Computational Design and Experimental Validation of Functional Ribonucleic Acid Nanostructures

In living cells, two major classes of ribonucleic acid (RNA) molecules can be found. The first class called the messenger RNA (mRNA) contains the genetic information that allows the ribosome to read and translate it into proteins. The second class called non-coding RNA (ncRNA), do not code for proteins and are involved with key cellular processes, such as gene expression regulation, splicing, differentiation, and development. NcRNAs fold into an ensemble of thermodynamically stable secondary structures, which will eventually lead the molecule to fold into a specific 3D structure. It is widely known that ncRNAs carry their functions via their 3D structures as well as their molecular composition. The secondary structure of ncRNAs is composed of different types of structural elements (motifs) such as stacking base pairs, internal loops, hairpin loops and pseudoknots. Pseudoknots are specifically difficult to model, are abundant in nature and known to stabilize the functional form of the molecule. Due to the diverse range of functions of ncRNAs, their computational design and analysis have numerous applications in nano-technology, therapeutics, synthetic biology, and materials engineering. The RNA design problem is to find novel RNA sequences that are predicted to fold into target structure(s) while satisfying specific qualitative characteristics and constraints. RNA design can be modeled as a combinatorial optimization problem (COP) and is known to be computationally challenging or more precisely NP-hard. Numerous algorithms to solve the RNA design problem have been developed over the past two decades, however mostly ignore pseudoknots and therefore limit application to only a slice of real-world modeling and design problems. Moreover, the few existing pseudoknot designer methods which were developed only recently, do not provide any evidence about the applicability of their proposed design methodology in biological contexts. The two objectives of this thesis are set to address these two shortcomings. First, we are interested in developing an efficient computational method for the design of RNA secondary structures including pseudoknots that show significantly improved in-silico quality characteristics than the state of the art. Second, we are interested in showing the real-world worthiness of the proposed method by validating it experimentally. More precisely, our aim is to design instances of certain types of RNA enzymes (i.e. ribozymes) and demonstrate that they are functionally active. This would likely only happen if their predicted folding matched their actual folding in the in-vitro experiments. In this thesis, we present four contributions. First, we propose a novel adaptive defect weighted sampling algorithm to efficiently solve the RNA secondary structure design problem where pseudoknots are included. We compare the performance of our design algorithm with the state of the art and show that our method generates molecules that are thermodynamically more stable and less defective than those generated by state of the art methods. Moreover, we show when the effect of fitness evaluation is decoupled from the search and optimization process, our optimization method converges faster than the non-dominated sorting genetic algorithm (NSGA II) and the ant colony optimization (ACO) algorithm do. Second, we use our algorithmic development to implement an RNA design pipeline called Enzymer and make it available as an open source package useful for wet lab practitioners and RNA bioinformaticians. Enzymer uses multiple sequence alignment (MSA) data to generate initial design templates for further optimization. Our design pipeline can then be used to re-engineer naturally occurring RNA enzymes such as ribozymes and riboswitches. Our first and second contributions are published in the RNA section of the Journal of Frontiers in Genetics. Third, we use Enzymer to reengineer three different species of pseudoknotted ribozymes: a hammerhead ribozyme from the mouse gut metagenome, a hammerhead ribozyme from Yarrowia lipolytica and a glmS ribozyme from Thermoanaerobacter tengcogensis. We designed a total of 18 ribozyme sequences and showed the 16 of them were active in-vitro. Our experimental results have been submitted to the RNA journal and strongly suggest that Enzymer is a reliable tool to design pseudoknotted ncRNAs with desired secondary structure. Finally, we propose a novel architecture for a new ribozyme-based gene regulatory network where a hammerhead ribozyme modulates expression of a reporter gene when an external stimulus IPTG is present. Our in-vivo results show expected results in 7 out of 12 cases

Sparse Grid Methods for Higher Dimensional Approximation

Diese Arbeit befasst sich mit Dünngitterverfahren zur Lösung von höherdimensionalen Problemen. Sie zeigt drei neue Aspekte von Dünnen Gittern auf: Erweiterungen der elementaren Werkzeuge zur Arbeit mit Dünnen Gittern, eine Analyse von sowohl inhärenten Einschränkungen als auch Vorteilen von Dünnen Gittern speziell für die Anwendung zur Dichteapproximation (Fokker--Planck--Gleichung) sowie einen neuen Ansatz zur dimensions- und ortsadaptiven Darstellung von Funktionen effektiv niedriger Dimension. Der erste Beitrag beinhaltet die erste (dem Autor bekannte) Fehlerschranke für inhomogene Randbedingungen bei Dünngitterapproximation und eine erweiterte Operationsbibliothek zur Durchführung von Addition, Multiplikation und Hintereinanderausführung von Dünngitterdarstellungen sowie einen adaptiven Kollokationsansatz für approximative Integraltransformationen mit beliebigen Kernen. Die Analyse verwendet Konditionszahlen für den Datenfehler und verallgemeinert damit die bisher bekannten Abschätzungen aus der Literatur. Ferner wird erstmals auch der Konsistenzfehler bei derartigen Operationen berücksichtigt sowie eine adaptive Methode zur Kontrolle desselben vorgeschlagen, die insbesondere zuvor vorhandene Schwachstellen behebt und die Methode verlässlich macht. Der zweite Beitrag ist eine Untersuchung von dimensionsabhängigen Kosten/Nutzen-Koeffizienten, wie sie bei der Lösung von Fokker--Planck--Gleichungen und der damit verbundenen Approximation von Wahrscheinlichkeitsdichten auftreten. Es werden sowohl theoretische Schranken als auch A-posteriori-Fehlermessungen anhand einer repräsentativen Fallstudie für lineare Fokker--Planck--Gleichungen und der Normalverteilung auf Rd vorgestellt und die auftretenden dimensionsabhängigen Koeffizienten bei Interpolation und Bestapproximation (sowohl L2 als auch beim Lösen der Gleichung mittels Galerkin-Verfahren) untersucht. Dabei stehen reguläre Dünne Gitter, adaptive Dünne Gitter und die speziell für die Energienorm optimierten Dünnen Gitter im Vordergrund. Insbesondere werden Schlussfolgerungen auf inhärente Einschränkungen aber auch auf Vorteile gegenüber klassischen Vollgitterverfahren diskutiert. Der dritte Beitrag dieser Arbeit ist der erste Ansatz für dimensionsadaptive Verfeinerung, der insbesondere für Approximationsprobleme konzipiert wurde. Der Ansatz behebt bekannte Schwierigkeiten mit frühzeitiger Terminierung, wie sie bei bisherigen Ansätzen zur Verallgemeinerung der erfolgreichen Dimensionsadaptivität aus dem Bereich Dünngitterquadratur zu beobachten waren. Das Verfahren erlaubt eine systematische Reduktion der Freiheitsgrade für Funktionen, die effektiv nur von wenigen (Teilmengen von) Koordinaten abhängen. Der Ansatz kombiniert die erfolgreiche ortsadaptive Dünngittertechnik aus dem Bereich der Approximation mit der ebenfalls erfolgreichen dimensionsadaptiven Verfeinerung aus dem Bereich der Dünngitterquadratur. Die Abhängigkeit von unterschiedlichen (Teilmengen von) Koordinaten wird mittels gewichteter Räume unter Zuhilfenahme der ANOVA-Zerlegung durchgeführt. Die Arbeit stellt neue a priori optimierte Dünngitterräume vor, die optimale Approximation für Funktionenräume mit gewichteten gemischten zweiten Ableitungen und bekannten Gewichten erlauben. Die Konstruktion liefert die bekannten regulären Dünnen Gitter mit gewichtsabhängigen Leveln für jede Teilmenge von Koordinaten (ANOVA Komponenten). Für unbekannte Gewichte wird eine neue a-posteriori dimensionsadaptive Methode vorgestellt, die im Unterschied zu bekannten Verfahren explizit ANOVA Komponenten ermittelt und berücksichtigt und so höhere Verlässlichkeit beim Einsatz für Approximationsanwendungen erzielt. Neben reiner dimensionsadaptiver Approximation erlaubt das Verfahren auch erstmals gekoppelte orts- und dimensionsadaptive Verfeinerung. Die Arbeit stellt die Methodik dar und verifiziert die Verlässlichkeit anhand dimensionsadaptiver Interpolation und dimensionsadaptiver Lösung partieller Differentialgleichungen./td

Developments in multiscale ONIOM and fragment methods for complex chemical systems

Multiskalenprobleme werden in der Computerchemie immer allgegenwärtiger und bestimmte Klassen solcher Probleme entziehen sich einer effizienten Beschreibung mit den verfügbaren Berechnungsansätzen. In dieser Arbeit wurden effiziente Erweiterungen der Multilayer-Methode ONIOM und von Fragmentmethoden als Lösungsansätze für derartige Probleme entwickelt. Dabei wurde die Kombination von ONIOM und Fragmentmethoden im Rahmen der Multi-Centre Generalised ONIOM entwickelt sowie die eine Multilayer-Variante der Fragment Combinatio Ranges. Außerdem wurden Schemata für elektronische Einbettung derartiger Multilayer-Systeme entwickelt. Der zweite Teil der Arbeit beschreibt die Implementierung im Haskell-Programm "Spicy" und demonstriert Anwendungen derartiger Multiskalen-Methoden

Learning representations for speech recognition using artificial neural networks

Learning representations is a central challenge in machine learning. For speech recognition, we are interested in learning robust representations that are stable across different acoustic environments, recording equipment and irrelevant inter– and intra– speaker variabilities. This thesis is concerned with representation learning for acoustic model adaptation to speakers and environments, construction of acoustic models in low-resource settings, and learning representations from multiple acoustic channels. The investigations are primarily focused on the hybrid approach to acoustic modelling based on hidden Markov models and artificial neural networks (ANN). The first contribution concerns acoustic model adaptation. This comprises two new adaptation transforms operating in ANN parameters space. Both operate at the level of activation functions and treat a trained ANN acoustic model as a canonical set of fixed-basis functions, from which one can later derive variants tailored to the specific distribution present in adaptation data. The first technique, termed Learning Hidden Unit Contributions (LHUC), depends on learning distribution-dependent linear combination coefficients for hidden units. This technique is then extended to altering groups of hidden units with parametric and differentiable pooling operators. We found the proposed adaptation techniques pose many desirable properties: they are relatively low-dimensional, do not overfit and can work in both a supervised and an unsupervised manner. For LHUC we also present extensions to speaker adaptive training and environment factorisation. On average, depending on the characteristics of the test set, 5-25% relative word error rate (WERR) reductions are obtained in an unsupervised two-pass adaptation setting. The second contribution concerns building acoustic models in low-resource data scenarios. In particular, we are concerned with insufficient amounts of transcribed acoustic material for estimating acoustic models in the target language – thus assuming resources like lexicons or texts to estimate language models are available. First we proposed an ANN with a structured output layer which models both context–dependent and context–independent speech units, with the context-independent predictions used at runtime to aid the prediction of context-dependent states. We also propose to perform multi-task adaptation with a structured output layer. We obtain consistent WERR reductions up to 6.4% in low-resource speaker-independent acoustic modelling. Adapting those models in a multi-task manner with LHUC decreases WERRs by an additional 13.6%, compared to 12.7% for non multi-task LHUC. We then demonstrate that one can build better acoustic models with unsupervised multi– and cross– lingual initialisation and find that pre-training is a largely language-independent. Up to 14.4% WERR reductions are observed, depending on the amount of the available transcribed acoustic data in the target language. The third contribution concerns building acoustic models from multi-channel acoustic data. For this purpose we investigate various ways of integrating and learning multi-channel representations. In particular, we investigate channel concatenation and the applicability of convolutional layers for this purpose. We propose a multi-channel convolutional layer with cross-channel pooling, which can be seen as a data-driven non-parametric auditory attention mechanism. We find that for unconstrained microphone arrays, our approach is able to match the performance of the comparable models trained on beamform-enhanced signals

International Congress of Mathematicians: 2022 July 6–14: Proceedings of the ICM 2022

Following the long and illustrious tradition of the International Congress of Mathematicians, these proceedings include contributions based on the invited talks that were presented at the Congress in 2022. Published with the support of the International Mathematical Union and edited by Dmitry Beliaev and Stanislav Smirnov, these seven volumes present the most important developments in all fields of mathematics and its applications in the past four years. In particular, they include laudations and presentations of the 2022 Fields Medal winners and of the other prestigious prizes awarded at the Congress. The proceedings of the International Congress of Mathematicians provide an authoritative documentation of contemporary research in all branches of mathematics, and are an indispensable part of every mathematical library

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Modern applications of machine learning in quantum sciences

In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning

Modern applications of machine learning in quantum sciences

In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.Comment: 268 pages, 87 figures. Comments and feedback are very welcome. Figures and tex files are available at https://github.com/Shmoo137/Lecture-Note

Diffracting (meta)‘fictions’: performativity, neocybernetics, diffraction, and the living practice/s of story through select metafictional novels

This thesis aims to re-energize metafiction studies through the frameworks of performativity, neocybernetics, and diffraction. My contention is that the human experience can be viewed as a metafictioning manifold, i.e., an active self-perpetuating entanglement and emergence of narrativizing structures. Metafictions, then, are living artifacts that model the metafictional processes of our constructed realities, while also actively re-organizing our experiences, and acting as heuristics for engaging with the world in metafictional ways. Renewed attention should be given to metafictionality, and in particular to metafictional artifacts, so as to better engage with our material reality as co-participant storytellers alongside the objects and systems around us. The introductory chapter sets the critical and methodological stage. Chapter One uses David Markson’s This is not a Novel (2001) to demonstrate the performativity of metafictions and objects. Chapter Two discusses The Third Policeman (1967) by Flann O’Brien and identifies metafictions as living systems. Chapter Three looks at Jerzy Kosinski’s Being There (1970) in order to theorize the agential natures of such object-systems. Finally, Chapter Four investigates the heuristic ethos of a metafictioning manifold through Mark Z. Danielewski’s The Familiar (2015)