Universität Stuttgart
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Item Open Access Gold nanoparticle-mediated DNA origami nanoarchitectures(2024) Peil, Andreas; Na Liu, Laura (Prof. Dr.)Since its origin in the 1980s, DNA (deoxyribonucleic acid) nanotechnology has established itself as a captivating nanofabrication technique with ever increasing impact that combines aspects from physics, chemistry, and biology to construct artificial nanosystems by means of molecular self assembly. Within the field of DNA nanotechnology, the DNA origami technique represents one of the most versatile fabrication tools to craft functional two-dimensional (2D) and three-dimensional (3D) nanostructures from the bottom up. These structures offer precisely tailored geometries along with programmable functions, featuring positional addressability with sub-5 nm resolution and exceptional spatiotemporal accuracy. This thesis discusses strategies to employ the DNA origami technique to assemble intricate hybrid nanosystems with synergistically integrated gold nanoparticles (AuNPs). The AuNPs take over different roles; they grant (i) structural and (ii) functional features and enable the (iii) optical monitoring of the systems. This approach allows the fabrication of nanostructures piece by piece to explore their structural and functional properties at the nanoscale in detail. The first publication covers different strategies for the hierarchical assembly of topological DNA origami structures using a AuNP-templated self-assembly approach. The assembly of [2], [3], and [4]catenanes with interconnecting AuNPs is elucidated. The AuNPs can be controllably released to disconnect the individual rings, leaving only the mechanical bond of the catenane chain. In the second publication, a dynamic AuNP-DNA origami gear system is presented that is designed to emulate a planetary gearset with precise spatiotemporal control over its rotation dynamics. The AuNPs serve three crucial tasks. They (i) structurally link the origami ring modules, (ii) mediate the rotation and (iii) enable the real time optical tracking of the rotation via fluorescence spectroscopy. The system enables tightly orchestrated and programmable bidirectional rotations. In the third publication, reconfigurable chiral metastructures comprising multiple plasmonic particles that are accurately positioned in a helical manner around a DNA origami template are discussed. The implementation of a DNA ‘swingarm strategy’ enables the simultaneous and efficient relocation of multiple closely spaced AuNPs over large distances to precisely tune the chiroptical response of the system. The presented publications illustrate the beneficial synergies between DNA origami systems and rationally integrated AuNPs with the aim to advance and expand the application spectrum of these hybrid nanosystems within their scientific disciplines.Item Open Access Molecular dynamics simulations for the study of interaction between non-canonical DNA structures and biochemically relevant co-solutes(2023) Oprzeska-Zingrebe, Ewa Anna; Smiatek, Jens (Priv.-Doz. Dr.)Non-canonical nucleic acid structures, such as DNA G-quadruplexes and i-Motifs, have been proved to play an important role in key biological processes, including gene expression, replication, regulation or telomere maintenance. The presence of G-quadruplexes in promoter regions of certain oncogenes turn them into a potential target for cancer therapies. Besides their biological implications, non-canonical DNA structures are present in genomes of various organisms, who adopt certain levels of co-solutes to protect their internal structures against the harsh environment. This study presents the research on the selected non-canonical DNA structures of particular biological relevance: G-quadruplex with only two tetrads, small DNA hairpin and ssDNA strand as well as canonical double helix. The atomistic molecular dynamics (MD) simulations have been applied to elucidate the structural, configuration and solvation properties of the analyzed structures in the presence of assorted co-solutes, composing the native cellular environment in nature: urea, ectoine and trimethylamine-N-oxide (TMAO). With the application of molecular theory of solutions, one determines and exemplifies the thermodynamic properties of investigated structures in various environments close to the physiological conditions present in living cells. This study uncovers the versatile nature of DNA interaction with diverse co-solutes and water, as well as the cross-interactions between the inorganic components of the biomolecular solution. The cellular mechanisms of DNA structural stabilization and destabilization are hereby described in terms of preferential binding and preferential exclusion, with particular emphasis on the properties of solvent structure within individual solvation shells. In this regards, this work presents a comprehensive study on the intracellular interactions involving nucleic acids, thus shedding light into their microscopic properties and opening the path for further biomedical research.Item Open Access Item Open Access The benefit of muscle-actuated systems : internal mechanics, optimization and learning(Stuttgart : Institut für Modellierung und Simulation Biomechanischer Systeme, Computational Biophysics and Biorobotics, 2023) Wochner, Isabell; Schmitt, Syn (Prof. Dr.)We are facing the challenge of an over-aging and overweight society. This leads to an increasing number of movement disorders and causes the loss of mobility and independence. To address this pressing issue, we need to develop new rehabilitation techniques and design innovative assistive devices. Achieving this goal requires a deeper understanding of the underlying mechanics that control muscle-actuated motion. However, despite extensive studies, the neural control of muscle-actuated motion remains poorly understood. While experiments are valuable and necessary tools to further our understanding, they are often limited by ethical and practical constraints. Therefore, simulating muscle-actuated motion has become increasingly important for testing hypotheses and bridge this knowledge gap. In silico, we can establish cause-effect relationships that are experimentally difficult or even impossible to measure. By changing morphological aspects of the underlying musculoskeletal structure or the neural control strategy itself, simulations are crucial in the quest for a deeper understanding of muscle-actuated motion. The insights gained from these simulations paves the way to develop new rehabilitation techniques, enhance pre-surgical planning, design better assistive devices and improve the performance of current robots. The primary objective of this dissertation is to study the intricate interplay between musculoskeletal dynamics, neural controller and the environment. To achieve this goal, a simulation framework has been developed as part of this thesis, enabling the modeling and control of muscle-actuated motion using both model-based and learning-based methods. By utilizing this framework, musculoskeletal models of the arm, head-neck complex and a simplified whole-body model are investigated in conjunction with various concepts of motor control. The main research questions of this thesis are therefore: 1. How does the neural control strategy select muscle activation patterns to generate the desired movement, and can we use this knowledge to design better assistive devices? 2. How does the musculoskeletal dynamics facilitate the neural control strategy in accomplishing this task of generating desired movements? To address these research questions, this thesis comprises a total of five journal and conference articles. More specifically, contributions I-III of this thesis focus on addressing the first research question which aims to understand how voluntary and reflexive movements can be predicted. First, we investigate various optimality principles using a musculoskeletal arm model to predict point-to-manifold reaching tasks. By using predictive simulations, we demonstrate how the arm would move towards a goal if, for example, our neural control strategy would minimize energy consumption. The main finding of this contribution shows that it is essential to include muscle dynamics and consider tasks with more openly defined targets to draw accurate conclusions about motor control. Through our analysis, we show that a combination of mechanical work, jerk and neuronal stimulation effort best predicts point-reaching when compared to human experiments. Second, we propose a novel method to optimize the design of exoskeleton power units taking into account the load cycle of predicted human movements. To achieve this goal, we employ a forward dynamic simulation of a generic musculoskeletal arm model, which is first scaled to represent different individuals. Next, we predict individual human motions and employ the predicted human torques to scale the electrical power units employing a novel scalability model. By considering the individual user needs and task demands, our approach achieves a lighter and more efficient design. In conclusion, our framework demonstrates the potential to improve the design of individual assistive devices. The third contribution focuses on predicting reflexive movements in response to sudden perturbations of the head-neck complex. To achieve this, we conducted experiments in which volunteers were placed on a table while supporting their heads with a trapdoor. This trapdoor was then suddenly released leading to a downward movement of the head until the reflexive reaction of the muscles stops the head from falling. We analyzed the results of these experiments, presenting characteristic parameters and highlighting differences between separate age and gender groups. Using this data, we also set up benchmark validations for a musculoskeletal head-neck model, including reflex control strategies. Our main findings are that there are large individual differences in reflexive responses between participants and that the perturbation direction significantly affects the reflexive response. Furthermore, we show that this data can be used as a benchmark test to validate musculoskeletal models and different muscle control strategies. While the first three contributions focus on the research question (1), contributions IV-V focus on (2) whether and how the musculoskeletal dynamics facilitate the learning and control task of various movements. We utilize a recently introduced information-theoretic approach called control effort to quantify the minimally required information to perform specific movements. By applying this concept, we can for example quantify how much biological muscles reduce the neuronal information load compared to technical DC-motors. We present a novel optimization algorithm to find this control effort and apply it to point-reaching and walking tasks. The main finding of this contribution is that the musculoskeletal dynamics reduce the control effort required for these movements compared to torque-driven systems. Finally, we hypothesize that the highly nonlinear muscle dynamics not only facilitate the control task but also provide inherent stability that is beneficial for learning from scratch. To test this, we employed various learning strategies for multiple anthropomorphic tasks, including point-reaching, ball-hitting, hopping, and squatting. The results of this investigation demonstrate that using muscle-like actuators improves the data-efficiency of the learning tasks. Additionally, including the muscle dynamics improves the robustness towards hyperparameters and allows for a better generalization towards unknown and unlearned perturbations. In summary, this thesis enhances existing methods to control and learn muscle-actuated motion, quantifies the control effort needed to perform certain movements and demonstrates that the inherent stability of the muscle dynamics facilitates the learning task. The models, control strategies, and experimental data presented in this work aid researchers in science and industry to improve their predictions in various fields such as neuroscience, ergonomics, rehabilitation, passive safety systems, and robotics. This allows us to reverse-engineer how we as humans control movement, uncovering the complex relationship between musculoskeletal dynamics and neural controller.Item Open Access Scale-up of gas fermentations : modelling tools for risk minimisation(2020) Siebler, FloraThe reduction of greenhouse gas emissions is a global endeavour supported by society, politics and industry. In recent years, circular economy, reducing the exploitation of fossil energy sources, have increased the demand for new solutions when producing commodities and fine chemicals. Caboxydotrophic fermentations with acetogenic bacteria are potential processes in order to reach these goals. They convert gaseous substrates such as CO, and CO2/H2 mixtures. However, gases as sole substrate are rather challenging, not only in small lab-scales but especially in large-scale. Transferring an efficient fermentation process from experimental to industrial scales often results in unpredictable performance losses. This study presents an in silico concept minimising possible risks in gas fermentations up-scaling. First, the economical feasibility of various fermentation methods is investigated. Then, two computational tools are presented using Clostridium ljungdahlii as model organism and synthesis gas as substrate in a 125 m3 bubble column reactor. The combination of economical investigation with modelling tools show high potential for successful scale-up of gas fermentations. With this concept feasibility, reactor design, operation mode and general risk minimisation can be analysed and specified.