In the mammalian embryo, between 3.5 and 4.5 days after fertilisation, the cells of the inner cell mass evolve from a uniform aggregate to an ordered structure with two distinct tissue layers - the primitive endoderm and epiblast. It was originally assumed that cells differentiated to form these layers in situ, but more recent evidence suggests that both cell types arise scattered throughout the inner cell mass, and it is thus proposed that the tissue layers self-organise by physical mechanisms after the specification of the two cell types. We have developed a computational model based on the subcellular element method to combine theoretical and experimental work and elucidate the mechanisms that drive this self-organisation. The subcellular element method models each cell as a cloud of infinitesimal points that interact with their nearest neighbours by local forces. Our method is built around the introduction of a tensile cortex in each cell by identifying boundary elements and using a Delaunay triangulation to define a network of forces that act within this boundary layer. Once the cortex has been established, we allow the tension in the network to vary locally at interfaces, modelling the exclusion of myosin at cell-cell interfaces and consequent reduction in tension. The model is validated by testing the simulated interfaces in cell doublets and comparing to experimental data and previous theoretical work. Furthermore, we introduce dynamic tension to model blebbing in primitive endoderm cells. We investigate the effects of cortical tension, differential interfacial tension, and blebbing on interfaces, rearrangement, and sorting. By establishing quantitative measurements of sorting we produce phase diagrams of sorting magnitude given system parameters and find that robust sorting in a 30 cell aggregate is best achieved by a combination of differential interfacial tension and blebbing.

The dynamics on networks and the dynamics of networks are usually entangled with each other in many highly connected systems, where the former means the evolution of state and the latter means the adaptation of structure. In this thesis, we will study the coupled dynamics through analytical and computational approaches, where the adaptive networks are driven by learning of various complexities. Firstly, we investigate information diffusion on networks through an adaptive voter model, where two opinions are competing for the dominance. Two types of dynamics facilitate the agreement between neighbours: one is pairwise imitation and the other is link rewiring. As the rewiring strength increases, the network of voters will transform from consensus to fragmentation. By exploring various strategies for structure adaptation and state evolution, our results suggest that network configuration is highly influenced by range-based rewiring and biased imitation. In particular, some approximation techniques are proposed to capture the dynamics analytically through moment-closure differential equations. Secondly, we study an evolutionary model under the framework of natural selection. In a structured community made up of cooperators and cheaters (or defectors), a new-born player will adopt a strategy and reorganise its neighbourhood based on social inheritance. Starting from a cooperative population, an invading cheater may spread in the population occasionally leading to the collapse of cooperation. Such a collapse unfolds rapidly with the change of external conditions, bearing the traits of a critical transition. In order to detect the risk of invasions, some indicators based on population composition and network structure are proposed to signal the fragility of communities. Through the analyses of consistency and accuracy, our results suggest possible avenues for detecting the loss of cooperation in evolving networks. Lastly, we incorporate distributed learning into adaptive agents coordination, which emerges as a consequence of rational individual behaviours. A generic framework of work-learn-adapt (WLA) is proposed to foster the success of agents organisation. To gain higher organisation performance, the division of labour is achieved by a series of events of state evolution and structure adaptation. Importantly, agents are able to adjust their states and structures through quantitative information obtained from distributed learning. The adaptive networks driven by explicit learning pave the way for a better understanding of intelligent organisations in real world.

Active matter theory studies the collective behaviour of self-propelled organisms or objects. Although the field has made great progress in the past decade, little is known of the role played by confinement and surfaces. This thesis analyses the self-organisation of dense bacterial suspensions in three different microchambers: flattened drops, racetracks and lattices of cavities. Suspensions of swimming bacteria are well-known to spontaneously form macroscopic quasi-turbulent patterns such as jets and swirls. Confinement inside flattened drops and racetracks stabilises their motion into a spiral vortex and wavy streams, respectively. We have quantitatively measured and analysed bacterial circulation and discovered cells at the interfaces to move against the bulk. To understand this phenomenon, we developed a method able to measure simultaneously the directions of swimming and of motion. Experiments in drops reveal that cells align in a helical pattern, facing outward and against the main bulk circulation. Likewise, bacteria in racetracks share a biased orientation against the overall stream. Particle-based simulations confirm these results and identify hydrodynamic interactions as the main driving force: bacteria generate long-range fluid flows which advect the suspension in the bulk against its swimming direction, resulting in the double-circulation pattern. We have finally injected dense suspensions of bacteria into lattices of cavities. They form a single vortex in each cavity, initially spinning clockwise or counterclockwise with equal probabilities. Changing the topology of the lattice and the geometry of connections between cavities allows us to control the lattice state (random, ferromagnetic, antiferromagnetic, or unstable). Edge currents along interfaces and connections appear to determine the lattice organisation. We finally propose an Ising model to understand experimental results and estimate Hamiltonian and interactions parameters. This work opens new perspectives for the study of active matter and, we hope, will have a great impact on the field.

The replacement of the conventional field effect transistor (FET) by single electron transistors (SET) would lead to high energy savings and to devices with significantly longer battery life. There are many production approaches, but mostly for specimens in the laboratory. Most of them suffer from the fact that they either only work at cryogenic temperatures, have a low production yield or are not reproducible and each unit works in a unique way. A room temperature (RT) operating SET can be configured by inserting a small (few nm diameters) Si-Nanocrystal (NC) into a thin (<10 nm) SiO2 interlayer in Si. Industrial production has so far been excluded due to a lack of manufacturing processes. Classical technologies such as lithography fail to produce structures in this small scale. Even electron beam lithography or extreme ultraviolet lithography are far from being able to realize these structures in mass production. However, self-organization processes enable structures to be produced in any order of magnitude down to atomic sizes. Earlier studies realized similar systems using a layer of Si-NCs to fabricate a non-volatile memory by using the charge of the NCs for data storage. Based on this, it is very promising to use it for the realization of the SET. The self-organization depends only on the start configuration of the system and the boundary conditions during the process. These macroscopic conditions control the self-formed structures. In this work, ion beam irradiation is used to form the initial configuration, and thermal annealing is used to drive self-organization. A Si/SiO2/Si stack is irradiated and transforms the stack into Si/SiOx/Si by ion beam mixing (IBM) of the two Si/SiO2 interfaces. The oxide becomes metastable and the subsequent thermal treatment induces selforganization, which might leave a single Si-NC in the SiO2 layer for a sufficiently small mixing volume. The transformation of the planar SiOx layer (restriction only in one dimension) into a small SiOx volume (restriction in all three dimensions) is done by etching nanopillars with a diameter of less than 10nm. This forms a small SiOx plate embedded between two Si layers. The challenge is to control the self-organization process. In this work, simulation was used to investigate dependencies and parameter optimization. The ion mixing simulations were performed using binary collision approximation (BCA), followed by kinetic Monte Carlo (KMC) simulations of the decomposition process, which gave good qualitative agreement with the structures observed in related experiments. Quantitatively, however, the BCA simulation seemed to overestimate the mixing effect. This is due to the neglect of the positive entropy of the Si-SiO2 system mixing, i.e. the immiscibility counteracts the collisional mixing. The influence of this mechanism increases with increasing ion fluence. Compared to the combined BCA and KMC simulations, a larger ion mixing fluence has to be applied experimentally to obtain the predicted nanocluster morphology. To model the ion beam mixing of the Si/SiO2 interface, phase field methods have been applied to describe the influence of chemical effects during the irradiation of buried SiO2 layers by 60 keV Si+ ions at RT and thermal annealing at 1050°C. The ballistic collisional mixing was modeled by an approach using Fick’s diffusion equation, and the chemical effects and the annealing were described by the Cahn Hilliard equation. By that, it is now possible to predict composition profiles of Si/SiO2 interfaces during irradiation. The results are in good agreement with the experiment and are used for the predictions of the NCs formation in the nanopillar. For the thermal treatment model extensions were also necessary. The KMC simulations of Si-SiO2 systems in the past were based on normed time and temperature, so that the diffusion velocity of the components was not considered. However, the diffusion of Si in SiO2 and SiO2 in Si differs by several orders of magnitude. This cannot be neglected in the thermal treatment of the Si/SiO2 interface, because the processes that differ in speed in this order of magnitude are only a few nanometers apart. The KMC method was extended to include the different diffusion coefficients of the Si-SiO2 system. This allows to extensively investigate the influence of the diffusion. The phase diagram over temperature and composition was examined regarding decomposition (nucleation as well as spinodal decomposition) and growing of NCs. Using the methods and the knowledge gained about the system, basic simulations for the individual NC formation in the nanopillar were carried out. The influence of temperature, diameter, and radiation fluence was discussed in detail on the basis of simulation results.

This thesis puts forward a novel way of control for robotic morphologies. Taking inspiration from Behaviour Based robotics and self-organisation principles, we present an interfacing mechanism, capable of adapting both to the user and the robot, while enabling a paradigm of intuitive control for the user. A transparent mechanism is presented, allowing for a seamless integration of control signals and robot behaviours. Instead of the user adapting to the interface and control paradigm, the proposed architecture allows the user to shape the control motifs in their way of preference, moving away from the cases where the user has to read and understand operation manuals or has to learn to operate a specific device. The seminal idea behind the work presented is the coupling of intuitive human behaviours with the dynamics of a machine in order to control and direct the machine dynamics. Starting from a tabula rasa basis, the architectures presented are able to identify control patterns (behaviours) for any given robotic morphology and successfully merge them with control signals from the user, regardless of the input device used. We provide a deep insight in the advantages of behaviour coupling, investigating the proposed system in detail, providing evidence for and quantifying emergent properties of the models proposed. The structural components of the interface are presented and assessed both individually and as a whole, as are inherent properties of the architectures. The proposed system is examined and tested both in vitro and in vivo, and is shown to work even in cases of complicated environments, as well as, complicated robotic morphologies. As a whole, this paradigm of control is found to highlight the potential for a change in the paradigm of robotic control, and a new level in the taxonomy of human in the loop systems.

Investigating the arrangement of the packed DNA inside the nucleus has revealed the essential role of genome organisation in controlling genome function. Furthermore, genome architecture is highly dynamic and significant chromatin re-organisation occurs in response to environmental changes. However, the mechanisms that drive the 3D organisation of the genome remain largely unknown. To understand the effect of biophysical properties of chromatin on the dynamics and structure of chromosomes, I developed a 3D computational model of the nucleus of the yeast S. cerevisiae during interphase. In the model, each chromosome was a hetero-polymer informed by our bioinformatics analysis for heterogeneous occupancy of chromatin-associated proteins across the genome. Two different conditions were modelled, normal growth (25°C) and heat shock (37°C), where a concerted redistribution of proteins was observed upon transition from one temperature to the other. Movement of chromatin segments was based on Langevin dynamics and each segment had a mobility according to their protein occupancy and the expression level of their corresponding genes. The model provides a significantly improved match with quantitative microscopy measurements of telomere positions, the distributions of 3D distances between pairs of different loci, and the mean squared displacement of a labelled locus. The quantified contacts between chromosomal segments were similar to the observed Hi-C data. At both 25°C and 37°C conditions, the segments that were highly occupied by proteins had high number of interactions with each other, and the highly transcribed genes had lower contacts with other segments. In addition, similar to the experimental observations, heat-shock genes were found to be located closer to the nuclear periphery upon activation in the simulations. It was also shown that the determined distribution of proteins along the genome is crucial to achieve the correct genome organisation. Hence, the heterogeneous binding of proteins, which results in differential mobility of chromatin segments, leads to 3D self-organisation.

Nous avons étudié la structure de la surface de mica muscovite clivée à l?air par diffraction d?électrons lents en mode oscillant. Cette nouvelle méthode d?analyse structurale, que nous avons développée à partir d?un système optique standard de résolution conventionnelle, possède une grande sensibilité. En outre, elle permet de former des images de diffraction entières sans procéder, comme le font les autres diffractomètres à haute résolution, à une reconstitution ligne par ligne. Cette caractéristique permet aussi de réaliser des acquisitions d?image qui sont corrélées temporellement avec une excitation extérieure, ce qui rend possible toute une gamme d?expériences nouvelles, notamment dans le cadre de la réponse thermodynamique d?une surface au voisinage d?une transition de phase. Notre étude de la surface du mica muscovite par diffraction d?électrons lents en mode oscillant a permis pour la première fois de mettre en évidence la nanostructuration de sa couche superficielle. Notre interprétation est que lors du clivage, la surface du mica s?auto-organise pour former un arrangement d?îlots à l?intérieur desquels les atomes de potassium occupent les sites réguliers du cristal. Les îlots sont soit de taille définie soit séparés par une distance définie. Dans les zones interstitielles qui les séparent, les ions potassium, en concentration beaucoup plus faible, formeraient une couche amorphe.

Understanding the principles of self-organisation exhibited by block copolymers requires the combination of synthetic and physicochemical knowledge. The ability to synthesise block copolymers with desired architecture facilitates the ability to manipulate their aggregation behaviour, thus providing the key to nanotechnology. Apart from relative block volumes, the size and morphology of the produced nanostructures is controlled by the effective incompatibility between the different blocks. Since polymerisation techniques allowing for the synthesis of well-defined block copolymers are restricted to a limited number of monomers, the ability to tune the incompatibility is very limited. <br><br> Nevertheless, Polymer Analogue Reactions can offer another possibility for the production of functional block copolymers by chemical modifications of well-defined polymer precursors. Therefore, by applying appropriate modification methods both volume fractions and incompatibility, can be adjusted. Moreover, copolymers with introduced functional units allow utilization of the concept of molecular recognition in the world of synthetic polymers.<br><br> The present work describes a modular synthetic approach towards functional block copolymers. Radical addition of functional mercaptanes was employed for the introduction of diverse functional groups to polybutadiene-containing block copolymers. Various modifications of 1,2-polybutadiene-poly(ethylene oxide) block copolymer precursors are described in detail. Furthermore, extension of the concept to 1,2-polybutadiene-polystyrene block copolymers is demonstrated. Further investigations involved the self-organisation of the modified block copolymers. Formed aggregates in aqueous solutions of block copolymers with introduced carboxylic acid, amine and hydroxyl groups as well as fluorinated chains were characterised. Study of the aggregation behaviour allowed general conclusions to be drawn regarding the influence of the introduced groups on the self-organisation of the modified copolymers. Finally, possibilities for the formation of complexes, based on electrostatic or hydrogen-bonding interactions in mixtures of block copolymers bearing mutually interacting functional groups, were investigated. / Um die Prinzipien der Selbstorganisation von Blockcopolymeren zu verstehen, ist es notwendig das Wissen auf den Gebieten der Synthese und der Physikochemie zu kombinieren. Die Fähigkeit, Blockcopolymere mit gewünschter Architektur zu synthetisieren, gibt einem auch die Möglichkeit das Aggregationsverhalten zu steuern, was wiederum der Schlüssel zur Nanotechnologie ist. Abgesehen von den relativen Blockvolumina, wird die Größe und Morphologie der gebildeten Nanostrukturen durch die effektive Inkompartibilität zwischen den unterschiedlichen Blöcken bestimmt. Da die Polymerisationstechniken, mit denen man gut definierte Blockcopolymere synthetisieren kann, auf bestimmte Monomere beschränkt sind, läßt sich diese Inkompatibilität nur eingeschränkt abstimmen. Polymeranaloge Reaktionen können dagegen eine Möglichkeit bieten, funktionale Blockcopolymere durch die chemische Modifizierung von gut definierten Copolymeren zu erhalten. Somit können, bei Verwendung von geeigneten Modifikationsmethoden, die Volumenanteile sowie die Inkompatibilität der Blöcke angepasst werden. Außerdem können Copolymere, die funktionelle Gruppe enthalten, es ermöglichen das Prinzip der molekularen Erkennung (Schloss-Schlüssel) auf dem Gebiet der synthetischen Polymere anzuwenden.<br><br> Die vorleigende Arbeit beschreibt einen Ansatz zur modularen Synthese von funktionalen Blockcopolymeren. Durch radikalische Addition von funktionellen Mercaptanen wurden in Copolymere mit einem Polybutadien-Block verschiedenen funktionelle Gruppen eingebracht. Von 1,2-Polybutadien-Polyethylenoxid Blockcopolymeren werden dabei mehrere Modifikationen im Detail beschrieben. Zudem wird die Erweiterung des Konzepts auf 1,2-Polybutadien-Polystyrol Blockcopolymere gezeigt. Die weiteren Untersuchungen betrafen die Selbstorganisation der modifizierten Blockcopolymere in Lösung. Hierbei wurden die Aggregate, die in wässriger Lösung von Blockcopolymeren mit Carbonsäure-, Amin- und Hydroxylgruppen sowie fluorierte Ketten gebildet werden, charakterisiert. Die Untersuchung des Aggregationsverhaltens erlaubt es, generelle Aussagen über den Einfluss der eingebrachten Gruppen auf die Selbstorganisation der modifizierten Copolymere zu treffen. Abschließend wurde die Bildung von Komplexen auf der Basis von elektrostatischer Wechselwirkung oder Wasserstoffbrückenbindung in Mischungen aus Copolymeren, die untereinander Wechselwirkende funktionale Gruppe besitzen, untersucht.

Anders als menschengemachte Maschinen verfügen Zellen über keinen festgeschriebenen Bauplan und die Positionen einzelner Elemente sind häufig nicht genau festgelegt, da die Moleküle diffusiven Zufallsbewegungen unterworfen sind. Darüber hinaus sind einzelne Bauteile auch nicht auf eine einzelne Funktion festgelegt, sondern können parallel in verschiedene Prozesse einbezogen sein. Basierend auf Selbstorganisation und Selbstassemblierung muß die Organisation von Anordnung und Funktion einer lebenden Zelle also bereits in ihren einzelnen Komponenten inhärent enthalten sein. Die intrazelluläre Organisation wird zum großen Teil durch ein internes Biopolymergerüst reguliert, das Zytoskelett. Biopolymer-Netzwerke und –Fasern durchdringen die gesamte Zelle und sind verantworlich für mechanische Integrität und die funktionale Architektur. Unzählige essentielle biologische Prozesse hängen direkt von einem funktionierenden Zytoskelett ab. Die vorliegende Arbeit zielt auf ein besser Verständnis und den Nachbau zweier verschiedener funktionaler Module lebender Zellen anhand stark reduzierter Modellsysteme. Als zentrales Element wurde Aktin gewählt, da dieses Biopolymer eine herausragende Rolle in nahezu allen eukaryotischen Zellen spielt. Mit dem ersten Modellsystem wird der bewegliche Aktin-Polymerfilm an der Vorderkante migrierender Zellen betrachtet. Die wichtigsten Elemente dieser hochdynamischen Netzwerke sind bereits bekannt und wurden in dieser Arbeit benutzt um ein experimentelles Modellsystem zu etablieren. Vor allem aber lieferten detailierte Computersimulationen und ein mathematisches Modell neue Erkenntnisse über grundlegende Organisationsprinzipien dieser Aktinnetzwerke. Damit war es nicht nur möglich, experimentelle Daten erfolgreich zu reproduzieren, sondern das Entstehen von Substrukturen und deren Charakteristika auf proteinunabhängige, generelle Mechanismen zurückzuführen. Das zweite studierte System betrachtet die Selbstassemblierung von Aktinnetzwerken durch entropische Kräfte. Aktinfilamente aggregieren hierbei durch Kondensation multivalenter Ionen oder durch Volumenausschluss hochkonzentrierter inerter Polymere. Ein neu entwickelter Experimentalaufbau bietet die Möglichkeit in gut definierten zellähnlichen Volumina, Konvektionseinflüsse zu umgehen und Aggregationseffekte gezielt einzuschalten. Hierbei wurden neuartige, regelmäßige Netzwerkstrukturen entdeckt, die bislang nur im Zusammenhang mit molekularen Motoren bekannt waren. Es konnte ferner gezeigt werden, dass die Physik der Flüssigkristalle entscheidend zu weiteren Variationen dieser Netzwerke beiträgt. Dabei wird ersichtlich, dass entstehende Netzwerke in ihrer Architektur direkt die zuvor herrschenden Anisotropien der Filamentlösung widerspiegeln.

Internal Models (IMs) play a significant role in autonomous robotics. They are mechanisms able to represent the input-output characteristics of the sensorimotor loop. In developmental robotics, open-ended learning of skills and knowledge serves the purpose of reaction to unexpected inputs, to explore the environment and to acquire new behaviours. The development of the robot includes self-exploration of the state-action space and learning of the environmental dynamics. In this dissertation, we explore the properties and benefits of the self-organisation of robot behaviour based on the homeokinetic learning paradigm. A homeokinetic robot explores the environment in a coherent way without prior knowledge of its configuration or the environment itself. First, we propose a novel approach to self-organisation of behaviour by artificial curiosity in the sensorimotor loop. Second, we study how different forward models settings alter the behaviour of both exploratory and goal-oriented robots. Diverse complexity, size and learning rules are compared to assess the importance in the robot’s exploratory behaviour. We define the self-organised behaviour performance in terms of simultaneous environment coverage and best prediction of future sensori inputs. Among the findings, we have encountered that models with a fast response and a minimisation of the prediction error by local gradients achieve the best performance. Third, we study how self-organisation of behaviour can be exploited to learn IMs for goal-oriented tasks. An IM acquires coherent self-organised behaviours that are then used to achieve high-level goals by reinforcement learning (RL). Our results demonstrate that learning of an inverse model in this context yields faster reward maximisation and a higher final reward. We show that an initial exploration of the environment in a goal-less yet coherent way improves learning. In the same context, we analyse the self-organisation of central pattern generators (CPG) by reward maximisation. Our results show that CPGs can learn favourable reward behaviour on high-dimensional robots using the self-organised interaction between degrees of freedom. Finally, we examine an on-line dual control architecture where we combine an Actor-Critic RL and the homeokinetic controller. With this configuration, the probing signal is generated by the exertion of the embodied robot experience with the environment. This set-up solves the problem of designing task-dependant probing signals by the emergence of intrinsically motivated comprehensible behaviour. Faster improvement of the reward signal compared to classic RL is achievable with this configuration.