@phdthesis{14651, abstract = {For self-incompatibility (SI) to be stable in a population, theory predicts that sufficient inbreeding depression (ID) is required: the fitness of offspring from self-mated individuals must be low enough to prevent the spread of self-compatibility (SC). Reviews of natural plant populations have supported this theory, with SI species generally showing high levels of ID. However, there is thought to be an under-sampling of self-incompatible taxa in the current literature. In this thesis, I study inbreeding depression in the SI plant species Antirrhinum majus using both greenhouse crosses and a large collected field dataset. Additionally, the gametophytic S-locus of A. majus is highly heterozygous and polymorphic, thus making assembly and discovery of S-alleles very difficult. Here, 206 new alleles of the male component SLFs are presented, along with a phylogeny showing the high conservation with alleles from another Antirrhinum species. Lastly, selected sites within the protein structure of SLFs are investigated, with one site in particular highlighted as potentially being involved in the SI recognition mechanism.}, author = {Arathoon, Louise S}, issn = {2663-337X}, pages = {96}, publisher = {Institute of Science and Technology Austria}, title = {{Investigating inbreeding depression and the self-incompatibility locus of Antirrhinum majus}}, doi = {10.15479/at:ista:14651}, year = {2023}, } @phdthesis{12726, abstract = {Most motions of many-body systems at any scale in nature with sufficient degrees of freedom tend to be chaotic; reaching from the orbital motion of planets, the air currents in our atmosphere, down to the water flowing through our pipelines or the movement of a population of bacteria. To the observer it is therefore intriguing when a moving collective exhibits order. Collective motion of flocks of birds, schools of fish or swarms of self-propelled particles or robots have been studied extensively over the past decades but the mechanisms involved in the transition from chaos to order remain unclear. Here, the interactions, that in most systems give rise to chaos, sustain order. In this thesis we investigate mechanisms that preserve, destabilize or lead to the ordered state. We show that endothelial cells migrating in circular confinements transition to a collective rotating state and concomitantly synchronize the frequencies of nucleating actin waves within individual cells. Consequently, the frequency dependent cell migration speed uniformizes across the population. Complementary to the WAVE dependent nucleation of traveling actin waves, we show that in leukocytes the actin polymerization depending on WASp generates pushing forces locally at stationary patches. Next, in pipe flows, we study methods to disrupt the self–sustaining cycle of turbulence and therefore relaminarize the flow. While we find in pulsating flow conditions that turbulence emerges through a helical instability during the decelerating phase. Finally, we show quantitatively in brain slices of mice that wild-type control neurons can compensate the migratory deficits of a genetically modified neuronal sub–population in the developing cortex.}, author = {Riedl, Michael}, issn = {2663-337X}, pages = {260}, publisher = {Institute of Science and Technology Austria}, title = {{Synchronization in collectively moving active matter}}, doi = {10.15479/at:ista:12726}, year = {2023}, } @phdthesis{14530, abstract = {Most motions of many-body systems at any scale in nature with sufficient degrees of freedom tend to be chaotic; reaching from the orbital motion of planets, the air currents in our atmosphere, down to the water flowing through our pipelines or the movement of a population of bacteria. To the observer it is therefore intriguing when a moving collective exhibits order. Collective motion of flocks of birds, schools of fish or swarms of self-propelled particles or robots have been studied extensively over the past decades but the mechanisms involved in the transition from chaos to order remain unclear. Here, the interactions, that in most systems give rise to chaos, sustain order. In this thesis we investigate mechanisms that preserve, destabilize or lead to the ordered state. We show that endothelial cells migrating in circular confinements transition to a collective rotating state and concomitantly synchronize the frequencies of nucleating actin waves within individual cells. Consequently, the frequency dependent cell migration speed uniformizes across the population. Complementary to the WAVE dependent nucleation of traveling actin waves, we show that in leukocytes the actin polymerization depending on WASp generates pushing forces locally at stationary patches. Next, in pipe flows, we study methods to disrupt the self--sustaining cycle of turbulence and therefore relaminarize the flow. While we find in pulsating flow conditions that turbulence emerges through a helical instability during the decelerating phase. Finally, we show quantitatively in brain slices of mice that wild-type control neurons can compensate the migratory deficits of a genetically modified neuronal sub--population in the developing cortex. }, author = {Riedl, Michael}, issn = {2663-337X}, keywords = {Synchronization, Collective Movement, Active Matter, Cell Migration, Active Colloids}, pages = {260}, publisher = {Institute of Science and Technology Austria}, title = {{Synchronization in collectively moving active matter}}, doi = {10.15479/14530}, year = {2023}, } @phdthesis{13074, abstract = {Deep learning has become an integral part of a large number of important applications, and many of the recent breakthroughs have been enabled by the ability to train very large models, capable to capture complex patterns and relationships from the data. At the same time, the massive sizes of modern deep learning models have made their deployment to smaller devices more challenging; this is particularly important, as in many applications the users rely on accurate deep learning predictions, but they only have access to devices with limited memory and compute power. One solution to this problem is to prune neural networks, by setting as many of their parameters as possible to zero, to obtain accurate sparse models with lower memory footprint. Despite the great research progress in obtaining sparse models that preserve accuracy, while satisfying memory and computational constraints, there are still many challenges associated with efficiently training sparse models, as well as understanding their generalization properties. The focus of this thesis is to investigate how the training process of sparse models can be made more efficient, and to understand the differences between sparse and dense models in terms of how well they can generalize to changes in the data distribution. We first study a method for co-training sparse and dense models, at a lower cost compared to regular training. With our method we can obtain very accurate sparse networks, and dense models that can recover the baseline accuracy. Furthermore, we are able to more easily analyze the differences, at prediction level, between the sparse-dense model pairs. Next, we investigate the generalization properties of sparse neural networks in more detail, by studying how well different sparse models trained on a larger task can adapt to smaller, more specialized tasks, in a transfer learning scenario. Our analysis across multiple pruning methods and sparsity levels reveals that sparse models provide features that can transfer similarly to or better than the dense baseline. However, the choice of the pruning method plays an important role, and can influence the results when the features are fixed (linear finetuning), or when they are allowed to adapt to the new task (full finetuning). Using sparse models with fixed masks for finetuning on new tasks has an important practical advantage, as it enables training neural networks on smaller devices. However, one drawback of current pruning methods is that the entire training cycle has to be repeated to obtain the initial sparse model, for every sparsity target; in consequence, the entire training process is costly and also multiple models need to be stored. In the last part of the thesis we propose a method that can train accurate dense models that are compressible in a single step, to multiple sparsity levels, without additional finetuning. Our method results in sparse models that can be competitive with existing pruning methods, and which can also successfully generalize to new tasks.}, author = {Peste, Elena-Alexandra}, issn = {2663-337X}, pages = {147}, publisher = {Institute of Science and Technology Austria}, title = {{Efficiency and generalization of sparse neural networks}}, doi = {10.15479/at:ista:13074}, year = {2023}, } @phdthesis{14641, abstract = {Mutation rates represent the net result of complex interactions among various cellular processes and can dramatically influence the evolutionary fate of microbial populations. However, many popular techniques used to study mutations are subject to the confounding effects of heredity and the subtleties of adaptation to selection, all of which make it difficult to observe any dynamic responses of mutation rates to fitness challenges. Furthermore, in spite of the ubiquity of quorum sensing systems across the bacterial domain and relevance for many physiological behaviors, the effects of such mechanisms on mutation rate and adaptation remain poorly understood. In the following work, I present the development of a microfluidic droplet-based method to measure single base-pair mutation rates in growing populations of the bacterium Escherichia coli. I use this method to observe a stress-induced increase in mutation rate that is mediated by luxS, a highly conserved bacterial quorum sensing component. I also show that the aforementioned increase in mutation rate, and its associated control by luxS, corresponds to a higher degree of adaptability under competitive environments.}, author = {Hennessey-Wesen, Mike}, issn = {2663-337X}, keywords = {microfluidics, miceobiology, mutations, quorum sensing}, pages = {104}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive mutation in E. coli modulated by luxS}}, doi = {10.15479/at:ista:14641}, year = {2023}, } @phdthesis{14506, abstract = {Payment channel networks are a promising approach to improve the scalability bottleneck of cryptocurrencies. Two design principles behind payment channel networks are efficiency and privacy. Payment channel networks improve efficiency by allowing users to transact in a peer-to-peer fashion along multi-hop routes in the network, avoiding the lengthy process of consensus on the blockchain. Transacting over payment channel networks also improves privacy as these transactions are not broadcast to the blockchain. Despite the influx of recent protocols built on top of payment channel networks and their analysis, a common shortcoming of many of these protocols is that they typically focus only on either improving efficiency or privacy, but not both. Another limitation on the efficiency front is that the models used to model actions, costs and utilities of users are limited or come with unrealistic assumptions. This thesis aims to address some of the shortcomings of recent protocols and algorithms on payment channel networks, particularly in their privacy and efficiency aspects. We first present a payment route discovery protocol based on hub labelling and private information retrieval that hides the route query and is also efficient. We then present a rebalancing protocol that formulates the rebalancing problem as a linear program and solves the linear program using multiparty computation so as to hide the channel balances. The rebalancing solution as output by our protocol is also globally optimal. We go on to develop more realistic models of the action space, costs, and utilities of both existing and new users that want to join the network. In each of these settings, we also develop algorithms to optimise the utility of these users with good guarantees on the approximation and competitive ratios.}, author = {Yeo, Michelle X}, issn = {2663-337X}, pages = {162}, publisher = {Institute of Science and Technology Austria}, title = {{Advances in efficiency and privacy in payment channel network analysis}}, doi = {10.15479/14506}, year = {2023}, } @phdthesis{13107, abstract = {Within the human body, the brain exhibits the highest rate of energy consumption amongst all organs, with the majority of generated ATP being utilized to sustain neuronal activity. Therefore, the metabolism of the mature cerebral cortex is geared towards preserving metabolic homeostasis whilst generating significant amounts of energy. This requires a precise interplay between diverse metabolic pathways, spanning from a tissue-wide scale to the level of individual neurons. Disturbances to this delicate metabolic equilibrium, such as those resulting from maternal malnutrition or mutations affecting metabolic enzymes, often result in neuropathological variants of neurodevelopment. For instance, mutations in SLC7A5, a transporter of metabolically essential large neutral amino acids (LNAAs), have been associated with autism and microcephaly. However, despite recent progress in the field, the extent of metabolic restructuring that occurs within the developing brain and the corresponding alterations in nutrient demands during various critical periods remain largely unknown. To investigate this, we performed metabolomic profiling of the murine cerebral cortex to characterize the metabolic state of the forebrain at different developmental stages. We found that the developing cortex undergoes substantial metabolic reprogramming, with specific sets of metabolites displaying stage-specific changes. According to our observations, we determined a distinct temporal period in postnatal development during which the cortex displays heightened reliance on LNAAs. Hence, using a conditional knock-out mouse model, we deleted Slc7a5 in neural cells, allowing us to monitor the impact of a perturbed neuronal metabolic state across multiple developmental stages of corticogenesis. We found that manipulating the levels of essential LNAAs in cortical neurons in vivo affects one particular perinatal developmental period critical for cortical network refinement. Abnormally low intracellular LNAA levels result in cell-autonomous alterations in neuronal lipid metabolism, excitability, and survival during this particular time window. Although most of the effects of Slc7a5 deletion on neuronal physiology are transient, derailment of these processes during this brief but crucial window leads to long-term circuit dysfunction in mice. In conclusion, out data indicate that the cerebral cortex undergoes significant metabolic reorganization during development. This process involves the intricate integration of multiple metabolic pathways to ensure optimal neuronal function throughout different developmental stages. Our findings offer a paradigm for understanding how neurons synchronize the expression of nutrient-related genes with their activity to allow proper brain maturation. Further, our results demonstrate that disruptions in these precisely calibrated metabolic processes during critical periods of brain development may result in neuropathological outcomes in mice and in humans.}, author = {Knaus, Lisa}, issn = {2663-337X}, pages = {147}, publisher = {Institute of Science and Technology Austria}, title = {{The metabolism of the developing brain : How large neutral amino acids modulate perinatal neuronal excitability and survival}}, doi = {10.15479/at:ista:13107}, year = {2023}, } @phdthesis{13175, abstract = {About a 100 years ago, we discovered that our universe is inherently noisy, that is, measuring any physical quantity with a precision beyond a certain point is not possible because of an omnipresent inherent noise. We call this - the quantum noise. Certain physical processes allow this quantum noise to get correlated in conjugate physical variables. These quantum correlations can be used to go beyond the potential of our inherently noisy universe and obtain a quantum advantage over the classical applications. Quantum noise being inherent also means that, at the fundamental level, the physical quantities are not well defined and therefore, objects can stay in multiple states at the same time. For example, the position of a particle not being well defined means that the particle is in multiple positions at the same time. About 4 decades ago, we started exploring the possibility of using objects which can be in multiple states at the same time to increase the dimensionality in computation. Thus, the field of quantum computing was born. We discovered that using quantum entanglement, a property closely related to quantum correlations, can be used to speed up computation of certain problems, such as factorisation of large numbers, faster than any known classical algorithm. Thus began the pursuit to make quantum computers a reality. Till date, we have explored quantum control over many physical systems including photons, spins, atoms, ions and even simple circuits made up of superconducting material. However, there persists one ubiquitous theme. The more readily a system interacts with an external field or matter, the more easily we can control it. But this also means that such a system can easily interact with a noisy environment and quickly lose its coherence. Consequently, such systems like electron spins need to be protected from the environment to ensure the longevity of their coherence. Other systems like nuclear spins are naturally protected as they do not interact easily with the environment. But, due to the same reason, it is harder to interact with such systems. After decades of experimentation with various systems, we are convinced that no one type of quantum system would be the best for all the quantum applications. We would need hybrid systems which are all interconnected - much like the current internet where all sorts of devices can all talk to each other - but now for quantum devices. A quantum internet. Optical photons are the best contenders to carry information for the quantum internet. They can carry quantum information cheaply and without much loss - the same reasons which has made them the backbone of our current internet. Following this direction, many systems, like trapped ions, have already demonstrated successful quantum links over a large distances using optical photons. However, some of the most promising contenders for quantum computing which are based on microwave frequencies have been left behind. This is because high energy optical photons can adversely affect fragile low-energy microwave systems. In this thesis, we present substantial progress on this missing quantum link between microwave and optics using electrooptical nonlinearities in lithium niobate. The nonlinearities are enhanced by using resonant cavities for all the involved modes leading to observation of strong direct coupling between optical and microwave frequencies. With this strong coupling we are not only able to achieve almost 100\% internal conversion efficiency with low added noise, thus presenting a quantum-enabled transducer, but also we are able to observe novel effects such as cooling of a microwave mode using optics. The strong coupling regime also leads to direct observation of dynamical backaction effect between microwave and optical frequencies which are studied in detail here. Finally, we also report first observation of microwave-optics entanglement in form of two-mode squeezed vacuum squeezed 0.7dB below vacuum level. With this new bridge between microwave and optics, the microwave-based quantum technologies can finally be a part of a quantum network which is based on optical photons - putting us one step closer to a future with quantum internet. }, author = {Sahu, Rishabh}, isbn = {978-3-99078-030-5}, issn = {2663-337X}, keywords = {quantum optics, electrooptics, quantum networks, quantum communication, transduction}, pages = {202}, publisher = {Institute of Science and Technology Austria}, title = {{Cavity quantum electrooptics}}, doi = {10.15479/at:ista:13175}, year = {2023}, } @phdthesis{12900, abstract = {About a 100 years ago, we discovered that our universe is inherently noisy, that is, measuring any physical quantity with a precision beyond a certain point is not possible because of an omnipresent inherent noise. We call this - the quantum noise. Certain physical processes allow this quantum noise to get correlated in conjugate physical variables. These quantum correlations can be used to go beyond the potential of our inherently noisy universe and obtain a quantum advantage over the classical applications. Quantum noise being inherent also means that, at the fundamental level, the physical quantities are not well defined and therefore, objects can stay in multiple states at the same time. For example, the position of a particle not being well defined means that the particle is in multiple positions at the same time. About 4 decades ago, we started exploring the possibility of using objects which can be in multiple states at the same time to increase the dimensionality in computation. Thus, the field of quantum computing was born. We discovered that using quantum entanglement, a property closely related to quantum correlations, can be used to speed up computation of certain problems, such as factorisation of large numbers, faster than any known classical algorithm. Thus began the pursuit to make quantum computers a reality. Till date, we have explored quantum control over many physical systems including photons, spins, atoms, ions and even simple circuits made up of superconducting material. However, there persists one ubiquitous theme. The more readily a system interacts with an external field or matter, the more easily we can control it. But this also means that such a system can easily interact with a noisy environment and quickly lose its coherence. Consequently, such systems like electron spins need to be protected from the environment to ensure the longevity of their coherence. Other systems like nuclear spins are naturally protected as they do not interact easily with the environment. But, due to the same reason, it is harder to interact with such systems. After decades of experimentation with various systems, we are convinced that no one type of quantum system would be the best for all the quantum applications. We would need hybrid systems which are all interconnected - much like the current internet where all sorts of devices can all talk to each other - but now for quantum devices. A quantum internet. Optical photons are the best contenders to carry information for the quantum internet. They can carry quantum information cheaply and without much loss - the same reasons which has made them the backbone of our current internet. Following this direction, many systems, like trapped ions, have already demonstrated successful quantum links over a large distances using optical photons. However, some of the most promising contenders for quantum computing which are based on microwave frequencies have been left behind. This is because high energy optical photons can adversely affect fragile low-energy microwave systems. In this thesis, we present substantial progress on this missing quantum link between microwave and optics using electrooptical nonlinearities in lithium niobate. The nonlinearities are enhanced by using resonant cavities for all the involved modes leading to observation of strong direct coupling between optical and microwave frequencies. With this strong coupling we are not only able to achieve almost 100\% internal conversion efficiency with low added noise, thus presenting a quantum-enabled transducer, but also we are able to observe novel effects such as cooling of a microwave mode using optics. The strong coupling regime also leads to direct observation of dynamical backaction effect between microwave and optical frequencies which are studied in detail here. Finally, we also report first observation of microwave-optics entanglement in form of two-mode squeezed vacuum squeezed 0.7dB below vacuum level. With this new bridge between microwave and optics, the microwave-based quantum technologies can finally be a part of a quantum network which is based on optical photons - putting us one step closer to a future with quantum internet. }, author = {Sahu, Rishabh}, isbn = {978-3-99078-030-5}, issn = {2663-337X}, keywords = {quantum optics, electrooptics, quantum networks, quantum communication, transduction}, pages = {190}, publisher = {Institute of Science and Technology Austria}, title = {{Cavity quantum electrooptics}}, doi = {10.15479/at:ista:12900}, year = {2023}, } @phdthesis{12897, abstract = {Inverse design problems in fabrication-aware shape optimization are typically solved on discrete representations such as polygonal meshes. This thesis argues that there are benefits to treating these problems in the same domain as human designers, namely, the parametric one. One reason is that discretizing a parametric model usually removes the capability of making further manual changes to the design, because the human intent is captured by the shape parameters. Beyond this, knowledge about a design problem can sometimes reveal a structure that is present in a smooth representation, but is fundamentally altered by discretizing. In this case, working in the parametric domain may even simplify the optimization task. We present two lines of research that explore both of these aspects of fabrication-aware shape optimization on parametric representations. The first project studies the design of plane elastic curves and Kirchhoff rods, which are common mathematical models for describing the deformation of thin elastic rods such as beams, ribbons, cables, and hair. Our main contribution is a characterization of all curved shapes that can be attained by bending and twisting elastic rods having a stiffness that is allowed to vary across the length. Elements like these can be manufactured using digital fabrication devices such as 3d printers and digital cutters, and have applications in free-form architecture and soft robotics. We show that the family of curved shapes that can be produced this way admits geometric description that is concise and computationally convenient. In the case of plane curves, the geometric description is intuitive enough to allow a designer to determine whether a curved shape is physically achievable by visual inspection alone. We also present shape optimization algorithms that convert a user-defined curve in the plane or in three dimensions into the geometry of an elastic rod that will naturally deform to follow this curve when its endpoints are attached to a support structure. Implemented in an interactive software design tool, the rod geometry is generated in real time as the user edits a curve and enables fast prototyping. The second project tackles the problem of general-purpose shape optimization on CAD models using a novel variant of the extended finite element method (XFEM). Our goal is the decoupling between the simulation mesh and the CAD model, so no geometry-dependent meshing or remeshing needs to be performed when the CAD parameters change during optimization. This is achieved by discretizing the embedding space of the CAD model, and using a new high-accuracy numerical integration method to enable XFEM on free-form elements bounded by the parametric surface patches of the model. Our simulation is differentiable from the CAD parameters to the simulation output, which enables us to use off-the-shelf gradient-based optimization procedures. The result is a method that fits seamlessly into the CAD workflow because it works on the same representation as the designer, enabling the alternation of manual editing and fabrication-aware optimization at will.}, author = {Hafner, Christian}, isbn = {978-3-99078-031-2}, issn = {2663-337X}, pages = {180}, publisher = {Institute of Science and Technology Austria}, title = {{Inverse shape design with parametric representations: Kirchhoff Rods and parametric surface models}}, doi = {10.15479/at:ista:12897}, year = {2023}, } @phdthesis{12491, abstract = {The extracellular matrix (ECM) is a hydrated and complex three-dimensional network consisting of proteins, polysaccharides, and water. It provides structural scaffolding for the cells embedded within it and is essential in regulating numerous physiological processes, including cell migration and proliferation, wound healing, and stem cell fate. Despite extensive study, detailed structural knowledge of ECM components in physiologically relevant conditions is still rudimentary. This is due to methodological limitations in specimen preparation protocols which are incompatible with keeping large samples, such as the ECM, in their native state for subsequent imaging. Conventional electron microscopy (EM) techniques rely on fixation, dehydration, contrasting, and sectioning. This results in the alteration of a highly hydrated environment and the potential introduction of artifacts. Other structural biology techniques, such as nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography, allow high-resolution analysis of protein structures but only work on homogenous and purified samples, hence lacking contextual information. Currently, no approach exists for the ultrastructural and structural study of extracellular components under native conditions in a physiological, 3D environment. In this thesis, I have developed a workflow that allows for the ultrastructural analysis of the ECM in near-native conditions at molecular resolution. The developments I introduced include implementing a novel specimen preparation workflow for cell-derived matrices (CDMs) to render them compatible with ion-beam milling and subsequent high-resolution cryo-electron tomography (ET). To this end, I have established protocols to generate CDMs grown over several weeks on EM grids that are compatible with downstream cryo-EM sample preparation and imaging techniques. Characterization of these ECMs confirmed that they contain essential ECM components such as collagen I, collagen VI, and fibronectin I in high abundance and hence represent a bona fide biologically-relevant sample. I successfully optimized vitrification of these specimens by testing various vitrification techniques and cryoprotectants. In order to obtain high-resolution molecular insights into the ultrastructure and organization of CDMs, I established cryo-focused ion beam scanning electron microscopy (FIBSEM) on these challenging and complex specimens. I explored different approaches for the creation of thin cryo-lamellae by FIB milling and succeeded in optimizing the cryo-lift-out technique, resulting in high-quality lamellae of approximately 200 nm thickness. High-resolution Cryo-ET of these lamellae revealed for the first time the architecture of native CDM in the context of matrix-secreting cells. This allowed for the in situ visualization of fibrillar matrix proteins such as collagen, laying the foundation for future structural and ultrastructural characterization of these proteins in their near-native environment. In summary, in this thesis, I present a novel workflow that combines state-of-the-art cryo-EM specimen preparation and imaging technologies to permit characterization of the ECM, an important tissue component in higher organisms. This innovative and highly versatile workflow will enable addressing far-reaching questions on ECM architecture, composition, and reciprocal ECM-cell interactions.}, author = {Zens, Bettina}, isbn = {978-3-99078-027-5}, issn = {2663-337X}, keywords = {cryo-EM, cryo-ET, FIB milling, method development, FIBSEM, extracellular matrix, ECM, cell-derived matrices, CDMs, cell culture, high pressure freezing, HPF, structural biology, tomography, collagen}, pages = {187}, publisher = {Institute of Science and Technology Austria}, title = {{Ultrastructural characterization of natively preserved extracellular matrix by cryo-electron tomography}}, doi = {10.15479/at:ista:12491}, year = {2023}, } @phdthesis{13984, abstract = {Social insects fight disease using their individual immune systems and the cooperative sanitary behaviors of colony members. These social defenses are well explored against externally-infecting pathogens, but little is known about defense strategies against internally-infecting pathogens, such as viruses. Viruses are ubiquitous and in the last decades it has become evident that also many ant species harbor viruses. We present one of the first studies addressing transmission dynamics and collective disease defenses against viruses in ants on a mechanistic level. I successfully established an experimental ant host – viral pathogen system as a model for the defense strategies used by social insects against internal pathogen infections, as outlined in the third chapter. In particular, we studied how garden ants (Lasius neglectus) defend themselves and their colonies against the generalist insect virus CrPV (cricket paralysis virus). We chose microinjections of virus directly into the ants’ hemolymph because it allowed us to use a defined exposure dose. Here we show that this is a good model system, as the virus is replicating and thus infecting the host. The ants mount a clear individual immune response against the viral infection, which is characterized by a specific siRNA pattern, namely siRNAs mapping against the viral genome with a peak of 21 and 22 bp long fragments. The onset of this immune response is consistent with the timeline of viral replication that starts already within two days post injection. The disease manifests in decreased survival over a course of two to three weeks. Regarding group living, we find that infected ants show a strong individual immune response, but that their course of disease is little affected by nestmate presence, as described in chapter four. Hence, we do not find social immunity in the context of viral infections in ants. Nestmates, however, can contract the virus. Using Drosophila S2R+ cells in culture, we showed that 94 % of the nestmates contract active virus within four days of social contact to an infected individual. Virus is transmitted in low doses, thus not causing disease transmission within the colony. While virus can be transmitted during short direct contacts, we also assume transmission from deceased ants and show that the nestmates’ immune system gets activated after contracting a low viral dose. We find considerable potential for indirect transmission via the nest space. Virus is shed to the nest, where it stays viable for one week and is also picked up by other ants. Apart from that, we want to underline the potential of ant poison as antiviral agent. We determined that ant poison successfully inactivates CrPV in vitro. However, we found no evidence for effective poison use to sanitize the nest space. On the other hand, local application of ant poison by oral poison uptake, which is part of the ants prophylactic behavioral repertoire, probably contributes to keeping the gut of each individual sanitized. We hypothesize that oral poison uptake might be the reason why we did not find viable virus in the trophallactic fluid. The fifth chapter encompasses preliminary data on potential social immunization. However, our experiments do not confirm an actual survival benefit for the nestmates upon pathogen challenge under the given experimental settings. Nevertheless, we do not want to rule out the possibility for nestmate immunization, but rather emphasize that considering different experimental timelines and viral doses would provide a multitude of options for follow-up experiments. In conclusion, we find that prophylactic individual behaviors, such as oral poison uptake, might play a role in preventing viral disease transmission. Compared to colony defense against external pathogens, internal pathogen infections require a stronger component of individual physiological immunity than behavioral social immunity, yet could still lead to collective protection.}, author = {Franschitz, Anna}, isbn = {978-3-99078-034-3}, issn = {2663-337X}, pages = {89}, publisher = {Institute of Science and Technology Austria}, title = {{Individual and social immunity against viral infections in ants}}, doi = {10.15479/at:ista:13984}, year = {2023}, } @phdthesis{12964, abstract = {Pattern formation is of great importance for its contribution across different biological behaviours. During developmental processes for example, patterns of chemical gradients are established to determine cell fate and complex tissue patterns emerge to define structures such as limbs and vascular networks. Patterns are also seen in collectively migrating groups, for instance traveling waves of density emerging in moving animal flocks as well as collectively migrating cells and tissues. To what extent these biological patterns arise spontaneously through the local interaction of individual constituents or are dictated by higher level instructions is still an open question however there is evidence for the involvement of both types of process. Where patterns arise spontaneously there is a long standing interest in how far the interplay of mechanics, e.g. force generation and deformation, and chemistry, e.g. gene regulation and signaling, contributes to the behaviour. This is because many systems are able to both chemically regulate mechanical force production and chemically sense mechanical deformation, forming mechano-chemical feedback loops which can potentially become unstable towards spatio and/or temporal patterning. We work with experimental collaborators to investigate the possibility that this type of interaction drives pattern formation in biological systems at different scales. We focus first on tissue-level ERK-density waves observed during the wound healing response across different systems where many previous studies have proposed that patterns depend on polarized cell migration and arise from a mechanical flocking-like mechanism. By combining theory with mechanical and optogenetic perturbation experiments on in vitro monolayers we instead find evidence for mechanochemical pattern formation involving only scalar bilateral feedbacks between ERK signaling and cell contraction. We perform further modeling and experiment to study how this instability couples with polar cell migration in order to produce a robust and efficient wound healing response. In a following chapter we implement ERK-density coupling and cell migration in a 2D active vertex model to investigate the interaction of ERK-density patterning with different tissue rheologies and find that the spatio-temporal dynamics are able to both locally and globally fluidize a tissue across the solid-fluid glass transition. In a last chapter we move towards lower spatial scales in the context of subcellular patterning of the cell cytoskeleton where we investigate the transition between phases of spatially homogeneous temporal oscillations and chaotic spatio-temporal patterning in the dynamics of myosin and ROCK activities (a motor component of the actomyosin cytoskeleton and its activator). Experimental evidence supports an intrinsic chemical oscillator which we encode in a reaction model and couple to a contractile active gel description of the cell cortex. The model exhibits phases of chemical oscillations and contractile spatial patterning which reproduce many features of the dynamics seen in Drosophila oocyte epithelia in vivo. However, additional pharmacological perturbations to inhibit myosin contractility leaves the role of contractile instability unclear. We discuss alternative hypotheses and investigate the possibility of reaction-diffusion instability.}, author = {Boocock, Daniel R}, isbn = {978-3-99078-032-9}, issn = {2663-337X}, pages = {146}, publisher = {Institute of Science and Technology Austria}, title = {{Mechanochemical pattern formation across biological scales}}, doi = {10.15479/at:ista:12964}, year = {2023}, } @phdthesis{12891, abstract = {The tight spatiotemporal coordination of signaling activity determining embryo patterning and the physical processes driving embryo morphogenesis renders embryonic development robust, such that key developmental processes can unfold relatively normally even outside of the full embryonic context. For instance, embryonic stem cell cultures can recapitulate the hallmarks of gastrulation, i.e. break symmetry leading to germ layer formation and morphogenesis, in a very reduced environment. This leads to questions on specific contributions of embryo-specific features, such as the presence of extraembryonic tissues, which are inherently involved in gastrulation in the full embryonic context. To address this, we established zebrafish embryonic explants without the extraembryonic yolk cell, an important player as a signaling source and for morphogenesis during gastrulation, as a model of ex vivo development. We found that dorsal-marginal determinants are required and sufficient in these explants to form and pattern all three germ layers. However, formation of tissues, which require the highest Nodal-signaling levels, is variable, demonstrating a contribution of extraembryonic tissues for reaching peak Nodal signaling levels. Blastoderm explants also undergo gastrulation-like axis elongation. We found that this elongation movement shows hallmarks of oriented mesendoderm cell intercalations typically associated with dorsal tissues in the intact embryo. These are disrupted by uniform upregulation of BMP signaling activity and concomitant explant ventralization, suggesting that tight spatial control of BMP signaling is a prerequisite for explant morphogenesis. This control is achieved by Nodal signaling, which is critical for effectively downregulating BMP signaling in the mesendoderm, highlighting that Nodal signaling is not only directly required for mesendoderm cell fate specification and morphogenesis, but also by maintaining low levels of BMP signaling at the dorsal side. Collectively, we provide insights into the capacity and organization of signaling and morphogenetic domains to recapitulate features of zebrafish gastrulation outside of the full embryonic context.}, author = {Schauer, Alexandra}, issn = {2663-337X}, pages = {190}, publisher = {Institute of Science and Technology Austria}, title = {{Mesendoderm formation in zebrafish gastrulation: The role of extraembryonic tissues}}, doi = {10.15479/at:ista:12891}, year = {2023}, } @phdthesis{14422, abstract = {Animals exhibit a remarkable ability to learn and remember new behaviors, skills, and associations throughout their lifetime. These capabilities are made possible thanks to a variety of changes in the brain throughout adulthood, regrouped under the term "plasticity". Some cells in the brain —neurons— and specifically changes in the connections between neurons, the synapses, were shown to be crucial for the formation, selection, and consolidation of memories from past experiences. These ongoing changes of synapses across time are called synaptic plasticity. Understanding how a myriad of biochemical processes operating at individual synapses can somehow work in concert to give rise to meaningful changes in behavior is a fascinating problem and an active area of research. However, the experimental search for the precise plasticity mechanisms at play in the brain is daunting, as it is difficult to control and observe synapses during learning. Theoretical approaches have thus been the default method to probe the plasticity-behavior connection. Such studies attempt to extract unifying principles across synapses and model all observed synaptic changes using plasticity rules: equations that govern the evolution of synaptic strengths across time in neuronal network models. These rules can use many relevant quantities to determine the magnitude of synaptic changes, such as the precise timings of pre- and postsynaptic action potentials, the recent neuronal activity levels, the state of neighboring synapses, etc. However, analytical studies rely heavily on human intuition and are forced to make simplifying assumptions about plasticity rules. In this thesis, we aim to assist and augment human intuition in this search for plasticity rules. We explore whether a numerical approach could automatically discover the plasticity rules that elicit desired behaviors in large networks of interconnected neurons. This approach is dubbed meta-learning synaptic plasticity: learning plasticity rules which themselves will make neuronal networks learn how to solve a desired task. We first write all the potential plasticity mechanisms to consider using a single expression with adjustable parameters. We then optimize these plasticity parameters using evolutionary strategies or Bayesian inference on tasks known to involve synaptic plasticity, such as familiarity detection and network stabilization. We show that these automated approaches are powerful tools, able to complement established analytical methods. By comprehensively screening plasticity rules at all synapse types in realistic, spiking neuronal network models, we discover entire sets of degenerate plausible plasticity rules that reliably elicit memory-related behaviors. Our approaches allow for more robust experimental predictions, by abstracting out the idiosyncrasies of individual plasticity rules, and provide fresh insights on synaptic plasticity in spiking network models. }, author = {Confavreux, Basile J}, issn = {2663-337X}, pages = {148}, publisher = {Institute of Science and Technology Austria}, title = {{Synapseek: Meta-learning synaptic plasticity rules}}, doi = {10.15479/at:ista:14422}, year = {2023}, } @phdthesis{12809, abstract = {Understanding the mechanisms of learning and memory formation has always been one of the main goals in neuroscience. Already Pavlov (1927) in his early days has used his classic conditioning experiments to study the neural mechanisms governing behavioral adaptation. What was not known back then was that the part of the brain that is largely responsible for this type of associative learning is the cerebellum. Since then, plenty of theories on cerebellar learning have emerged. Despite their differences, one thing they all have in common is that learning relies on synaptic and intrinsic plasticity. The goal of my PhD project was to unravel the molecular mechanisms underlying synaptic plasticity in two synapses that have been shown to be implicated in motor learning, in an effort to understand how learning and memory formation are processed in the cerebellum. One of the earliest and most well-known cerebellar theories postulates that motor learning largely depends on long-term depression at the parallel fiber-Purkinje cell (PC-PC) synapse. However, the discovery of other types of plasticity in the cerebellar circuitry, like long-term potentiation (LTP) at the PC-PC synapse, potentiation of molecular layer interneurons (MLIs), and plasticity transfer from the cortex to the cerebellar/ vestibular nuclei has increased the popularity of the idea that multiple sites of plasticity might be involved in learning. Still a lot remains unknown about the molecular mechanisms responsible for these types of plasticity and whether they occur during physiological learning. In the first part of this thesis we have analyzed the variation and nanodistribution of voltagegated calcium channels (VGCCs) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid type glutamate receptors (AMPARs) on the parallel fiber-Purkinje cell synapse after vestibuloocular reflex phase reversal adaptation, a behavior that has been suggested to rely on PF-PC LTP. We have found that on the last day of adaptation there is no learning trace in form of VGCCs nor AMPARs variation at the PF-PC synapse, but instead a decrease in the number of PF-PC synapses. These data seem to support the view that learning is only stored in the cerebellar cortex in an initial learning phase, being transferred later to the vestibular nuclei. Next, we have studied the role of MLIs in motor learning using a relatively simple and well characterized behavioral paradigm – horizontal optokinetic reflex (HOKR) adaptation. We have found behavior-induced MLI potentiation in form of release probability increase that could be explained by the increase of VGCCs at the presynaptic side. Our results strengthen the idea of distributed cerebellar plasticity contributing to learning and provide a novel mechanism for release probability increase. }, author = {Alcarva, Catarina}, issn = {2663-337X}, pages = {115}, publisher = {Institute of Science and Technology Austria}, title = {{Plasticity in the cerebellum: What molecular mechanisms are behind physiological learning}}, doi = {10.15479/at:ista:12809}, year = {2023}, } @phdthesis{14622, abstract = {This Ph.D. thesis presents a detailed investigation into Variational Quantum Algorithms (VQAs), a promising class of quantum algorithms that are well suited for near-term quantum computation due to their moderate hardware requirements and resilience to noise. Our primary focus lies on two particular types of VQAs: the Quantum Approximate Optimization Algorithm (QAOA), used for solving binary optimization problems, and the Variational Quantum Eigensolver (VQE), utilized for finding ground states of quantum many-body systems. In the first part of the thesis, we examine the issue of effective parameter initialization for the QAOA. The work demonstrates that random initialization of the QAOA often leads to convergence in local minima with sub-optimal performance. To mitigate this issue, we propose an initialization of QAOA parameters based on the Trotterized Quantum Annealing (TQA). We show that TQA initialization leads to the same performance as the best of an exponentially scaling number of random initializations. The second study introduces Transition States (TS), stationary points with a single direction of descent, as a tool for systematically exploring the QAOA optimization landscape. This leads us to propose a novel greedy parameter initialization strategy that guarantees for the energy to decrease with increasing number of circuit layers. In the third section, we extend the QAOA to qudit systems, which are higher-dimensional generalizations of qubits. This chapter provides theoretical insights and practical strategies for leveraging the increased computational power of qudits in the context of quantum optimization algorithms and suggests a quantum circuit for implementing the algorithm on an ion trap quantum computer. Finally, we propose an algorithm to avoid “barren plateaus”, regions in parameter space with vanishing gradients that obstruct efficient parameter optimization. This novel approach relies on defining a notion of weak barren plateaus based on the entropies of local reduced density matrices and showcases how these can be efficiently quantified using shadow tomography. To illustrate the approach we employ the strategy in the VQE and show that it allows to successfully avoid barren plateaus in the initialization and throughout the optimization. Taken together, this thesis greatly enhances our understanding of parameter initialization and optimization in VQAs, expands the scope of QAOA to higher-dimensional quantum systems, and presents a method to address the challenge of barren plateaus using the VQE. These insights are instrumental in advancing the field of near-term quantum computation.}, author = {Sack, Stefan}, issn = {2663-337X}, pages = {142}, publisher = {Institute of Science and Technology Austria}, title = {{Improving variational quantum algorithms : Innovative initialization techniques and extensions to qudit systems}}, doi = {10.15479/at:ista:14622}, year = {2023}, } @phdthesis{14697, abstract = {During my Ph.D. research, I managed a series of projects, each focused on the mechanisms underlying cell migration. My work involved an in-depth examination of the complex strategies employed by neutrophils, with a specific focus on their ability to synchronize spatial-temporal cues and optimize their gradient perception. However, it is essential to acknowledge that not all projects yielded successful results, as some ideas were discontinued and are archived for future reference within this thesis. My main project investigated how neutrophils decode spatial cues for precise navigation. Human neutrophils showcased distinct movement patterns based on source type – linear or point-like. By combining single-cell tracking in 3D environments with proxy dyes, this project linked cell behaviors to gradient changes, revealing a stronger response to semi-exponential gradients from point sources. In addition, neutrophils exhibited oscillating migration speeds, using speed minima to adjust trajectories toward sources. Experiencing continuous concentration changes, they accelerated over time and employed a "Run and Fumble" strategy, alternating between consistent runs and strategic "tumbles" for efficient navigation. The project extended to the possibility of cells amplifying perceived gradients by enclosing their immediate surroundings, pushing attractants forward for enrichment while depleting it at the cell rear. Microfluidic devices were employed, and various experimental parameters configurations were optimized. Although significant differences in migratory efficacy were detected across pore sizes and device heights, quantifying gradient manipulation effects proved challenging. The "Laser-Assisted Protein Adsorption by Photobleaching" (LAPAP) project was promising, as it allowed the printing of gradients. Initially successful with dendritic cells, we aimed to adapt it for neutrophils. Through extensive experimentation with multiple parameters, we attempted to trigger responses from neutrophils. Despite these efforts and collaboration, the project failed due to practical challenges and limitations. Facing a lack of neutrophil-like cells at IST, we initially established the SCF-HoxB8 primary murine cell line. Despite their existence, their migratory behavior was largely unexplored due to potential limitations. Through differentiation protocol refinements we enhanced their migratory capabilities, though their capacity still lagged behind human neutrophils. Despite this, the improved migration potential of these cells pointed toward their utility for in vitro murine neutrophil migration studies.}, author = {Stopp, Julian A}, isbn = {978-3-99078-038-1}, issn = {2663-337X}, pages = {226}, publisher = {Institute of Science and Technology Austria}, title = {{Neutrophils on the hunt : Migratory strategies employed by neutrophils to fulfill their effector function}}, doi = {10.15479/at:ista:14697}, year = {2023}, } @phdthesis{14280, abstract = {Cell division in Escherichia coli is performed by the divisome, a multi-protein complex composed of more than 30 proteins. The divisome spans from the cytoplasm through the inner membrane to the cell wall and the outer membrane. Divisome assembly is initiated by a cytoskeletal structure, the so-called Z-ring, which localizes at the center of the E. coli cell and determines the position of the future cell septum. The Z-ring is composed of the highly conserved bacterial tubulin homologue FtsZ, which forms treadmilling filaments. These filaments are recruited to the inner membrane by FtsA, a highly conserved bacterial actin homologue. FtsA interacts with other proteins in the periplasm and thus connects the cytoplasmic and periplasmic components of the divisome. A previous model postulated that FtsA regulates maturation of the divisome by switching from an oligomeric, inactive state to a monomeric and active state. This model was based mostly on in vivo studies, as a biochemical characterization of FtsA has been hampered by difficulties in purifying the protein. Here, we studied FtsA using an in vitro reconstitution approach and aimed to answer two questions: (i) How are dynamics from cytoplasmic, treadmilling FtsZ filaments coupled to proteins acting in the periplasmic space and (ii) How does FtsA regulate the maturation of the divisome? We found that the cytoplasmic peptides of the transmembrane proteins FtsN and FtsQ interact directly with FtsA and can follow the spatiotemporal signal of FtsA/Z filaments. When we investigated the underlying mechanism by imaging single molecules of FtsNcyto, we found the peptide to interact transiently with FtsA. An in depth analysis of the single molecule trajectories helped to postulate a model where PG synthases follow the dynamics of FtsZ by a diffusion and capture mechanism. Following up on these findings we were interested in how the self-interaction of FtsA changes when it encounters FtsNcyto and if we can confirm the proposed oligomer-monomer switch. For this, we compared the behavior of the previously identified, hyperactive mutant FtsA R286W with wildtype FtsA. The mutant outperforms WT in mirroring and transmitting the spatiotemporal signal of treadmilling FtsZ filaments. Surprisingly however, we found that this was not due to a difference in the self-interaction strength of the two variants, but a difference in their membrane residence time. Furthermore, in contrast to our expectations, upon binding of FtsNcyto the measured self-interaction of FtsA actually increased. We propose that FtsNcyto induces a rearrangement of the oligomeric architecture of FtsA. In further consequence this change leads to more persistent FtsZ filaments which results in a defined signalling zone, allowing formation of the mature divisome. The observed difference between FtsA WT and R286W is due to the vastly different membrane turnover of the proteins. R286W cycles 5-10x faster compared to WT which allows to sample FtsZ filaments at faster frequencies. These findings can explain the observed differences in toxicity for overexpression of FtsA WT and R286W and help to understand how FtsA regulates divisome maturation.}, author = {Radler, Philipp}, isbn = {978-3-99078-033-6}, issn = {2663-337X}, keywords = {Cell Division, Reconstitution, FtsZ, FtsA, Divisome, E.coli}, pages = {156}, publisher = {Institute of Science and Technology Austria}, title = {{Spatiotemporal signaling during assembly of the bacterial divisome}}, doi = {10.15479/at:ista:14280}, year = {2023}, } @phdthesis{12781, abstract = {Most energy in humans is produced in form of ATP by the mitochondrial respiratory chain consisting of several protein assemblies embedded into lipid membrane (complexes I-V). Complex I is the first and the largest enzyme of the respiratory chain which is essential for energy production. It couples the transfer of two electrons from NADH to ubiquinone with proton translocation across bacterial or inner mitochondrial membrane. The coupling mechanism between electron transfer and proton translocation is one of the biggest enigma in bioenergetics and structural biology. Even though the enzyme has been studied for decades, only recent technological advances in cryo-EM allowed its extensive structural investigation. Complex I from E.coli appears to be of special importance because it is a perfect model system with a rich mutant library, however the structure of the entire complex was unknown. In this thesis I have resolved structures of the minimal complex I version from E. coli in different states including reduced, inhibited, under reaction turnover and several others. Extensive structural analyses of these structures and comparison to structures from other species allowed to derive general features of conformational dynamics and propose a universal coupling mechanism. The mechanism is straightforward, robust and consistent with decades of experimental data available for complex I from different species. Cyanobacterial NDH (cyanobacterial complex I) is a part of broad complex I superfamily and was studied as well in this thesis. It plays an important role in cyclic electron transfer (CET), during which electrons are cycled within PSI through ferredoxin and plastoquinone to generate proton gradient without NADPH production. Here, I solved structure of NDH and revealed additional state, which was not observed before. The novel “resting” state allowed to propose the mechanism of CET regulation. Moreover, conformational dynamics of NDH resembles one in complex I which suggest more broad universality of the proposed coupling mechanism. In summary, results presented here helped to interpret decades of experimental data for complex I and contributed to fundamental mechanistic understanding of protein function. }, author = {Kravchuk, Vladyslav}, isbn = {978-3-99078-029-9}, issn = {2663-337X}, pages = {127}, publisher = {Institute of Science and Technology Austria}, title = {{Structural and mechanistic study of bacterial complex I and its cyanobacterial ortholog}}, doi = {10.15479/at:ista:12781}, year = {2023}, }