We prove that every (nice) Feller process is an It^o process in the sense of Cinlar, Jacod, Protter and Sharpe (1980). Next we generalize the notion of the symbol and define it for this larger class of processes. As examples the solutions of stochastic differential equations are considered. The symbol is then used to derive a quick approach to the semimartingale characteristics as well as the generator of the process under consideration. Finally we give some examples of how our methods work for processes used in mathematical finance. / Wir haben gezeigt, dass jeder (nette) Feller Prozess ein It^o Prozess im Sinne von Cinlar, Jacod, Protter und Sharpe (1980) ist. Es stellt sich heraus, dass man den Begriff des Symbols, der für Feller Prozesse bekannt ist, auf diese größere Klasse verallgemeinern kann. Dieses Symbol haben wir für die Lösungen verschiedener stochastischer Differentialgleichungen berechnet. Außerdem haben wir gezeigt, dass das Symbol einen schnellen Zugang zur Berechnung der Semimartingal-Charakteristiken und des Erzeugers eines It^o Prozesses liefert. Zuletzt wurden die Ergebnisse auf Prozesse angewendet, die in der Finanzmathematik gebräuchlich sind. - (Die Dissertation ist veröffentlicht im Shaker Verlag GmbH, Postfach 101818, 52018 Aachen, Deutschland, http://www.shaker.de, ISBN: 978-3-8322-8244-8)

Cancer cells display characteristic traits acquired in a step-wise manner during carcinogenesis. Some of these traits are autonomous growth, induction of angiogenesis, invasion and metastasis. In this thesis, the focus is on one of the latest stages of tumor progression, tumor invasion. Tumor invasion emerges from the combined effect of tumor cell-cell and cell-microenvironment interactions, which can be studied with the help of mathematical analysis. Cellular automata (CA) can be viewed as simple models of self-organizing complex systems in which collective behavior can emerge out of an ensemble of many interacting "simple" components. In particular, we focus on an important class of CA, the so-called lattice-gas cellular automata (LGCA). In contrast to traditional CA, LGCA provide a straightforward and intuitive implementation of particle transport and interactions. Additionally, the structure of LGCA facilitates the mathematical analysis of their behavior. Here, the principal tools of mathematical analysis of LGCA are the mean-field approximation and the corresponding Lattice Boltzmann equation. The main objective of this thesis is to investigate important aspects of tumor invasion, under the microscope of mathematical modeling and analysis: Impact of the tumor environment: We introduce a LGCA as a microscopic model of tumor cell migration together with a mathematical description of different tumor environments. We study the impact of the various tumor environments (such as extracellular matrix) on tumor cell migration by estimating the tumor cell dispersion speed for a given environment. Effect of tumor cell proliferation and migration: We study the effect of tumor cell proliferation and migration on the tumor’s invasive behavior by developing a simplified LGCA model of tumor growth. In particular, we derive the corresponding macroscopic dynamics and we calculate the tumor’s invasion speed in terms of tumor cell proliferation and migration rates. Moreover, we calculate the width of the invasive zone, where the majority of mitotic activity is concentrated, and it is found to be proportional to the invasion speed. Mechanisms of tumor invasion emergence: We investigate the mechanisms for the emergence of tumor invasion in the course of cancer progression. We conclude that the response of a microscopic intracellular mechanism (migration/proliferation dichotomy) to oxygen shortage, i.e. hypoxia, maybe responsible for the transition from a benign (proliferative) to a malignant (invasive) tumor. Computing in vivo tumor invasion: Finally, we propose an evolutionary algorithm that estimates the parameters of a tumor growth LGCA model based on time-series of patient medical data (in particular Magnetic Resonance and Diffusion Tensor Imaging data). These parameters may allow to reproduce clinically relevant tumor growth scenarios for a specific patient, providing a prediction of the tumor growth at a later time stage. / Krebszellen zeigen charakteristische Merkmale, die sie in einem schrittweisen Vorgang während der Karzinogenese erworben haben. Einige dieser Merkmale sind autonomes Wachstum, die Induktion von Angiogenese, Invasion und Metastasis. Der Schwerpunkt dieser Arbeit liegt auf der Tumorinvasion, einer der letzten Phasen der Tumorprogression. Die Tumorinvasion ensteht aus der kombinierten Wirkung von den Wechselwirkungen Tumorzelle-Zelle und Zelle-Mikroumgebung, die mit die Hilfe von mathematischer Analyse untersucht werden können. Zelluläre Automaten (CA) können als einfache Modelle von selbst-organisierenden komplexen Systemen betrachtet werden, in denen kollektives Verhalten aus einer Kombination von vielen interagierenden "einfachen" Komponenten entstehen kann. Insbesondere konzentrieren wir uns auf eine wichtige CA-Klasse, die sogenannten Zelluläre Gitter-Gas Automaten (LGCA). Im Gegensatz zu traditionellen CA bieten LGCA eine einfache und intuitive Umsetzung der Teilchen und Wechselwirkungen. Zusätzlich erleichtert die Struktur der LGCA die mathematische Analyse ihres Verhaltens. Die wichtigsten Werkzeuge der mathematischen Analyse der LGCA sind hier die Mean-field Approximation und die entsprechende Lattice - Boltzmann - Gleichung. Das wichtigste Ziel dieser Arbeit ist es, wichtige Aspekte der Tumorinvasion unter dem Mikroskop der mathematischen Modellierung und Analyse zu erforschen: Auswirkungen der Tumorumgebung: Wir stellen einen LGCA als mikroskopisches Modell der Tumorzellen-Migration in Verbindung mit einer mathematischen Beschreibung der verschiedenen Tumorumgebungen vor. Wir untersuchen die Auswirkungen der verschiedenen Tumorumgebungen (z. B. extrazellulären Matrix) auf die Migration von Tumorzellen dürch Schätzung der Tumorzellen-Dispersionsgeschwindigkeit in einem gegebenen Umfeld. Wirkung von Tumor-Zellenproliferation und Migration: Wir untersuchen die Wirkung von Tumorzellenproliferation und Migration auf das invasive Verhalten der Tumorzellen durch die Entwicklung eines vereinfachten LGCA Tumorwachstumsmodells. Wir leiten die entsprechende makroskopische Dynamik und berechnen die Tumorinvasionsgeschwindigkeit im Hinblick auf die Tumorzellenproliferation- und Migrationswerte. Darüber hinaus berechnen wir die Breite der invasiven Zone, wo die Mehrheit der mitotischer Aktivität konzentriert ist, und es wird festgestellt, dass diese proportional zu den Invasionsgeschwindigkeit ist. Mechanismen der Tumorinvasion Entstehung: Wir untersuchen Mechanismen, die für die Entstehung von Tumorinvasion im Verlauf des Krebs zuständig sind. Wir kommen zu dem Schluss, dass die Reaktion eines mikroskopischen intrazellulären Mechanismus (Migration/Proliferation Dichotomie) zu Sauerstoffmangel, d.h. Hypoxie, möglicheweise für den Übergang von einem gutartigen (proliferative) zu einer bösartigen (invasive) Tumor verantwortlich ist. Berechnung der in-vivo Tumorinvasion: Schließlich schlagen wir einen evolutionären Algorithmus vor, der die Parameter eines LGCA Modells von Tumorwachstum auf der Grundlage von medizinischen Daten des Patienten für mehrere Zeitpunkte (insbesondere die Magnet-Resonanz-und Diffusion Tensor Imaging Daten) ermöglicht. Diese Parameter erlauben Szenarien für einen klinisch relevanten Tumorwachstum für einen bestimmten Patienten zu reproduzieren, die eine Vorhersage des Tumorwachstums zu einem späteren Zeitpunkt möglich machen.

This work addresses properties of two-component lipid membranes on the experimentally relevant spatial scales of order of a micrometer and time intervals of order of a second by means of lattice-based Monte Carlo (MC) simulations. To be able to do that with reasonable computational efforts the lipid membrane is modeled as a square lattice of lipid molecules with next-neighbor interaction. This allows for efficient computation and thus provides a large-scale simulation with which it was possible to obtain important results previously not reported in simulation studies of lipid membranes. After properly tuning the next-neighbor interaction energies the simulation reproduces the experimental phase diagram of the DMPC/DSPC lipid system which is used as a model system in this work. Beyond that, the MC simulation provides a more detailed description of the phase behavior of the lipid mixture than the experimental data. It is found that, within a certain range of lipid compositions, the phase transition from the fluid phase to the fluid–gel phase coexistence proceeds via near-critical fluctuations, while for other lipid compositions this phase transition has a quasi-abrupt character. The complete combined state and component phase diagram is constructed by structure function analysis which confirms the existence of a critical point in the system. The dynamics of membrane coarsening after an abrupt temperature quench to the fluid–gel coexistence region of the phase diagram are studied. In this context, it is found that lateral diffusion of lipids plays an important role in the fluid–gel phase separation process. Dynamic scaling is observed only if the ratio of gel and fluid phase in the membrane stays constant in time. The line tension characterizing lipid domains in the fluid–gel coexistence region is found to be in the pN range thus matching values both predicted theoretically and measured experimentally. When approaching the critical point, the line tension, the inverse correlation length of fluid–gel spatial fluctuations, and the corresponding inverse order parameter susceptibility of the membrane vanish in agreement with recent experimental findings for model lipid membranes. By simulating single particle tracking and fluorescence correlation spectroscopy experiments it is found that in the presence of near-critical fluctuations lipid molecules show transient subdiffusive behavior, which is a new result important for understanding the origins of subdiffusion in cell membranes which are believed to be close to a critical point. The membrane–cytoskeleton interaction strongly affects phase separation, enhances subdiffusion, and eventually leads to hop diffusion of lipids. Thus, a minimum realistic model for membrane rafts showing the features of both microscopic phase separation and subdiffusion is established.

The theoretical study of nonlinear expectations is the focus of attention for applications in a variety of different fields — often with the objective to model systems under incomplete information. Especially in mathematical finance, advances in the theory of sublinear expectations (also referred to as coherent risk measures) lay the theoretical foundation for modern approaches to evaluations under the presence of Knightian uncertainty. In this book, we introduce and study a large class of jump-type processes for sublinear expectations, which can be interpreted as Lévy-type processes under uncertainty in their characteristics. Moreover, we establish an existence and uniqueness theory for related nonlinear, nonlocal Hamilton-Jacobi-Bellman equations with non-dominated jump terms.

In this thesis, we present a new existence result for Lévy-type processes. Lévy-type processes behave locally like a Lévy process, but the Lévy triplet may depend on the current position of the process. They can be characterized by their so-called symbol; this is the analogue of the characteristic exponent in the Lévy case. Using a parametrix construction, we prove the existence of Lévy-type processes with a given symbol under weak regularity assumptions on the regularity of the symbol. Applications range from existence results for stable-like processes and mixed processes to uniqueness results for Lévy-driven stochastic differential equations. Moreover, we discuss sufficient conditions for the existence of moments of Lévy-type processes and derive estimates for fractional moments.

Multivariate mixed Poisson processes are special multivariate counting processes whose coordinates are, in general, dependent. The first part of this thesis is devoted to properties which multivariate counting processes may possess. Such properties are, for example, the Markov property, the multinomial property and regularity. With regard to regularity we study the properties of transition probabilities and intensities. The second part of this thesis restricts the class of all multivariate counting processes by additional assumptions leading to different types of multivariate mixed Poisson processes which, however, are connected with each other. Using a multivariate version of the Bernstein-Widder theorem, it is shown that multivariate mixed Poisson processes are characterized by the multinomial property. Furthermore, regularity of multivariate mixed Poisson processes and properties of their moments are studied in detail. Throughout this thesis, two types of stability of properties of multivariate counting processes are studied: It is shown that most properties of a multivariate counting process are stable under certain linear transformations including the selection of single coordinates and summation of all coordinates. It is also shown that the different types of multivariate mixed Poisson processes under consideration are in a certain sense stable in time.

Self-organization of regular surface pattern under ion beam erosion was described in detail by Navez in 1962. Several years later in 1986 Bradley and Harper (BH) published the first self-consistent theory on this phenomenon based on the competition of surface roughening described by Sigmund's sputter theory and surface smoothing by Mullins-Herring diffusion. Many papers that followed BH theory introduced other processes responsible for the surface patterning e.g. viscous flow, redeposition, phase separation, preferential sputtering, etc. The present understanding is still not sufficient to specify the dominant driving forces responsible for self-organization. 3D atomistic simulations can improve the understanding by reproducing the pattern formation with the detailed microscopic description of the driving forces. 2D simulations published so far can contribute to this understanding only partially. A novel program package for 3D atomistic simulations called TRIDER (TRansport of Ions in matter with DEfect Relaxation), which unifies full collision cascade simulation with atomistic relaxation processes, has been developed. The collision cascades are provided by simulations based on the Binary Collision Approximation, and the relaxation processes are simulated with the 3D lattice kinetic Monte-Carlo method. This allows, without any phenomenological model, a full 3D atomistic description on experimental spatiotemporal scales. Recently discussed new mechanisms of surface patterning like ballistic mass drift or the dependence of the local morphology on sputtering yield are inherently included in our atomistic approach. The atomistic 3D simulations do not depend so much on experimental assumptions like reported 2D simulations or continuum theories. The 3D computer experiments can even be considered as 'cleanest' possible experiments for checking continuum theories. This work aims mainly at the methodology of a novel atomistic approach, showing that: (i) In general, sputtering is not the dominant driving force responsible for the ripple formation. Processes like bulk and surface defect kinetics dominate the surface morphology evolution. Only at grazing incidence the sputtering has been found to be a direct cause of the ripple formation. Bradley and Harper theory fails in explaining the ripple dynamics because it is based on the second-order-effect 'sputtering'. However, taking into account the new mechanisms, a 'Bradley-Harper equation' with redefined parameters can be derived, which describes pattern formation satisfactorily. (ii) Kinetics of (bulk) defects has been revealed as the dominating driving force of pattern formation. Constantly created defects within the collision cascade, are responsible for local surface topography fluctuation and cause surface mass currents. The mass currents smooth the surface at normal and close to normal ion incidence angles, while ripples appear first at incidence angles larger than 40°. The evolution of bimetallic interfaces under ion irradiation is another application of TRIDER described in this thesis. The collisional mixing is in competition with diffusion and phase separation. The irradiation with He ions is studied for two extreme cases of bimetals: (i) Irradiation of interfaces formed by immiscible elements, here Al and Pb. Ballistic interface mixing is accompanied by phase separation. Al and Pb nanoclusters show a self-ordering (banding) parallel to the interface. (ii) Irradiation of interfaces by intermetallics forming species, here Pt and Co. Well-ordered layers of phases of intermetallics appear in the sequence Pt/Pt3Co/PtCo/PtCo3/Co. The TRIDER program package has been proven to be an appropriate technique providing a complete picture of mixing mechanisms.