Transportphänomene treten in biologischen und künstlichen Systemen auf allen Längenskalen auf. In dieser Arbeit untersuchen wir sie für verschiedene Systeme aus einer mesoskopischen Perspektive, in der Fluktuationen physikalischer Größen um ihre Mittelwerte eine wichtige Rolle spielen. Im ersten Teil untersuchen wir die persistente Bewegung aktiver Brownscher Teilchen mit zusätzlichem Drehmoment, wie sie z.B. für Spermien oder Janus Teilchen auftritt. Wird ihre Bewegung auf einen Tunnel variierender Breite beschränkt, so setzt im thermischen Nichtgleichgewicht Transport ein; ungerichtete Fluktuationen des rauschhaften Antriebs werden gleichgerichtet. Hierdurch wird ein neuer Ratschentyp realisiert. Im zweiten Teil untersuchen wir den intrazellulären Cargotransport in den Axonen von Nervenzellen mithilfe molekularer Motoren. Sie werden als asymmetrischer Ausschlussprozess simuliert. Zusätzlich können die Cargos zwischen benachbarten Motoren ausgetauscht werden. Dadurch lassen sich charakteristische Eigenschaften des langsamen axonalen Transports mit einer einzigen Motorspezies reproduzieren. Bewerkstelligt wird dies durch die transiente Anbindung der Cargos an rückwärtslaufende Motorstaus. Im dritten Teil diskutieren wir resistive switching, die nicht volatile Widerstandsänderung eines Dielektrikums durch elektrische Impulse. Es wird für Anwendungen im Computerspeicher ausgenutzt, dem resistive RAM. Wir schlagen ein auf Sauerstoffvakanzen basierendes stochastisches Gitterhüpfmodell vor. Wir definieren binäre logische Zustände mit Hilfe der zugrunde liegenden Vakanzenverteilung und definieren Schreibe- und Leseoperationen durch Spannungsimpulse für ein solches Speicherelement. Überlegungen über die Unterscheidbarkeit dieser Operationen unter Fluktuationen zusammen mit der Deutlichkeit der unterschiedlichen Widerstandszustände selbst ermöglichen es uns, eine optimale Vakanzenzahl vorherzusagen. / Transport phenomena occur in biological and artificial systems at all length scales. In this thesis, we investigate them for various systems from a mesoscopic perspective, in which fluctuations around their average properties play an important role. In the first part, we investigate the persistent diffusive motion of active Brownian particles with an additional torque. It can appear in many real life systems, for example in sperm cells or Janus particles. If their motion is confined to a tunnel of varying width, transport arises out of thermal equilibrium; unbiased fluctuations of the noisy drive are rectified. This way, we have realized a novel kind of ratchet. In the second part, we study intracellular cargo transport in the axons of nerve cells by molecular motors. They are modeled by an asymmetric exclusion process. In a new approach, we add a cargo exchange interaction between the motors. This way, the characteristics of slow axonal transport can be accounted for with a single motor species. It is explained by the transient attachment of cargos to reverse walking motors jams. In the third part, we discuss resistive switching, the non-volatile change of resistance in a dielectric due to electric pulses. It is exploited for applications in computer memory, the resistive random access memory (ReRAM). We propose a stochastic lattice hopping model based on the on oxygen vacancies. We define binary logical states by means of the underlying vacancy distributions, and establish a framework of writing and reading such a memory element with voltage pulses. Considerations about the discriminability of these operations under fluctuations together with the markedness of the resistive switching effect itself enable us to predict an optimal vacancy number.

Information spielt in der Natur eine zentrale Rolle. Als intrinsischer Teil des genetischen Codes ist sie das Grundgerüst jeder Struktur und ihrer Entwicklung. Im Speziellen dient sie auch Organismen, ihre Umgebung wahrzunehmen und sich daran anzupassen. Die Grundvoraussetzung dafür ist, dass sie Information ihrer Umgebung sowohl messen als auch interpretieren können, wozu Zellen komplexe Signaltransduktionswege entwickelt haben. In dieser Arbeit konzentrieren wir uns auf Signalprozesse in S.cerevisiae die von osmotischem Stress (High Osmolarity Glycerol (HOG) Signalweg) und der Stimulation mit α-Faktor (Pheromon Signalweg) angesprochen werden. Wir wenden stochastische Modelle an, die das intrinsische Rauschen biologischer Prozesse darstellen können, um verstehen zu können wie Signalwege die ihnen zur Verfügung stehende Information umsetzen. Informationsübertragung wird dabei mit einem Ansatz aus Shannons Informationstheorie gemessen, indem wir sie als einen Kanal in diesem Sinne auffassen. Wir verwenden das Maß der Kanalkapazität, um die Genauigkeit des Phosphorelays einschränken zu können. In diesem Modell, simuliert mit dem Gillespie Algorithmus, können wir durch die Analyse des Signalverhaltens den Parameterraum zusätzlich stark einschränken. Eine weitere Herangehensweise der Signalverarbeitung beschäftigt sich mit dem “Crosstalk” zwischen HOG und Pheromon Signalweg. Wir zeigen, dass die Kontrolle der Signalspezifizität vor allem bei Scaffold-Proteinen liegt, die Komponenten der Signalkaskade binden. Diese konservierten Motive zellulärer Signaltransduktion besitzen eine geeignete Struktur, um Information getreu übertragen zu können. Im letzten Teil der Arbeit untersuchen wir potentielle Gründe für die evolutionäre Selektion von Scaffolds. Wir zeigen, dass ihnen bereits durch die Struktur des Mechanismus möglich ist, Informationsgenauigkeit zu verbessern und einer verteilten Informationsweiterleitung sowohl dadurch als auch durch ihre Robustheit überlegen sind. / Information plays a ubiquitous role in nature. It provides the basis for structure and development, as it is inherent part of the genetic code. It also enables organisms to make sense of their environments and react accordingly. For this, a cellular interpretation of information is needed. Cells have developed sophisticated signaling mechanisms to fulfill this task and integrate many different external cues with their help. Here we focus on signaling that senses osmotic stress (High Osmolarity Glycerol (HOG) pathway) as well as α-factor stimulation (pheromone pathway) in S.cerevisiae. We employ stochastic modeling to simulates the inherent noisy nature of biological processes to assess how systems process the information they receive. This information transmission is evaluated with an information theoretic approach by interpreting signal transduction as a transmission channel in the sense of Shannon. We use channel capacity to both constrain as well as quantify the fidelity in the phosphorelay system of the HOG pathway. In this model, simulated with the Gillespie Algorithm, the analysis of signaling behavior allows us to constrain the possible parameter sets for the system severely. A further approach to signal processing is concerned with the mechanisms that conduct crosstalk between the HOG and the pheromone pathway. We find that the control for signal specificity lies especially with the scaffold proteins that tether signaling components and facilitate signaling by trans-location to the membrane and shielding against miss-activation. As conserved motifs of cellular signal transmission, these scaffold proteins show a particularly well suited structure for accurate information transmission. In the last part of this thesis, we examine the potential reasons for an evolutionary selection of the scaffolding structure. We show that due to its structure, scaffolds are increasing information transmission fidelity and outperform a distributed signal in this regard.

Numerical methods are often required to solve chemical problems, either to verify theoretical models or to access information that is not readily available experimentally. This thesis deals with both situations, though in differing levels of detail. A major component of this thesis is devoted to developing new methods to determine a full eigendecomposition of the matrices derived from "low temperature" unimolecular master equations. When transient behaviour is of interest achieving relative accuracy for more than just the eigenvector corresponding to the smallest eigenvalue is of central importance. Three new methods are presented. The first is based on a weighted implementation of subspace projection methods, in this case explored for the well-known Arnoldi method. This weighted inner product subspace projection methodology is demonstrated to

Numerical methods are often required to solve chemical problems, either to verify theoretical models or to access information that is not readily available experimentally. This thesis deals with both situations, though in differing levels of detail. A major component of this thesis is devoted to developing new methods to determine a full eigendecomposition of the matrices derived from "low temperature" unimolecular master equations. When transient behaviour is of interest achieving relative accuracy for more than just the eigenvector corresponding to the smallest eigenvalue is of central importance. Three new methods are presented. The first is based on a weighted implementation of subspace projection methods, in this case explored for the well-known Arnoldi method. This weighted inner product subspace projection methodology is demonstrated to

Numerical methods are often required to solve chemical problems, either to verify theoretical models or to access information that is not readily available experimentally. This thesis deals with both situations, though in differing levels of detail. A major component of this thesis is devoted to developing new methods to determine a full eigendecomposition of the matrices derived from "low temperature" unimolecular master equations. When transient behaviour is of interest achieving relative accuracy for more than just the eigenvector corresponding to the smallest eigenvalue is of central importance. Three new methods are presented. The first is based on a weighted implementation of subspace projection methods, in this case explored for the well-known Arnoldi method. This weighted inner product subspace projection methodology is demonstrated to

Numerical methods are often required to solve chemical problems, either to verify theoretical models or to access information that is not readily available experimentally. This thesis deals with both situations, though in differing levels of detail. A major component of this thesis is devoted to developing new methods to determine a full eigendecomposition of the matrices derived from "low temperature" unimolecular master equations. When transient behaviour is of interest achieving relative accuracy for more than just the eigenvector corresponding to the smallest eigenvalue is of central importance. Three new methods are presented. The first is based on a weighted implementation of subspace projection methods, in this case explored for the well-known Arnoldi method. This weighted inner product subspace projection methodology is demonstrated to

Quantification of stochastic or quantum systems by a joint probability density or wave function is a notoriously difficult computational problem, since the solution depends on all possible states (or realizations) of the system. Due to this combinatorial flavor, even a system containing as few as ten particles may yield as many as $10^{10}$ discretized states. None of even modern supercomputers are capable to cope with this curse of dimensionality straightforwardly, when the amount of quantum particles, for example, grows up to more or less interesting order of hundreds. A traditional approach for a long time was to avoid models formulated in terms of probabilistic functions, and simulate particular system realizations in a randomized process. Since different times in different communities, data-sparse methods came into play. Generally, they aim to define all data points indirectly, by a map from a low amount of representers, and recast all operations (e.g. linear system solution) from the initial data to the effective parameters. The most advanced techniques can be applied (at least, tried) to any given array, and do not rely explicitly on its origin. The current work contributes further progress to this area in the particular direction: tensor product methods for separation of variables. The separation of variables has a long history, and is based on the following elementary concept: a function of many variables may be expanded as a product of univariate functions. On the discrete level, a function is encoded by an array of its values, or a tensor. Therefore, instead of a huge initial array, the separation of variables allows to work with univariate factors with much less efforts. The dissertation contains a short overview of existing tensor representations: canonical PARAFAC, Hierarchical Tucker, Tensor Train (TT) formats, as well as the artificial tensorisation, resulting in the Quantized Tensor Train (QTT) approximation method. The contribution of the dissertation consists in both theoretical constructions and practical numerical algorithms for high-dimensional models, illustrated on the examples of the Fokker-Planck and the chemical master equations. Both arise from stochastic dynamical processes in multiconfigurational systems, and govern the evolution of the probability function in time. A special focus is put on time propagation schemes and their properties related to tensor product methods. We show that these applications yield large-scale systems of linear equations, and prove analytical separable representations of the involved functions and operators. We propose a new combined tensor format (QTT-Tucker), which descends from the TT format (hence TT algorithms may be generalized smoothly), but provides complexity reduction by an order of magnitude. We develop a robust iterative solution algorithm, constituting most advantageous properties of the classical iterative methods from numerical analysis and alternating density matrix renormalization group (DMRG) techniques from quantum physics. Numerical experiments confirm that the new method is preferable to DMRG algorithms. It is as fast as the simplest alternating schemes, but as reliable and accurate as the Krylov methods in linear algebra.

The entropy production rate is a well established measure for the extent of irreversibility in a process. For irreversible processes, one thus usually expects that the entropy production rate approaches zero in the reversible limit. Fractional diffusion equations provide a fascinating testbed for that intuition in that they build a bridge connecting the fully irreversible diffusion equation with the fully reversible wave equation by a one-parameter family of processes. The entropy production paradox describes the very non-intuitive increase of the entropy production rate as that bridge is passed from irreversible diffusion to reversible waves. This paradox has been established for time- and space-fractional diffusion equations on one-dimensional continuous space and for the Shannon, Tsallis and Renyi entropies. After a brief review of the known results, we generalize it to time-fractional diffusion on a finite chain of points described by a fractional master equation.

Ziel der vorliegenden Arbeit ist es, das Preisach-Modell bezüglich stochastischer äußerer Felder und temperaturbezogener Aspekte zu untersuchen. Das phänomenologische Preisach-Modell wird oft erfolgreich angewendet, um Systeme mit Hysterese zu beschreiben. Im ersten Teil der Arbeit wird die Antwort des Preisach-Modells auf stochastische äußere Felder untersucht. Hier liegt das Augenmerk hauptsächlich auf der Autokorrelation; sie dient dazu den Einfluss des hysteretischen Gedächtnisses zu quantifizieren. Mit analytischen Methoden wird gezeigt, dass sich ein Langzeitgedächtnis, sichtbar in der Autokorrelation der Systemantwort, entwickeln kann, selbst wenn das treibende Feld unkorreliert ist. Im Anschluss werden diese Resultate, m.H. von Simulationen, auf äußere Felder ausgeweitet, die selbst Korrelationen aufweisen können. Der zweite Teil der Arbeit befasst sich mit dem Einfluss einer endlichen Temperatur auf das Preisach-Modell. Es werden unterschiedliche Methoden besprochen, wie das Nichtgleichgewichtsmodell in seiner mikromagnetischen Interpretation mit Temperatur als Gleichgewichtseigenschaft verknüpft werden kann. Eine Formulierung wird genutzt, um die Magnetisierung von Nickelnanopartikeln in einer Fullerenmatrix zu simulieren und mit Experimenten zu vergleichen. Des Weiteren wird die Relaxationsdynamik des Gedächtnisses des Preisach-Modells bei endlichen Temperaturen untersucht. / The aim of this thesis is to investigate the Preisach model in regard to stochastically driving and temperature-related aspects. The Preisach model is a phenomenological model for systems with hysteresis which is often successfully applied. Hysteresis is a widespread phenomenon which is observed in nature and the key feature of certain technological applications. Further, it contributes to phenomena of interest in social science and economics as well. Prominent examples are the magnetization of ferromagnetic materials in an external magnetic field or the adsorption-desorption hysteresis observed in porous media. Hysteresis involves the development of a hysteresis memory, and multistability in the interrelations between external driving fields and system response. In the first part, we mainly investigate the response of Preisach hysteresis models driven by stochastic input processes with regard to autocorrelation functions to quantify the influence of the system’s memory. Using rigorous methods, it is shown that the development of a hysteresis memory is reflected in the possibility of long-time tails in the autocorrelation functions, even for uncorrelated driving fields. In the case of uncorrelated driving, these long-time tails in the autocorrelations of the system’s response are determined only by the tails of the involved densities. They will be observed if there are broad Preisach densities assigning a high weight to elementary loops of large width and narrow input densities such that rare extreme events of the input time series contribute significantly to the output for a long period of time. Afterwards, these results are extended by simulations to driving fields which themselves show correlations. It is shown that the autocorrelation of the output does not decay faster than the autocorrelation of the input process. Further, there is a possibility that long-term memory in the hysteretic response is more pronounced in the case of uncorrelated driving than in the case of correlated driving. The behavior of the output probability distribution at the saturation values is quite universal. It is not affected by the presence of correlations and allows conclusions whether the input density is much more narrow than the Preisach density or not. Moreover, the existence of effective Preisach densities is shown which define equivalence classes of systems of input and Preisach densities which lead to realizations of the same output variable. The asymptotic behavior of an effective Preisach density determines the asymptotic correlation decay of the system’s response in the case of uncorrelated driving. In the second part, temperature-related effects are considered. It is reviewed how the non-equilibrium Preisach model in its micromagnetic picture can be related to temperature within the framework of extended irreversible thermodynamics. The irreversible response of a ferromagnetic material, namely, Nickel nanoparticles in a fullerene matrix, is simulated. The model includes superparamagnetism where ferromagnetism breaks down at temperatures lower than the Curie temperature and the results are compared to experimental data. Furthermore, we adapt known results for the thermal relaxation of the system’s memory in the form of a front propagation in the Preisach plane derived basically from solving a master equation and by the use of a contradictory assumption. A closer look is taken at short time scales which dissolves the contradiction and shows that the known results apply, taking into account the fact that the dividing line propagation starts with an additional delay time depending on the front coordinates in the Preisach plane. Additionally, it is outlined how thermal relaxation behavior in the Preisach model of hysteresis can be studied using a Fokker-Planck equation. The latter is solved analytically in the non-hysteretic limit using eigenfunction methods. The results indicate a change in the relaxation behavior, especially on short time scales.

Ziel der vorliegenden Arbeit ist es, das Preisach-Modell bezüglich stochastischer äußerer Felder und temperaturbezogener Aspekte zu untersuchen. Das phänomenologische Preisach-Modell wird oft erfolgreich angewendet, um Systeme mit Hysterese zu beschreiben. Im ersten Teil der Arbeit wird die Antwort des Preisach-Modells auf stochastische äußere Felder untersucht. Hier liegt das Augenmerk hauptsächlich auf der Autokorrelation; sie dient dazu den Einfluss des hysteretischen Gedächtnisses zu quantifizieren. Mit analytischen Methoden wird gezeigt, dass sich ein Langzeitgedächtnis, sichtbar in der Autokorrelation der Systemantwort, entwickeln kann, selbst wenn das treibende Feld unkorreliert ist. Im Anschluss werden diese Resultate, m.H. von Simulationen, auf äußere Felder ausgeweitet, die selbst Korrelationen aufweisen können. Der zweite Teil der Arbeit befasst sich mit dem Einfluss einer endlichen Temperatur auf das Preisach-Modell. Es werden unterschiedliche Methoden besprochen, wie das Nichtgleichgewichtsmodell in seiner mikromagnetischen Interpretation mit Temperatur als Gleichgewichtseigenschaft verknüpft werden kann. Eine Formulierung wird genutzt, um die Magnetisierung von Nickelnanopartikeln in einer Fullerenmatrix zu simulieren und mit Experimenten zu vergleichen. Des Weiteren wird die Relaxationsdynamik des Gedächtnisses des Preisach-Modells bei endlichen Temperaturen untersucht. / The aim of this thesis is to investigate the Preisach model in regard to stochastically driving and temperature-related aspects. The Preisach model is a phenomenological model for systems with hysteresis which is often successfully applied. Hysteresis is a widespread phenomenon which is observed in nature and the key feature of certain technological applications. Further, it contributes to phenomena of interest in social science and economics as well. Prominent examples are the magnetization of ferromagnetic materials in an external magnetic field or the adsorption-desorption hysteresis observed in porous media. Hysteresis involves the development of a hysteresis memory, and multistability in the interrelations between external driving fields and system response. In the first part, we mainly investigate the response of Preisach hysteresis models driven by stochastic input processes with regard to autocorrelation functions to quantify the influence of the system’s memory. Using rigorous methods, it is shown that the development of a hysteresis memory is reflected in the possibility of long-time tails in the autocorrelation functions, even for uncorrelated driving fields. In the case of uncorrelated driving, these long-time tails in the autocorrelations of the system’s response are determined only by the tails of the involved densities. They will be observed if there are broad Preisach densities assigning a high weight to elementary loops of large width and narrow input densities such that rare extreme events of the input time series contribute significantly to the output for a long period of time. Afterwards, these results are extended by simulations to driving fields which themselves show correlations. It is shown that the autocorrelation of the output does not decay faster than the autocorrelation of the input process. Further, there is a possibility that long-term memory in the hysteretic response is more pronounced in the case of uncorrelated driving than in the case of correlated driving. The behavior of the output probability distribution at the saturation values is quite universal. It is not affected by the presence of correlations and allows conclusions whether the input density is much more narrow than the Preisach density or not. Moreover, the existence of effective Preisach densities is shown which define equivalence classes of systems of input and Preisach densities which lead to realizations of the same output variable. The asymptotic behavior of an effective Preisach density determines the asymptotic correlation decay of the system’s response in the case of uncorrelated driving. In the second part, temperature-related effects are considered. It is reviewed how the non-equilibrium Preisach model in its micromagnetic picture can be related to temperature within the framework of extended irreversible thermodynamics. The irreversible response of a ferromagnetic material, namely, Nickel nanoparticles in a fullerene matrix, is simulated. The model includes superparamagnetism where ferromagnetism breaks down at temperatures lower than the Curie temperature and the results are compared to experimental data. Furthermore, we adapt known results for the thermal relaxation of the system’s memory in the form of a front propagation in the Preisach plane derived basically from solving a master equation and by the use of a contradictory assumption. A closer look is taken at short time scales which dissolves the contradiction and shows that the known results apply, taking into account the fact that the dividing line propagation starts with an additional delay time depending on the front coordinates in the Preisach plane. Additionally, it is outlined how thermal relaxation behavior in the Preisach model of hysteresis can be studied using a Fokker-Planck equation. The latter is solved analytically in the non-hysteretic limit using eigenfunction methods. The results indicate a change in the relaxation behavior, especially on short time scales.