This study demonstrates the value of assessment at an early development stage, when the choice between different routes is still possible.2022-11-23T00:00:00ZEfficient approximations of model predictive control laws via deep learning
http://hdl.handle.net/2003/41842
Title: Efficient approximations of model predictive control laws via deep learning
Authors: Karg, Benjamin
Abstract: Model predictive control (MPC) has established itself as the standard method for the
control of complex nonlinear systems due to its ability to directly consider constraints
and uncertainties while optimizing a control objective. However, the application of
MPC requires repeatedly solving an optimal control problem online which can be computationally
prohibitive, especially for large systems, for systems with very high control
sampling rates and for the implementation on embedded hardware.
This thesis presents deep neural networks (DNNs) as a means of enabling the implementation
of MPC algorithms when computation power is limited. The expressive
capabilities of DNNs are leveraged to closely approximate the control law implicitly
defined by the MPC problem. For the online application only evaluating the DNN, an
explicit function consisting of simple arithmetic operations, is required. This results in
speed-ups of several orders of magnitude in comparison to solving the MPC problem
online.
Throughout the thesis, we shed light onto various aspects that enable and motivate
the usage of DNNs as safe approximate MPC laws. Approaches to modify a DNN after
an initial learning phase such that the closed-loop performance is improved are proposed.
Further, methods that enable the analysis of the closed-loop behavior to obtain
both deterministic and probabilistic guarantees on the online operation regarding
safety, performance and stability are presented.
The efficacy of the proposed approaches is investigated for a wide range of case
studies including a polymerization reactor of industrial complexity. The analysis shows
that the DNN controllers do not only outperform other approximate MPC approaches
in terms of control performance, memory footprint and evaluation times, but that DNN
controllers can even outperform the exact optimization-based MPCs when ideas from
reinforcement learning are used. Further it is shown that the DNN controllers can be
deployed on embedded hardware such as microcontrollers with small effort.; Modelprädiktive Regelung (kurz: MPC) hat sich als die Standard-Methode zur Regelung
von komplexen nichtlinearen System etabliert, da es erlaubt Beschränkungen
und Unsicherheiten direkt zu berücksichtigen und zeitgleich eine Zielfunktion zu optimieren.
Dafür muss jedoch wiederholt ein Optimierungsproblem online gelöst werden.
Dies verhindert die Anwendung von MPC, wenn die Hardware nicht die nötige Rechenleistung
aufbringt zur Berechnung einer Lösung innerhalb eines Zeitschritts.
In dieser Dissertation werden tiefe neuronale Netzwerke (kurz: DNNs) eingeführt
als eine Möglichkeit, um MPC-Algorithmen auf leistungsschwacher Hardware zu realisieren.
Dabei werden die repräsentativen Fähigkeiten von tiefen neuronalen Netzwerken
genutzt, um das Regelgesetz, das implizit vom MPC-Optimierungsproblem
definiert wird, zu approximieren. Der approximative DNN-Regler kann mehrere Grössenordnungen
schneller evaluiert werden als der optimierungs-basierte Regler, da
das DNN eine explizite Funktion bestehend aus simplen arithmetischen Operationen
darstellt.
Im Laufe der Dissertation werden verschiedenste Aspekte, die die Nutzung von
DNNs als effiziente approximative MPC-Regler ermöglichen und motivieren, vorgestellt.
Dazu gehören Methoden zur Modifizierung eines gelernten DNN-Reglers, sodass
die Performance optimiert wird, und Ansätze zur Analyse des geschlossenen
Regelkreises, die es ermöglichen sowohl probabilistische als auch deterministische
Garantien bezüglich Sicherheit, Performance und Stabilität zu erhalten.
Die Effektivität des vorgestellten Ansatzes wird für eine Vielzahl an Fallstudien untersucht,
unter anderem für einen Polymerisationsreaktor von industrieller Komplexität.
Die Untersuchungen zeigen, dass DNNs nicht nur andere approximative MPC
Methoden übertreffen bezüglich Regel-Performance, Speicherbedarf und Ausführzeiten,
sondern auch den ursprünglichen exakten MPC-Ansatz übertreffen können,
wenn Ideen aus dem Bereich des bestärkenden Lernens genutzt werden. Zusätzlich
wird gezeigt, dass die gelernten Regler mit wenig Aufwand auf eingebetteten Plattformen
wie Micro-Controllern implementiert werden können.2023-01-01T00:00:00ZProbabilistic performance validation of deep learning-based robust NMPC controllers
http://hdl.handle.net/2003/40818
Title: Probabilistic performance validation of deep learning-based robust NMPC controllers
Authors: Karg, Benjamin; Alamo, Teodoro; Lucia, Sergio
Abstract: Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low-cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system.2021-07-22T00:00:00Z