@phdthesis{14711, abstract = {In nature, different species find their niche in a range of environments, each with its unique characteristics. While some thrive in uniform (homogeneous) landscapes where environmental conditions stay relatively consistent across space, others traverse the complexities of spatially heterogeneous terrains. Comprehending how species are distributed and how they interact within these landscapes holds the key to gaining insights into their evolutionary dynamics while also informing conservation and management strategies. For species inhabiting heterogeneous landscapes, when the rate of dispersal is low compared to spatial fluctuations in selection pressure, localized adaptations may emerge. Such adaptation in response to varying selection strengths plays an important role in the persistence of populations in our rapidly changing world. Hence, species in nature are continuously in a struggle to adapt to local environmental conditions, to ensure their continued survival. Natural populations can often adapt in time scales short enough for evolutionary changes to influence ecological dynamics and vice versa, thereby creating a feedback between evolution and demography. The analysis of this feedback and the relative contributions of gene flow, demography, drift, and natural selection to genetic variation and differentiation has remained a recurring theme in evolutionary biology. Nevertheless, the effective role of these forces in maintaining variation and shaping patterns of diversity is not fully understood. Even in homogeneous environments devoid of local adaptations, such understanding remains elusive. Understanding this feedback is crucial, for example in determining the conditions under which extinction risk can be mitigated in peripheral populations subject to deleterious mutation accumulation at the edges of species’ ranges as well as in highly fragmented populations. In this thesis we explore both uniform and spatially heterogeneous metapopulations, investigating and providing theoretical insights into the dynamics of local adaptation in the latter and examining the dynamics of load and extinction as well as the impact of joint ecological and evolutionary (eco-evolutionary) dynamics in the former. The thesis is divided into 5 chapters. Chapter 1 provides a general introduction into the subject matter, clarifying concepts and ideas used throughout the thesis. In chapter 2, we explore how fast a species distributed across a heterogeneous landscape adapts to changing conditions marked by alterations in carrying capacity, selection pressure, and migration rate. In chapter 3, we investigate how migration selection and drift influences adaptation and the maintenance of variation in a metapopulation with three habitats, an extension of previous models of adaptation in two habitats. We further develop analytical approximations for the critical threshold required for polymorphism to persist. The focus of chapter 4 of the thesis is on understanding the interplay between ecology and evolution as coupled processes. We investigate how eco-evolutionary feedback between migration, selection, drift, and demography influences eco-evolutionary outcomes in marginal populations subject to deleterious mutation accumulation. Using simulations as well as theoretical approximations of the coupled dynamics of population size and allele frequency, we analyze how gene flow from a large mainland source influences genetic load and population size on an island (i.e., in a marginal population) under genetically realistic assumptions. Analyses of this sort are important because small isolated populations, are repeatedly affected by complex interactions between ecological and evolutionary processes, which can lead to their death. Understanding these interactions can therefore provide an insight into the conditions under which extinction risk can be mitigated in peripheral populations thus, contributing to conservation and restoration efforts. Chapter 5 extends the analysis in chapter 4 to consider the dynamics of load (due to deleterious mutation accumulation) and extinction risk in a metapopulation. We explore the role of gene flow, selection, and dominance on load and extinction risk and further pinpoint critical thresholds required for metapopulation persistence. Overall this research contributes to our understanding of ecological and evolutionary mechanisms that shape species’ persistence in fragmented landscapes, a crucial foundation for successful conservation efforts and biodiversity management.}, author = {Olusanya, Oluwafunmilola O}, issn = {2663 - 337X}, pages = {183}, publisher = {Institute of Science and Technology Austria}, title = {{Local adaptation, genetic load and extinction in metapopulations}}, doi = {10.15479/at:ista:14711}, year = {2024}, } @phdthesis{14821, author = {Chiossi, Heloisa}, issn = {2663 - 337X}, pages = {89}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive hierarchical representations in the hippocampus}}, doi = {10.15479/at:ista:14821}, year = {2024}, } @phdthesis{15020, abstract = {This thesis consists of four distinct pieces of work within theoretical biology, with two themes in common: the concept of optimization in biological systems, and the use of information-theoretic tools to quantify biological stochasticity and statistical uncertainty. Chapter 2 develops a statistical framework for studying biological systems which we believe to be optimized for a particular utility function, such as retinal neurons conveying information about visual stimuli. We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the expected utility. We explore how such priors aid inference of system parameters with limited data and enable optimality hypothesis testing: is the utility higher than by chance? Chapter 3 examines the ultimate biological optimization process: evolution by natural selection. As some individuals survive and reproduce more successfully than others, populations evolve towards fitter genotypes and phenotypes. We formalize this as accumulation of genetic information, and use population genetics theory to study how much such information can be accumulated per generation and maintained in the face of random mutation and genetic drift. We identify the population size and fitness variance as the key quantities that control information accumulation and maintenance. Chapter 4 reuses the concept of genetic information from Chapter 3, but from a different perspective: we ask how much genetic information organisms actually need, in particular in the context of gene regulation. For example, how much information is needed to bind transcription factors at correct locations within the genome? Population genetics provides us with a refined answer: with an increasing population size, populations achieve higher fitness by maintaining more genetic information. Moreover, regulatory parameters experience selection pressure to optimize the fitness-information trade-off, i.e. minimize the information needed for a given fitness. This provides an evolutionary derivation of the optimization priors introduced in Chapter 2. Chapter 5 proves an upper bound on mutual information between a signal and a communication channel output (such as neural activity). Mutual information is an important utility measure for biological systems, but its practical use can be difficult due to the large dimensionality of many biological channels. Sometimes, a lower bound on mutual information is computed by replacing the high-dimensional channel outputs with decodes (signal estimates). Our result provides a corresponding upper bound, provided that the decodes are the maximum posterior estimates of the signal.}, author = {Hledik, Michal}, issn = {2663 - 337X}, keywords = {Theoretical biology, Optimality, Evolution, Information}, pages = {158}, publisher = {Institute of Science and Technology Austria}, title = {{Genetic information and biological optimization}}, doi = {10.15479/at:ista:15020}, year = {2024}, } @phdthesis{15101, author = {Chen, JingJing}, issn = {2663 - 337X}, pages = {84}, publisher = {Institute of Science and Technology Austria}, title = {{Developmental transformation of nanodomain coupling between Ca2+ channels and release sensors at a central GABAergic synapse}}, doi = {10.15479/at:ista:15101}, year = {2024}, } @phdthesis{15094, abstract = {Point sets, geometric networks, and arrangements of hyperplanes are fundamental objects in discrete geometry that have captivated mathematicians for centuries, if not millennia. This thesis seeks to cast new light on these structures by illustrating specific instances where a topological perspective, specifically through discrete Morse theory and persistent homology, provides valuable insights. At first glance, the topology of these geometric objects might seem uneventful: point sets essentially lack of topology, arrangements of hyperplanes are a decomposition of Rd, which is a contractible space, and the topology of a network primarily involves the enumeration of connected components and cycles within the network. However, beneath this apparent simplicity, there lies an array of intriguing structures, a small subset of which will be uncovered in this thesis. Focused on three case studies, each addressing one of the mentioned objects, this work will showcase connections that intertwine topology with diverse fields such as combinatorial geometry, algorithms and data structures, and emerging applications like spatial biology. }, author = {Cultrera di Montesano, Sebastiano}, issn = {2663 - 337X}, pages = {108}, publisher = {Institute of Science and Technology Austria}, title = {{Persistence and Morse theory for discrete geometric structures}}, doi = {10.15479/at:ista:15094}, year = {2024}, } @phdthesis{12716, abstract = {The process of detecting and evaluating sensory information to guide behaviour is termed perceptual decision-making (PDM), and is critical for the ability of an organism to interact with its external world. Individuals with autism, a neurodevelopmental condition primarily characterised by social and communication difficulties, frequently exhibit altered sensory processing and PDM difficulties are widely reported. Recent technological advancements have pushed forward our understanding of the genetic changes accompanying this condition, however our understanding of how these mutations affect the function of specific neuronal circuits and bring about the corresponding behavioural changes remains limited. Here, we use an innate PDM task, the looming avoidance response (LAR) paradigm, to identify a convergent behavioural abnormality across three molecularly distinct genetic mouse models of autism (Cul3, Setd5 and Ptchd1). Although mutant mice can rapidly detect threatening visual stimuli, their responses are consistently delayed, requiring longer to initiate an appropriate response than their wild-type siblings. Mutant animals show abnormal adaptation in both their stimulus- evoked escape responses and exploratory dynamics following repeated stimulus presentations. Similarly delayed behavioural responses are observed in wild-type animals when faced with more ambiguous threats, suggesting the mutant phenotype could arise from a dysfunction in the flexible control of this PDM process. Our knowledge of the core neuronal circuitry mediating the LAR facilitated a detailed dissection of the neuronal mechanisms underlying the behavioural impairment. In vivo extracellular recording revealed that visual responses were unaffected within a key brain region for the rapid processing of visual threats, the superior colliculus (SC), indicating that the behavioural delay was unlikely to originate from sensory impairments. Delayed behavioural responses were recapitulated in the Setd5 model following optogenetic stimulation of the excitatory output neurons of the SC, which are known to mediate escape initiation through the activation of cells in the underlying dorsal periaqueductal grey (dPAG). In vitro patch-clamp recordings of dPAG cells uncovered a stark hypoexcitability phenotype in two out of the three genetic models investigated (Setd5 and Ptchd1), that in Setd5, is mediated by the misregulation of voltage-gated potassium channels. Overall, our results show that the ability to use visual information to drive efficient escape responses is impaired in three diverse genetic mouse models of autism and that, in one of the models studied, this behavioural delay likely originates from differences in the intrinsic excitability of a key subcortical node, the dPAG. Furthermore, this work showcases the use of an innate behavioural paradigm to mechanistically dissect PDM processes in autism.}, author = {Burnett, Laura}, issn = {2663-337X}, pages = {178}, publisher = {Institute of Science and Technology Austria}, title = {{To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism}}, doi = {10.15479/at:ista:12716}, 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{12826, abstract = {During navigation, animals can infer the structure of the environment by computing the optic flow cues elicited by their own movements, and subsequently use this information to instruct proper locomotor actions. These computations require a panoramic assessment of the visual environment in order to disambiguate similar sensory experiences that may require distinct behavioral responses. The estimation of the global motion patterns is therefore essential for successful navigation. Yet, our understanding of the algorithms and implementations that enable coherent panoramic visual perception remains scarce. Here I pursue this problem by dissecting the functional aspects of interneuronal communication in the lobula plate tangential cell network in Drosophila melanogaster. The results presented in the thesis demonstrate that the basis for effective interpretation of the optic flow in this circuit are stereotyped synaptic connections that mediate the formation of distinct subnetworks, each extracting a particular pattern of global motion. Firstly, I show that gap junctions are essential for a correct interpretation of binocular motion cues by horizontal motion-sensitive cells. HS cells form electrical synapses with contralateral H2 neurons that are involved in detecting yaw rotation and translation. I developed an FlpStop-mediated mutant of a gap junction protein ShakB that disrupts these electrical synapses. While the loss of electrical synapses does not affect the tuning of the direction selectivity in HS neurons, it severely alters their sensitivity to horizontal motion in the contralateral side. These physiological changes result in an inappropriate integration of binocular motion cues in walking animals. While wild-type flies form a binocular perception of visual motion by non-linear integration of monocular optic flow cues, the mutant flies sum the monocular inputs linearly. These results indicate that rather than averaging signals in neighboring neurons, gap-junctions operate in conjunction with chemical synapses to mediate complex non-linear optic flow computations. Secondly, I show that stochastic manipulation of neuronal activity in the lobula plate tangential cell network is a powerful approach to study the neuronal implementation of optic flow-based navigation in flies. Tangential neurons form multiple subnetworks, each mediating course-stabilizing response to a particular global pattern of visual motion. Application of genetic mosaic techniques can provide sparse optogenetic activation of HS cells in numerous combinations. These distinct combinations of activated neurons drive an array of distinct behavioral responses, providing important insights into how visuomotor transformation is performed in the lobula plate tangential cell network. This approach can be complemented by stochastic silencing of tangential neurons, enabling direct assessment of the functional role of individual tangential neurons in the processing of specific visual motion patterns. Taken together, the findings presented in this thesis suggest that establishing specific activity patterns of tangential cells via stereotyped synaptic connectivity is a key to efficient optic flow-based navigation in Drosophila melanogaster.}, author = {Pokusaeva, Victoria}, issn = {2663 - 337X}, pages = {106}, publisher = {Institute of Science and Technology Austria}, title = {{Neural control of optic flow-based navigation in Drosophila melanogaster}}, doi = {10.15479/at:ista:12826}, 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}, } @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{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{12885, abstract = {High-performance semiconductors rely upon precise control of heat and charge transport. This can be achieved by precisely engineering defects in polycrystalline solids. There are multiple approaches to preparing such polycrystalline semiconductors, and the transformation of solution-processed colloidal nanoparticles is appealing because colloidal nanoparticles combine low cost with structural and compositional tunability along with rich surface chemistry. However, the multiple processes from nanoparticle synthesis to the final bulk nanocomposites are very complex. They involve nanoparticle purification, post-synthetic modifications, and finally consolidation (thermal treatments and densification). All these properties dictate the final material’s composition and microstructure, ultimately affecting its functional properties. This thesis explores the synthesis, surface chemistry and consolidation of colloidal semiconductor nanoparticles into dense solids. In particular, the transformations that take place during these processes, and their effect on the material’s transport properties are evaluated. }, author = {Calcabrini, Mariano}, isbn = {978-3-99078-028-2}, issn = {2663-337X}, pages = {82}, publisher = {Institute of Science and Technology Austria}, title = {{Nanoparticle-based semiconductor solids: From synthesis to consolidation}}, doi = {10.15479/at:ista:12885}, 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{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{12732, abstract = {Nonergodic systems, whose out-of-equilibrium dynamics fail to thermalize, provide a fascinating research direction both for fundamental reasons and for application in state of the art quantum devices. Going beyond the description of statistical mechanics, ergodicity breaking yields a new paradigm in quantum many-body physics, introducing novel phases of matter with no counterpart at equilibrium. In this Thesis, we address different open questions in the field, focusing on disorder-induced many-body localization (MBL) and on weak ergodicity breaking in kinetically constrained models. In particular, we contribute to the debate about transport in kinetically constrained models, studying the effect of $U(1)$ conservation and inversion-symmetry breaking in a family of quantum East models. Using tensor network techniques, we analyze the dynamics of large MBL systems beyond the limit of exact numerical methods. In this setting, we approach the debated topic of the coexistence of localized and thermal eigenstates separated by energy thresholds known as many-body mobility edges. Inspired by recent experiments, our work further investigates the localization of a small bath induced by the coupling to a large localized chain, the so-called MBL proximity effect. In the first Chapter, we introduce a family of particle-conserving kinetically constrained models, inspired by the quantum East model. The system we study features strong inversion-symmetry breaking, due to the nature of the correlated hopping. We show that these models host so-called quantum Hilbert space fragmentation, consisting of disconnected subsectors in an entangled basis, and further provide an analytical description of this phenomenon. We further probe its effect on dynamics of simple product states, showing revivals in fidelity and local observalbes. The study of dynamics within the largest subsector reveals an anomalous transient superdiffusive behavior crossing over to slow logarithmic dynamics at later times. This work suggests that particle conserving constrained models with inversion-symmetry breaking realize new universality classes of dynamics and invite their further theoretical and experimental studies. Next, we use kinetic constraints and disorder to design a model with many-body mobility edges in particle density. This feature allows to study the dynamics of localized and thermal states in large systems beyond the limitations of previous studies. The time-evolution shows typical signatures of localization at small densities, replaced by thermal behavior at larger densities. Our results provide evidence in favor of the stability of many-body mobility edges, which was recently challenged by a theoretical argument. To support our findings, we probe the mechanism proposed as a cause of delocalization in many-body localized systems with mobility edges suggesting its ineffectiveness in the model studied. In the last Chapter of this Thesis, we address the topic of many-body localization proximity effect. We study a model inspired by recent experiments, featuring Anderson localized coupled to a small bath of free hard-core bosons. The interaction among the two particle species results in non-trivial dynamics, which we probe using tensor network techniques. Our simulations show convincing evidence of many-body localization proximity effect when the bath is composed by a single free particle and interactions are strong. We furthter observe an anomalous entanglement dynamics, which we explain through a phenomenological theory. Finally, we extract highly excited eigenstates of large systems, providing supplementary evidence in favor of our findings.}, author = {Brighi, Pietro}, issn = {2663-337X}, pages = {158}, publisher = {Institute of Science and Technology Austria}, title = {{Ergodicity breaking in disordered and kinetically constrained quantum many-body systems}}, doi = {10.15479/at:ista:12732}, year = {2023}, } @phdthesis{13081, abstract = {During development, tissues undergo changes in size and shape to form functional organs. Distinct cellular processes such as cell division and cell rearrangements underlie tissue morphogenesis. Yet how the distinct processes are controlled and coordinated, and how they contribute to morphogenesis is poorly understood. In our study, we addressed these questions using the developing mouse neural tube. This epithelial organ transforms from a flat epithelial sheet to an epithelial tube while increasing in size and undergoing morpho-gen-mediated patterning. The extent and mechanism of neural progenitor rearrangement within the developing mouse neuroepithelium is unknown. To investigate this, we per-formed high resolution lineage tracing analysis to quantify the extent of epithelial rear-rangement at different stages of neural tube development. We quantitatively described the relationship between apical cell size with cell cycle dependent interkinetic nuclear migra-tions (IKNM) and performed high cellular resolution live imaging of the neuroepithelium to study the dynamics of junctional remodeling. Furthermore, developed a vertex model of the neuroepithelium to investigate the quantitative contribution of cell proliferation, cell differentiation and mechanical properties to the epithelial rearrangement dynamics and validated the model predictions through functional experiments. Our analysis revealed that at early developmental stages, the apical cell area kinetics driven by IKNM induce high lev-els of cell rearrangements in a regime of high junctional tension and contractility. After E9.5, there is a sharp decline in the extent of cell rearrangements, suggesting that the epi-thelium transitions from a fluid-like to a solid-like state. We found that this transition is regulated by the growth rate of the tissue, rather than by changes in cell-cell adhesion and contractile forces. Overall, our study provides a quantitative description of the relationship between tissue growth, cell cycle dynamics, epithelia rearrangements and the emergent tissue material properties, and novel insights on how epithelial cell dynamics influences tissue morphogenesis.}, author = {Bocanegra, Laura}, issn = {2663 - 337X}, pages = {93}, publisher = {Institute of Science and Technology Austria}, title = {{Epithelial dynamics during mouse neural tube development}}, doi = {10.15479/at:ista:13081}, year = {2023}, } @phdthesis{13331, abstract = {The extension of extremal combinatorics to the setting of exterior algebra is a work in progress that gained attention recently. In this thesis, we study the combinatorial structure of exterior algebra by introducing a dictionary that translates the notions from the set systems into the framework of exterior algebra. We show both generalizations of celebrated Erdös--Ko--Rado theorem and Hilton--Milner theorem to the setting of exterior algebra in the simplest non-trivial case of two-forms. }, author = {Köse, Seyda}, issn = {2791-4585}, pages = {26}, publisher = {Institute of Science and Technology Austria}, title = {{Exterior algebra and combinatorics}}, doi = {10.15479/at:ista:13331}, 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{14374, abstract = {Superconductivity has many important applications ranging from levitating trains over qubits to MRI scanners. The phenomenon is successfully modeled by Bardeen-Cooper-Schrieffer (BCS) theory. From a mathematical perspective, BCS theory has been studied extensively for systems without boundary. However, little is known in the presence of boundaries. With the help of numerical methods physicists observed that the critical temperature may increase in the presence of a boundary. The goal of this thesis is to understand the influence of boundaries on the critical temperature in BCS theory and to give a first rigorous justification of these observations. On the way, we also study two-body Schrödinger operators on domains with boundaries and prove additional results for superconductors without boundary. BCS theory is based on a non-linear functional, where the minimizer indicates whether the system is superconducting or in the normal, non-superconducting state. By considering the Hessian of the BCS functional at the normal state, one can analyze whether the normal state is possibly a minimum of the BCS functional and estimate the critical temperature. The Hessian turns out to be a linear operator resembling a Schrödinger operator for two interacting particles, but with more complicated kinetic energy. As a first step, we study the two-body Schrödinger operator in the presence of boundaries. For Neumann boundary conditions, we prove that the addition of a boundary can create new eigenvalues, which correspond to the two particles forming a bound state close to the boundary. Second, we need to understand superconductivity in the translation invariant setting. While in three dimensions this has been extensively studied, there is no mathematical literature for the one and two dimensional cases. In dimensions one and two, we compute the weak coupling asymptotics of the critical temperature and the energy gap in the translation invariant setting. We also prove that their ratio is independent of the microscopic details of the model in the weak coupling limit; this property is referred to as universality. In the third part, we study the critical temperature of superconductors in the presence of boundaries. We start by considering the one-dimensional case of a half-line with contact interaction. Then, we generalize the results to generic interactions and half-spaces in one, two and three dimensions. Finally, we compare the critical temperature of a quarter space in two dimensions to the critical temperatures of a half-space and of the full space.}, author = {Roos, Barbara}, issn = {2663 - 337X}, pages = {206}, publisher = {Institute of Science and Technology Austria}, title = {{Boundary superconductivity in BCS theory}}, doi = {10.15479/at:ista:14374}, year = {2023}, }