@phdthesis{21360, author = {Riegler, Stefan}, issn = {2663-337X}, pages = {185}, publisher = {Institute of Science and Technology Austria}, title = {{Root system plasticity under nutrient limitation : Investigating hormonal and molecular drivers in Arabidopsis thaliana and Coffea species}}, doi = {10.15479/AT-ISTA-21360}, year = {2026}, } @phdthesis{21423, author = {Dunajova, Zuzana}, isbn = {978-3-99078-076-3}, issn = {2663-337X}, pages = {110}, publisher = {Institute of Science and Technology Austria}, title = {{Geometry-driven self-organization of migrating cells and chiral filaments}}, doi = {10.15479/AT-ISTA-21423}, year = {2026}, } @phdthesis{21393, abstract = {This thesis documents a voyage towards truth and beauty via formal verification of theorems. To this end, we develop libraries in Lean 4 that present definitions and results from diverse areas of MathematiCS (i.e., Mathematics and Computer Science). The aim is to create code that is understandable, believable, useful, and elegant. The code should stand for itself as much as possible without a need for documentation; however, this text redundantly documents our code artifacts and provides additional context that isn’t present in the code. This thesis is written for readers who know Lean 4 but are not familiar with any of the topics presented. We manifest truth and beauty in three formalized areas of MathematiCS. We formalize general grammars in Lean 4 and use grammars to show closure of the class of type-0 languages under four operations; union, reversal, concatenation, and the Kleene star. Our second stop is the theory of optimization. Farkas established that a system of linear inequalities has a solution if and only if we cannot obtain a contradiction by taking a linear combination of the inequalities. We state and formally prove several Farkas-like theorems over linearly ordered fields in Lean 4. Furthermore, we extend duality theory to the case when some coefficients are allowed to take “infinite values”. Additionally, we develop the basics of the theory of optimization in terms of the framework called General-Valued Constraint Satisfaction Problems, and we prove that, if a Rational-Valued Constraint Satisfaction Problem template has symmetric fractional polymorphisms of all arities, then its basic LP relaxation is tight. Our third stop is matroid theory. Seymour’s decomposition theorem is a hallmark result in matroid theory, presenting a structural characterization of the class of regular matroids. We aim to formally verify Seymour’s theorem in Lean 4. First, we build a library for working with totally unimodular matrices. We define binary matroids and their standard representations, and we prove that they form a matroid in the sense how Mathlib defines matroids. We define regular matroids to be matroids for which there exists a full representation rational matrix that is totally unimodular, and we prove that all regular matroids are binary. We define 1-sum, 2-sum, and 3 sum of binary matroids as specific ways to compose their standard representation matrices. We prove that the 1-sum, the 2-sum, and the 3-sum of regular matroids are a regular matroid, which concludes the composition direction of the Seymour’s theorem. The (more difficult) decomposition direction remains unproved. In the pursuit of truth, we focus on identifying the trusted code in each project and presenting it faithfully. We emphasize the readability and believability of definitions rather than choosing definitions that are easier to work with. In search for beauty, we focus on the philosophical framework of Roger Scruton, who emphasizes that beauty is not a mere decoration but, most importantly, beauty is the means for shaping our place in the world and a source of redemption, where it can be viewed as a substitute for religion.}, author = {Dvorak, Martin}, isbn = {978-3-99078-074-9}, issn = {2663-337X}, pages = {160}, publisher = {Institute of Science and Technology Austria}, title = {{Pursuit of truth and beauty in Lean 4 : Formally verified theory of grammars, optimization, matroids}}, doi = {10.15479/AT-ISTA-21393}, year = {2026}, } @phdthesis{21198, abstract = {In recent years there has been a massive increase in the amount of data generated in a decentralized manner. Ever more powerful edge devices, such as smartphones, have become ubiquitous in most societies on earth. Through text typed, photos taken and apps used, these devices, which we refer to as clients, generate enormous amounts of high quality and complex data. Moreover, the nature of these devices means the data they generate is often sensitive and privacy concerns prevent it being gathered and stored in a central location. This presents a challenge to the modern machine learning paradigm that requires central access to large amounts of data. Federated learning (FL) has emerged as one of the answers to this problem. Rather than bringing the data to the model, FL sends the model to the data. Model training takes place on device, with periodically synchronized updates, allowing data to remain locally stored. While this approach offers significant privacy advantages it comes with its own set of unique challenges. These include: data heterogeneity, the notion that different devices generate data in distinct ways which can negatively impact training dynamics; systems heterogeneity, meaning that different devices may have differing hardware specifications; high communication costs, which are induced by the repeated transferring of models over the network and low device computational power, which limits the use of larger models on device. In this thesis we present a range of methods for federated learning. We focus primarily on the challenge of data heterogeneity, though the methods presented are designed to be well adapted to the other challenges of a federated setting, such as the constraints of limited compute and communication overhead. We first present a method for explicitly modeling client data heterogeneity. The approach formulates clients as samples from a certain probability distribution and infers the parameters of this distribution from the available training clients. This learned distribution then represents the heterogeneity present among the clients and can be sampled from in order to create new simulated clients that are similar to the real clients we have observed so far. Following this we present two methods for directly dealing with data heterogeneity through personalization. Highly heterogeneous client data distributions can mean that learning a single global model becomes suboptimal, and some form of personalization of models to each individual client is required. Our approaches are based around hypernetworks, which we use to generate personalized model parameters without the need for additional training or finetuning. In the first approach we focus on generating full parameterizations of client models using learned embeddings of client data and labels, with a hypernetwork located on the central server. In the second approach we address the more challenging scenario where we want to generate a personalized model for a client without any label information. The hypernetwork is trained to generate a low dimensional representation of a client’s personalized model parameters, allowing it to be transferred to and run on the client devices. In our final presented method, we change our focus and rather than aim to directly address the challenge of data heterogeneity, we instead ensure we are unaffected by it. This is done in the context of k-means clustering and we present a method for federated clustering with a focus on added privacy guarantees.}, author = {Scott, Jonathan A}, issn = {2663-337X}, pages = {158}, publisher = {Institute of Science and Technology Austria}, title = {{Data heterogeneity and personalization in federated learning}}, doi = {10.15479/AT-ISTA-21198}, year = {2026}, } @phdthesis{21021, abstract = {This thesis examines how geometry and topology intersect in the representation, transformation, and analysis of complex shapes. It considers how continuous manifolds relate to their discrete analogues, how topological structures evolve in persistence vineyards, and how tools from topological data analysis can illuminate problems in mathematical physics. Central to this exploration is the question of how structure, both geometric and topological, persists or changes under approximation, sampling, or deformation. The work develops new approaches to skeletal and grid-based representations of surfaces, reveals the full expressive capacity of persistence vineyards, and applies topological methods to the longstanding problem of equilibria in electrostatic fields. These threads braid together into a broader understanding of how topology and geometry inform one another across theory, computation, and application.}, author = {Fillmore, Christopher D}, issn = {2663-337X}, pages = {122}, publisher = {Institute of Science and Technology Austria}, title = {{Braiding geometry and topology to study shapes and data}}, doi = {10.15479/AT-ISTA-21021}, year = {2026}, } @phdthesis{21651, abstract = {Blockchains enable distributed consensus in permissionless settings, where participants are unknown, dynamically changing, and do not trust each other. While Bitcoin, based on Proof-of-Work (PoW), was the first protocol in this model, significant research has focused on permissionless protocols using alternative physical resources, specifically Proof-of-Space (PoSpace) and Verifiable Delay Functions (VDFs). This thesis investigates the theoretical limits and design space of longest-chain protocols in the fully permissionless and dynamically available settings using these three resources. First, we address the feasibility of blockchains relying solely on storage as a resource. We prove a fundamental impossibility result: there exists no secure longest-chain protocol based exclusively on Proof-of-Space in the fully permissionless or dynamically available settings. Further, we quantify the adversarial capabilities required to execute a double-spend attack. Our result formally justifies the necessity of coupling PoSpace with time-dependent primitives (such as VDFs) or to move to less permissive settings (quasi-permissionless or permissioned) to ensure security. Second, we generalize Nakamoto-like heaviest chain consensus to protocols utilizing combinations of multiple physical resources. We analyze chain selection rules governed by a weight function Γ(S, V,W), which assigns weight to blocks based on recorded Space (S), VDF speed (V ), and Work (W). We provide a complete classification of secure weight functions, proving that a weight function is secure against private double-spend attacks if and only if it is homogeneous in the timed resources (V,W) and sub-homogeneous in S. This framework unifies existing protocols like Bitcoin and Chia under a single theoretical model and provides a powerful tool for designing new longest-chain blockchains from a mix of physical resources.}, author = {Baig, Mirza Ahad}, isbn = {978-3-99078-078-7}, issn = {2663-337X}, publisher = {Institute of Science and Technology Austria}, title = {{On secure chain selection rules from physical resources in a permissionless setting}}, doi = {10.15479/AT-ISTA-21651}, year = {2026}, } @phdthesis{20991, abstract = {Rapid local adaptation to new environments is critical for species persistence, especially in introduced populations. The evolutionary success of these populations is fundamentally dictated by the organization of genetic variation—the genomic architecture—in the face of severe demographic constraints, such as the founder effects and genetic bottlenecks that frequently accompany colonization. A central question in evolutionary biology is whether rapid adaptation relies on major-effect loci, such as chromosomal inversions, or on many small-effect loci dispersed across the genome. Furthermore, the genomic architecture strongly influences the extent to which evolutionary outcomes are predictable. Using introduced populations of the marine snail, Littorina saxatilis, as a model, this thesis investigates how genetic variation and genomic structure drive adaptation following introduction. We employed a population genomics approach on experimentally and accidentally introduced populations to dissect the specific genomic features that underpin divergence in newly colonized environments. In Chapter 2, we tested the predictability of local adaptation through an uncommon 30-year transplant experiment in nature. By distinguishing allele and chromosomal inversion frequency changes from neutral expectations, we found that evolutionary change was highly predictable at the macro-scale (phenotypes and chromosomal inversions), but less robust at the level of individual collinear loci. This result demonstrates that evolution can be predictable when a population possesses sufficient standing genetic variation (SGV), with chromosomal inversions acting as key integrated units that facilitate a rapid response to selection. Building on this, Chapter 3 applied whole-genome sequencing to three accidentally introduced populations (Venice, San Francisco, and Redwood City) to investigate their likely source and genomic patterns of divergence. We identified genomic regions of remarkable divergence potentially associated with local adaptation, and likely fuelled by SGV, while explicitly acknowledging the difficulty in disentangling selection signals from the genome-wide effects of demographic processes. Furthermore, we found that the divergence patterns relied extensively on the collinear genome in these introduced populations, and less clearly on the chromosomal inversions. This observation contrasts with local adaptation observed in the experimental system that relied on both collinear loci and highly selected chromosomal inversions, highlighting how demographic history and genomic architecture influence the detectable signature of local adaptation. A major limitation to conducting large-scale comparative evolutionary studies is the lack of data standardization, which prevents the integration of community knowledge and high-resolution environmental and genetic data. Chapter 4 addresses this by developing a community database for the Littorina system. This platform implements standardized protocols for the integration of diverse phenotypic and environmental data from multiple Littorina species. Likewise, the platform also centralizes the availability of associated genomic data through links to external repositories. This database represents a crucial tool to test complex, large-scale evolutionary hypotheses. Collectively, this thesis strongly reinforces the fundamental importance of SGV as the raw material for successful local adaptation, a conclusion supported by evidence in both experimental and accidental introductions. Furthermore, this work highlights the critical role of the genomic architecture—specifically chromosomal inversions—in driving the predictability and effectiveness of adaptive responses. Our findings underscore how the interplay between SGV and genomic architecture dictates the trajectory and detectability of evolution in colonizing populations, while simultaneously providing a necessary tool to advance comparative evolutionary genomics in emerging model organisms.}, author = {Garcia Castillo, Diego Fernando}, isbn = {978-3-99078-077-0}, issn = {2663-337X}, pages = {199}, publisher = {Institute of Science and Technology Austria}, title = {{The genomic architecture of local adaptation in introduced populations}}, doi = {10.15479/AT-ISTA-20991}, year = {2026}, } @phdthesis{19684, abstract = {The overarching goal of this thesis is to break down the complexity of turbulent flows in terms of enumerable, coherent structures and patterns. In a five-paper series, we adopt a variety of perspectives and techniques to relate the properties of systems of increasing complexity to their underlying coherent structures. Initially, we take a dynamical systems point of view, seeing turbulent flow as a chaotic trajectory bouncing between exact unstable solutions of the underlying equations of motion. Using persistent homology, the main tool of topological data analysis capturing the persistence across scales of topological features in a point cloud, we introduce a method that quantifies visits of turbulent trajectories to unstable time-periodic solutions, also called periodic orbits. We demonstrate this method first in the Rössler and Kuramoto–Sivashinsky systems. Using this method in 3D Kolmogorov flow, we extract a Markov chain from turbulent data, where each node corresponds to the neighbourhood of a periodic orbit. The invariant distribution of this Markov chain reproduces expectation values on turbulent data when it is used to weight averages on the respective periodic orbits. In more realistic, wall-bounded settings, such as plane-Couette flow (pcf) driven by the relative motion of the walls, or plane-Poiseuille flow (ppf) driven by a pressure gradient, finding exact solutions is difficult. We use dynamic mode decomposition (DMD), a dimensionality reduction method for sequential data, to identify and approximate low-dimensional dynamics without knowing any exact solutions. Most spatially-extended systems are equivariant under translations, and in such cases spatial drifts dominate DMD, hindering its use in the search for and modelling of low-dimensional dynamics. We augment DMD with a symmetry reduction method trained on turbulent data to stop it from seeing translations as a feature, improving its ability to extract dynamical information in translation-equivariant systems. We find segments of turbulent trajectories that linearize well with their symmetry-reduced DMD spectra, akin to dynamics near exact solutions. Searching for harmonics in the spectra gives leads for periodic orbits with spatial drifts, one of which converges to a new solution. In larger domains, turbulence can localize and coexist with surrounding laminar flow. Our preceding approaches are global, taking all of a domain into account at once, and cannot readily treat each localized patch individually. Working first in a minimal oblique domain that can host a single 1D-localized turbulent patch, we find that turbulence in ppf is connected to a stable periodic orbit at a flow velocity much lower than when turbulence is first onset. We show that, well in advance of sustained turbulence, chaos sets in explosively, and for long time horizons, time series are consistent with that of a random process. Finally, in much larger domains, we study and compare 2D-localized turbulence that appears as large-scale inclined structures, called stripes, in ppf and pcf. While appearing similar, we find that stripes in these two settings differ significantly in terms of how they sustain themselves, and in higher velocities, how they proliferate.}, author = {Yalniz, Gökhan}, issn = {2663-337X}, pages = {155}, publisher = {Institute of Science and Technology Austria}, title = {{Transition to turbulence : Data-, solution-, and pattern-driven approaches}}, doi = {10.15479/AT-ISTA-19684}, year = {2025}, } @phdthesis{18979, abstract = {Topological Data Analysis (TDA) is a discipline utilizing the mathematical field of topology to study data, most prominently collections of point sets. This thesis summarizes three projects related to computations in TDA. The first one establishes a variant of TDA for chromatic point sets, where each point is given a color. For example, we are given positions of cells within a tumor microenvironment, and color the cancerous cells red, and the immune cells blue. The aim is then to give a quantitative description of how the two or more sets of points spatially interact. Building on image, kernel and cokernel variants of persistent homology, we suggest six-packs of persistent diagrams as such a descriptor. We describe a construction of a chromatic alpha complex, which enables efficient computation of several variants of the six-packs. We give topological descriptions of natural subcomplexes of the chromatic alpha complex, and show that the radii of the simplices form a discrete Morse function. Finally, we provide an implementation of the presented chromatic TDA pipeline. The second part aims to translate a powerful tool of sheaf theory to elementary terms using labeled matrices. The goal is to enable their use in computational settings. We show that derived categories of sheaves over finite posets have, up to isomorphism, unique objects---minimal injective resolutions---and give a concrete algorithm to compute them. We further describe simple algorithms to compute derived pushforwards and pullbacks for monotonic maps, and their proper variants for inclusions, and demonstrate their tractability by providing an implementation. Finally, we suggest a discrete definition of microsupport and show desirable properties inspired by discrete Morse theory. In the last part, we present a collection of observations about collapses. We give a characterization of collapsibility in terms of unitriangular submatrices of the boundary matrix, a cotree-tree decomposition, and the optimal solution to a variant of the Procrustes problem. We establish relation between dual collapses and relative Morse theory and pose several open questions. Finally, focusing on complexes embedded in the three-dimensional Euclidean space, we describe a relation between the collapsibility and the triviality of a polygonal knot.}, author = {Draganov, Ondrej}, issn = {2663-337X}, keywords = {topological data analysis, chromatic point set, alpha complex, persistent homology, six pack, sheaf, microlocal discrete Morse, injective resolution, collapse, knot, discrete Morse theory}, pages = {140}, publisher = {Institute of Science and Technology Austria}, title = {{Structures and computations in topological data analysis}}, doi = {10.15479/at:ista:18979}, year = {2025}, } @phdthesis{20206, abstract = {The internal structure of biomolecules and their organization in higher-order arrangements are key factors governing the working principles of biological systems. Bioimaging has successfully revealed arrangements across relevant spatial scales. For example, cryo-electron tomography has become widely used for analyzing biomolecular structures in situ due to its comprehensive structural visualization of near-natively preserved samples, and its capability of sub-nm resolution via averaging. However, the identification of molecules within crowded cellular environments is often hindered by low contrast. Fluorescence microscopy, on the other hand, routinely visualizes specifically labeled targets at single-molecule contrast against essentially zero background. Moreover, it provides comparatively high throughput and is amenable to multiplexing. Due to this complementarity, combining datasets from both modalities acquired on the same region via correlative light and electron microscopy can reveal novel types of information. The spatial scale at which information can be extracted depends on imaging resolution and correlation accuracy. Since diffraction of light limits the resolution of conventional fluorescence microscopy to few hundreds of nanometers, reaching the full potential of correlative imaging requires super-resolution approaches. Performing imaging at cryogenic temperature preserves structures in a near-native state and minimizes distortions between the fluorescence and the electron microscopy datasets. Implementations of this concept have achieved correlation on the scale of cellular organelles or bacterial domains. We have worked towards pushing correlative imaging to the single-molecule scale by improving cryo-super-resolution microscopy, and devising a refined image correlation workflow. As part of this project, I constructed a microscopy setup and adopted it for super-resolution fluorescence microscopy at room temperature and cryogenic conditions. I explored different cryo-stages and acquisition strategies. Specifically, I developed a new scheme for correcting sample drift, thus increasing mechanical stability during microscopy acquisitions. }, author = {Vorlaufer, Jakob}, issn = {2663-337X}, pages = {107}, publisher = {Institute of Science and Technology Austria}, title = {{Construction of a cryo-super-resolution microscope to guide in situ structure analysis}}, doi = {10.15479/AT-ISTA-20206}, year = {2025}, } @phdthesis{19722, author = {Inumella, Syamala}, isbn = {978-3-99078-059-6}, issn = {2663-337X}, pages = {113}, publisher = {Institute of Science and Technology Austria}, title = {{Molecular mechanisms of microtubule reorganization in elongating root epidermal cells}}, doi = {10.15479/AT-ISTA-19722}, year = {2025}, } @phdthesis{20138, abstract = {The evolution shapes the world around us. Not only in biology, where the fittest individuals spread their genes but also in physics and social dynamics, the evolutionary forces determine the development of a state of matter or public opinions. Many models describe these dynamics. This thesis examines the role of the structure in the models of selection. The population structure is represented as a graph or a network, and each vertex is occupied by one individual. Every individual has a type and fitness that represents the reproductive potential and depends on the type, occupied vertex, and the arrangement of the neighbors. The evolution is modeled in discrete steps; in one step, one individual is replaced by a neighbor selected randomly with the influence of fitness. The role of the networks is widely examined in the literature. The structures that promote the spread of the desired type compared to the structureless case are called amplifiers. The existence of amplifiers in various settings is an intensively studied topic, and in some settings, the amplifiers have been identified. Moreover, there are other important questions about the number of steps until one type spreads over the whole network (fixation time), the computational complexity, and the questions about the robustness of these processes. This thesis explores the role of structure in evolution from many perspectives. First, it introduces different models and various choices that can be made in the models of evolution. It highlights the role of the structure in the real world and how this is reflected in these models. Then, it describes the previous results and open problems. Second, the thesis describes an amplifier for two variants of the Moran process: one with a constant birth rate and the other with a constant death rate. This is an important contribution to the robustness of the amplification. Third, the thesis determines the complexity of spatial games. These are processes where the fitness comes from a game, and the strength of selection is high. It shows that determining the fate of cooperation in these games is a PSPACE-complete problem. Fourth, the thesis describes the amplifier of cooperation for spatial games. This is the first amplifier in this setting. Fifth, the thesis examines the coexistence in the Moran process with environmental heterogeneity. In this setting, the fitness depends not only on the type of the individual but also on the occupied vertex. The chapter determines the relationship between the interactions of vertices of different types and the coexistence time. Sixth, the thesis examines the social balance on networks and proposes a stochastic dynamic partially aware of the state of the graph, which reaches a balanced position quickly. Finally, the thesis presents conclusions and outlines the directions for future work. }, author = {Svoboda, Jakub}, issn = {2663-337X}, pages = {167}, publisher = {Institute of Science and Technology Austria}, title = {{Structural properties of games on graphs}}, doi = {10.15479/AT-ISTA-20138}, year = {2025}, } @phdthesis{20117, author = {Wang, Yiqun}, issn = {2663-337X}, pages = {108}, publisher = {Institute of Science and Technology Austria}, title = {{The role of dynamin related protein 2A in cytokinin regulated plant growth and development}}, doi = {10.15479/AT-ISTA-20117}, year = {2025}, } @phdthesis{19759, abstract = {Despite generating remarkable results in various computer vision tasks, deep learning comes with some surprising shortcomings. For example, tiny perturbations, often imperceptible to the human eye, can completely change the predictions of image classifiers. Despite a decade of research, the field has made limited progress in developing image classifiers that are both accurate and robust. This thesis aims to address this gap. As our first contribution, we aim to simplify the process of training certifiably robust image classifiers. We do this by designing a convolutional layer that does not require executing an iterative procedure in every forward pass, but relies on an explicit bound instead. We also propose a loss function that allows optimizing for a particular margin more precisely. Next, we provide an overview and comparison of various methods that create robust image classifiers by constraining the Lipschitz constant. This is important since generally longer training times and more parameters improve the performance of robust classifiers, making it challenging to determine the most practical and effective methods from existing literature. In 1-Lipschitz classification, the performance of current methods is still much worse than what we expect on the simple tasks we consider. Therefore, we next investigate potential causes of this shortcoming. We first consider the role of the activation function. We prove a theoretical shortcoming of the commonly used activation function, and provide an alternative without it. However this theoretical improvement does barely translate to the empirical performance of robust classifiers, suggesting a different bottleneck. Therefore, in the final chapter, we study how the performance depends on the amount of training data. We prove that in the worst case, we might require far more data to train a robust classifier compared to a normal one. We furthermore find that the amount of training data is a key determinant of the performance current methods achieve on popular datasets. Additionally, we show that linear subspaces exist with tiny data variance, and yet we can still train very accurate classifiers after projecting into those subspaces. This shows that on the datasets considered, enforcing robustness in classification makes the task strictly more challenging. -----------------“In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of [name of university or educational entity]’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation.” }, author = {Prach, Bernd}, issn = {2663-337X}, pages = {84}, publisher = {Institute of Science and Technology Austria}, title = {{Robust image classification with 1-Lipschitz networks}}, doi = {10.15479/10.15479/at-ista-19759}, year = {2025}, } @phdthesis{20167, author = {Schön, Hanna}, isbn = {978-3-99078-061-9}, issn = {2663-337X}, pages = {171}, publisher = {Institute of Science and Technology Austria}, title = {{The ER complex SUTU-7/MACO-1 regulates the fate of mRNAs encoding GPCRs}}, doi = {10.15479/AT-ISTA-20167}, year = {2025}, } @phdthesis{20362, author = {Babic, David}, issn = {2663-337X}, pages = {116}, publisher = {Institute of Science and Technology Austria}, title = {{Mechanisms of auxin-mediated early embryogenesis in Arabidopsis thaliana}}, doi = {10.15479/AT-ISTA-20362}, year = {2025}, } @phdthesis{20415, author = {Lee, Seungho}, issn = {2663-337X}, pages = {144}, publisher = {Institute of Science and Technology Austria}, title = {{Nanoparticle-based precursors toward advanced crystalline inorganic solids}}, doi = {10.15479/AT-ISTA-20415}, year = {2025}, } @phdthesis{20364, author = {Giannini, Caterina}, issn = {2663-337X}, keywords = {Auxin Signaling, Plant Development}, pages = {151}, publisher = {Institute of Science and Technology Austria}, title = {{Nuclear and cell surface auxin signaling in A. thaliana developmental transitions}}, doi = {10.15479/AT-ISTA-20364}, year = {2025}, } @phdthesis{20441, abstract = {Epithelial spreading plays a pivotal role in the development of organisms especially those such as zebrafish which require the epithelial enveloping layer (EVL) to spread to cover the substantial yolk surface during gastrulation. Epiboly requires the transition of the epithelium with cuboidal cells to form a thin, flat squamous epithelial sheet. During this transition, the cells show tissue-scale mechanosensation with mechanisms such as direct mechanical control over the axis of cell division. Cytoskeletal intermediate filaments play a crucial role in vertebrate cells, not only facilitating mechanical stability but also helping facilitate the mechanosensitive response of the cell. Mechanosenstivity displayed by intermediate filaments is due not just to their interesting physical properties but also to their interactions with other cytoskeletal elements such as actin and microtubules. Keratin is the predominant intermediate filament expressed in the EVL. It expresses concomitantly with the gastrulation movements of the developing embryo. Our work focuses on understanding the role and dynamics of the keratin cytoskeletal network in modulating the physical aspects of EVL spreading. We demonstrated with the combination of physical characterisation and manipulations of the EVL, utilising a variety of biophysical tools and microscopy, the mechanistic role of keratin in tissue spreading. Generating novel genetic morphants and mutants, we probe the effect that the loss of the keratin network has on the physiology of the epithelium and the developing embryo. We show that the changing organisation of the keratin network is important for changing EVL physical properties as the stress imposed on the EVL increases during epiboly. By modelling the epithelium, we study how the mechanical heterogeneity in an epithelium can feed back into a mechanical loop to the maturation of the keratin network and hence affect the mechanics of the epithelium. However, unlike what would be predicted by the effect of intermediate filaments in acting as a security belt and increasing the resistance of the epithelium, we observe that loss of keratin leads to a delay in the EVL movement. Using both local aspirations of the YSL and EVL ablations, we demonstrate the mechanistic facilitation of actin mechanosensation in a keratin-dependent manner. Furthermore, using chemical inhibitors of microtubule polymerisation, we provide insight into the mechanisms underlying the organisation and distribution of keratin. Interestingly, the phenotype observed upon this loss of microtubules shows that keratins interact with the nucleus through microtubular interactions. Together with these diverse observations, we describe the mechanosensory feedback between resilience and that is critical for uniform and robust spreading of the epithelium.}, author = {Naik, Suyash}, isbn = {978-3-99078-069-5}, issn = {2663-337X}, pages = {105}, publisher = {Institute of Science and Technology Austria}, title = {{Keratins act as global coordinators of tissue spreading through mechanosensitive feedback}}, doi = {10.15479/AT-ISTA-20441}, year = {2025}, } @phdthesis{20485, author = {Misova, Michaela}, isbn = {978-3-99078-068-8}, issn = {2663-337X}, pages = {155}, publisher = {Institute of Science and Technology Austria}, title = {{Dissecting gap junction biology using the C. elegans nervous system}}, doi = {10.15479/AT-ISTA-20485}, year = {2025}, }