Meine Dissertation behandelt verschiedene neue rauschinduzierte Phänomene in anregbaren Neuronenmodellen, insbesondere solche mit FitzHugh-Nagumo Dynamik. Ich beschreibe das Auftreten von vibronischer Resonanz in anregbaren Systemen. Sowohl in einer anregbaren elektronischen Schaltung als auch im FitzHugh-Nagumo Modell zeige ich, daß eine optimale Amplitude einer hochfrequenten externen Kraft die Signalantwort bezüglich eines niederfrequenten Signals verbessert. Weiterhin wird der Einfluß von additivem Rauschen auf das Zusammenwirken von stochastischer und vibronischer Resonanz untersucht. Weiterhin untersuche ich Systeme, die sowohl oszillierende als auch anregbare Eigenschaften beinhalten und dadurch zwei interne Frequenzen aufweisen. Ich zeige, daß in solchen Systemen der Effekt der stochastischen Resonanz deutlich erhöht werden kann, wenn eine zusätzliche hochfrequente Kraft in Resonanz mit den kleinen Oszillationen unterhalb der Anregungsschwelle hinzugenommen wird. Es ist beachtenswert, daß diese Verstärkung der stochastischen Resonanz eine geringere Rauschintensität zum Erreichen des Optimums benötigt als die standartmäßige stochastische Resonanz in anregbaren Systemen. Ich untersuche Frequenzselektivität bei der rauschinduzierten Signalverarbeitung von Signalen unterhalb der Anregungsschwelle in Systemen mit vielen rauschunterstützten stochastischen Attraktoren. Diese neuen Attraktoren mit abweichenden gemittelten Perioden weisen auch unterschiedliche Phasenbeziehungen zwischen den einzelnen Elementen auf. Ich zeige, daß die Signalantwort des gekoppelten Systems unter verschiedenen Rauscheinwirkungen deutlich verbessert oder auch reduziert werden kann durch das Treiben einzelner Elemente in Resonanz mit diesen neuen Resonanzfrequenzen, die mit passenden Phasenbeziehungen korrespondieren. Weiterhin konnte ich einen rauschinduzierten Phasenübergang von einem selbstoszillierenden System zu einem anregbaren System nachweisen. Dieser Übergang erfolgt durch eine rauschinduzierte Stabilisierung eines deterministisch instabilen Fixpunktes der lokalen Dynamik, während die gesamte Phasenraumstruktur des Systems erhalten bleibt. Die gemeinsame Wirkung von Kopplung und Rauschen führt zu einem neuen Typ von Phasenübergängen und bewirkt eine Stabilisierung des Systems. Das sich daraus ergebende rauschinduziert anregbare Regime zeigt charakteristische Eigenschaften von klassisch anregbaren Systemen, wie stochastische Resonanz und Wellenausbreitung. Dieser rauschinduzierte Phasenübergang ermöglicht dadurch die Übertragung von Signalen durch ansonsten global oszillierende Systeme und die Kontrolle der Signalübertragung durch Veränderung der Rauschintensität. Insbesondere eröffnen diese theoretischen Ergebnisse einen möglichen Mechanismus zur Unterdrückung unerwünschter globaler Oszillationen in neuronalen Netzwerken, welche charakteristisch für abnorme medizinische Zustände, wie z.B. bei der Parkinson′schen Krankheit oder Epilepsie, sind. Die Wirkung von Rauschen würde dann wieder die Anregbarkeit herstellen, die den normalen Zustand der erkrankten Neuronen darstellt. / My thesis is concerned with several new noise-induced phenomena in excitable neural models, especially those with FitzHugh-Nagumo dynamics. In these effects the fluctuations intrinsically present in any complex neural network play a constructive role and improve functionality. I report the occurrence of Vibrational Resonance in excitable systems. Both in an excitable electronic circuit and in the FitzHugh-Nagumo model, I show that an optimal amplitude of high-frequency driving enhances the response of an excitable system to a low-frequency signal. Additionally, the influence of additive noise and the interplay between Stochastic and Vibrational Resonance is analyzed. Further, I study systems which combine both oscillatory and excitable properties, and hence intrinsically possess two internal frequencies. I show that in such a system the effect of Stochastic Resonance can be amplified by an additional high-frequency signal which is in resonance with the oscillatory frequency. This amplification needs much lower noise intensities than for conventional Stochastic Resonance in excitable systems. I study frequency selectivity in noise-induced subthreshold signal processing in a system with many noise-supported stochastic attractors. I show that the response of the coupled elements at different noise levels can be significantly enhanced or reduced by forcing some elements into resonance with these new frequencies which correspond to appropriate phase-relations. A noise-induced phase transition to excitability is reported in oscillatory media with FitzHugh-Nagumo dynamics. This transition takes place via noise-induced stabilization of a deterministically unstable fixed point of the local dynamics, while the overall phase-space structure of the system is maintained. The joint action of coupling and noise leads to a different type of phase transition and results in a stabilization of the system. The resulting noise-induced regime is shown to display properties characteristic of excitable media, such as Stochastic Resonance and wave propagation. This effect thus allows the transmission of signals through an otherwise globally oscillating medium. In particular, these theoretical findings suggest a possible mechanism for suppressing undesirable global oscillations in neural networks (which are usually characteristic of abnormal medical conditions such as Parkinson′s disease or epilepsy), using the action of noise to restore excitability, which is the normal state of neuronal ensembles.

<img src="http://vg00.met.vgwort.de/na/806c85cec18906a64e06" width="1" height="1" alt=""> Subject of this work is the possibility to synchronize nonlinear systems via correlated noise and automatic control. The thesis is divided into two parts.<br> The first part is motivated by field studies on feral sheep populations on two islands of the St. Kilda archipelago, which revealed strong correlations due to environmental noise. For a linear system the population correlation equals the noise correlation (Moran effect). But there exists no systematic examination of the properties of nonlinear maps under the influence of correlated noise. Therefore, in the first part of this thesis the noise-induced correlation of logistic maps is systematically examined. For small noise intensities it can be shown analytically that the correlation of quadratic maps in the fixed-point regime is always smaller than or equal to the noise correlation. In the period-2 regime a Markov model explains qualitatively the main dynamical characteristics. Furthermore, two different mechanisms are introduced which lead to a higher correlation of the systems than the environmental correlation. The new effect of "correlation resonance" is described, i. e. the correlation yields a maximum depending on the noise intensity. <br> In the second part of the thesis an automatic control method is presented which synchronizes different systems in a robust way. This method is inspired by phase-locked loops and is based on a feedback loop with a differential control scheme, which allows to change the phases of the controlled systems. The effectiveness of the approach is demonstrated for controlled phase synchronization of regular oscillators and foodweb models. / Gegenstand der Arbeit ist die Möglichkeit der Synchronisierung von nichtlinearen Systemen durch korreliertes Rauschen und automatische Kontrolle. Die Arbeit gliedert sich in zwei Teile.<br> Der erste Teil ist motiviert durch Feldstudien an wilden Schafspopulationen auf zwei Inseln des St. Kilda Archipels, die starke Korrelationen aufgrund von Umwelteinflüssen zeigen. In einem linearen System entspricht die Korrelation der beiden Populationen genau der Rauschkorrelation (Moran-Effekt). Es existiert aber noch keine systematische Untersuchung des Verhaltens nichtlinearer Abbildungen unter dem Einfluss korrelierten Rauschens. Deshalb wird im ersten Teils dieser Arbeit systematisch die rauschinduzierte Korrelation zweier logistischer Abbildungen in den verschiedenen dynamischen Bereichen untersucht. Für kleine Rauschintensitäten wird analytisch gezeigt, dass die Korrelation von quadratischen Abbildungen im Fixpunktbereich immer kleiner oder gleich der Rauschkorrelation ist. Im Periode-2 Bereich beschreibt ein Markov-Modell qualitativ die wichtigsten dynamischen Eigenschaften. Weiterhin werden zwei unterschiedliche Mechanismen vorgestellt, die dazu führen, dass die beiden ungekoppelten Systeme stärker als ihre Umwelt korreliert sein können. Dabei wird der neue Effekt der "correlation resonance" aufgezeigt, d. h. es ergibt sich eine Resonanzkurve der Korrelation in Abbhängkeit von der Rauschstärke. <br> Im zweiten Teil der Arbeit wird eine automatische Kontroll-Methode präsentiert, die es ermöglicht sehr unterschiedliche Systeme auf robuste Weise in Phase zu synchronisieren. Die Methode ist angelehnt an Phase-locked-Loops und basiert auf einer Rückkopplungsschleife durch einen speziellen Regler, der es erlaubt die Phasen der kontrollierten Systeme zu ändern. Die Effektivität dieser Methode zur Kontrolle der Phasensynchronisierung wird an regulären Oszillatoren und an Nahrungskettenmodellen demonstriert.

In biological cells, the long-range intracellular traffic is powered by molecular motors which transport various cargos along microtubule filaments. The microtubules possess an intrinsic direction, having a 'plus' and a 'minus' end. Some molecular motors such as cytoplasmic dynein walk to the minus end, while others such as conventional kinesin walk to the plus end. Cells typically have an isopolar microtubule network. This is most pronounced in neuronal axons or fungal hyphae. In these long and thin tubular protrusions, the microtubules are arranged parallel to the tube axis with the minus ends pointing to the cell body and the plus ends pointing to the tip. In such a tubular compartment, transport by only one motor type leads to 'motor traffic jams'. Kinesin-driven cargos accumulate at the tip, while dynein-driven cargos accumulate near the cell body. We identify the relevant length scales and characterize the jamming behaviour in these tube geometries by using both Monte Carlo simulations and analytical calculations. A possible solution to this jamming problem is to transport cargos with a team of plus and a team of minus motors simultaneously, so that they can travel bidirectionally, as observed in cells. The presumably simplest mechanism for such bidirectional transport is provided by a 'tug-of-war' between the two motor teams which is governed by mechanical motor interactions only. We develop a stochastic tug-of-war model and study it with numerical and analytical calculations. We find a surprisingly complex cooperative motility behaviour. We compare our results to the available experimental data, which we reproduce qualitatively and quantitatively. / In biologischen Zellen transportieren molekulare Motoren verschiedenste Frachtteilchen entlang von Mikrotubuli-Filamenten. Die Mikrotubuli-Filamente besitzen eine intrinsische Richtung: sie haben ein "Plus-" und ein "Minus-"Ende. Einige molekulare Motoren wie Dynein laufen zum Minus-Ende, während andere wie Kinesin zum Plus-Ende laufen. Zellen haben typischerweise ein isopolares Mikrotubuli-Netzwerk. Dies ist besonders ausgeprägt in neuronalen Axonen oder Pilz-Hyphen. In diesen langen röhrenförmigen Ausstülpungen liegen die Mikrotubuli parallel zur Achse mit dem Minus-Ende zum Zellkörper und dem Plus-Ende zur Zellspitze gerichtet. In einer solchen Röhre führt Transport durch nur einen Motor-Typ zu "Motor-Staus". Kinesin-getriebene Frachten akkumulieren an der Spitze, während Dynein-getriebene Frachten am Zellkörper akkumulieren. Wir identifizieren die relevanten Längenskalen und charakterisieren das Stauverhalten in diesen Röhrengeometrien mit Hilfe von Monte-Carlo-Simulationen und analytischen Rechnungen. Eine mögliche Lösung für das Stauproblem ist der Transport mit einem Team von Plus- und einem Team von Minus-Motoren gleichzeitig, so dass die Fracht sich in beide Richtungen bewegen kann. Dies wird in Zellen tatsächlich beobachtet. Der einfachste Mechanismus für solchen bidirektionalen Transport ist ein "Tauziehen" zwischen den beiden Motor-Teams, das nur mit mechanischer Interaktion funktioniert. Wir entwickeln ein stochastisches Tauzieh-Modell, das wir mit numerischen und analytischen Rechnungen untersuchen. Es ergibt sich ein erstaunlich komplexes Motilitätsverhalten. Wir vergleichen unsere Resultate mit den vorhandenen experimentellen Daten, die wir qualitativ und quantitativ reproduzieren.

Cargo transport by molecular motors is ubiquitous in all eukaryotic cells and is typically driven cooperatively by several molecular motors, which may belong to one or several motor species like kinesin, dynein or myosin. These motor proteins transport cargos such as RNAs, protein complexes or organelles along filaments, from which they unbind after a finite run length. Understanding how these motors interact and how their movements are coordinated and regulated is a central and challenging problem in studies of intracellular transport. In this thesis, we describe a general theoretical framework for the analysis of such transport processes, which enables us to explain the behavior of intracellular cargos based on the transport properties of individual motors and their interactions. Motivated by recent in vitro experiments, we address two different modes of transport: unidirectional transport by two identical motors and cooperative transport by actively walking and passively diffusing motors. The case of cargo transport by two identical motors involves an elastic coupling between the motors that can reduce the motors’ velocity and/or the binding time to the filament. We show that this elastic coupling leads, in general, to four distinct transport regimes. In addition to a weak coupling regime, kinesin and dynein motors are found to exhibit a strong coupling and an enhanced unbinding regime, whereas myosin motors are predicted to attain a reduced velocity regime. All of these regimes, which we derive both by analytical calculations and by general time scale arguments, can be explored experimentally by varying the elastic coupling strength. In addition, using the time scale arguments, we explain why previous studies came to different conclusions about the effect and relevance of motor-motor interference. In this way, our theory provides a general and unifying framework for understanding the dynamical behavior of two elastically coupled molecular motors. The second mode of transport studied in this thesis is cargo transport by actively pulling and passively diffusing motors. Although these passive motors do not participate in active transport, they strongly enhance the overall cargo run length. When an active motor unbinds, the cargo is still tethered to the filament by the passive motors, giving the unbound motor the chance to rebind and continue its active walk. We develop a stochastic description for such cooperative behavior and explicitly derive the enhanced run length for a cargo transported by one actively pulling and one passively diffusing motor. We generalize our description to the case of several pulling and diffusing motors and find an exponential increase of the run length with the number of involved motors. / Lastentransport mittels Motorproteinen ist ein grundlegender Mechanismus aller eukaryotischen Zellen und wird üblicherweise von mehreren Motoren kooperativ durchgeführt, die zu einer oder zu verschiedenen Motorarten wie Kinesin, Dynein oder Myosin gehören. Diese Motoren befördern Lasten wie zum Beispiel RNAs, Proteinkomplexe oder Organellen entlang Filamenten, von denen sie nach einer endlichen zurückgelegten Strecke abbinden. Es ist ein zentrales und herausforderndes Problem zu verstehen, wie diese Motoren wechselwirken und wie ihre Bewegungen koordiniert und reguliert werden. In der vorliegenden Arbeit wird eine allgemeine theoretische Herangehensweise zur Untersuchung solcher Transportprozesse beschrieben, die es uns ermöglicht, das Verhalten von intrazellularem Transport, ausgehend von den Transporteigenschaften einzelner Motoren und ihren Wechselwirkungen, zu verstehen. Wir befassen uns mit zwei Arten kooperativen Transports, die auch kürzlich in verschiedenen in vitro-Experimenten untersucht wurden: (i) gleichgerichteter Transport mit zwei identischen Motorproteinen und (ii) kooperativer Transport mit aktiv schreitenden und passiv diffundierenden Motoren. Beim Lastentransport mit zwei identischen Motoren sind die Motoren elastisch gekoppelt, was eine Verminderung ihrer Geschwindigkeit und/oder ihrer Bindezeit am Filament hervorrufen kann. Wir zeigen, dass solch eine elastische Kopplung im Allgemeinen zu vier verschiedenen Transportcharakteristiken führt. Zusätzlich zu einer schwachen Kopplung, können bei Kinesinen und Dyneinen eine starke Kopplung und ein verstärktes Abbinden auftreten, wohingegen bei Myosin Motoren eine verminderte Geschwindigkeit vorhergesagt wird. All diese Transportcharakteristiken, die wir mit Hilfe analytischer Rechnungen und Zeitskalenargumenten herleiten, können durch Änderung der elastischen Kopplung experimentell untersucht werden. Zusätzlich erklären wir anhand der Zeitskalenargumente, warum frühere Untersuchungen zu unterschiedlichen Erkenntnissen über die Auswirkung und die Wichtigkeit der gegenseitigen Beeinflussung der Motoren gelangt sind. Auf diese Art und Weise liefert unsere Theorie eine allgemeine und vereinheitlichende Beschreibung des dynamischen Verhaltens von zwei elastisch gekoppelten Motorproteinen. Die zweite Art von Transport, die in dieser Arbeit untersucht wird ist der Lastentransport durch aktiv ziehende und passiv diffundierende Motoren. Obwohl die passiven Motoren nicht zum aktiven Transport beitragen, verlängern sie stark die zurückgelegte Strecke auf dem Filament. Denn wenn ein aktiver Motor abbindet, wird das Lastteilchen immer noch am Filament durch den passiven Motor festgehalten, was dem abgebundenen Motor die Möglichkeit gibt, wieder an das Filament anzubinden und den aktiven Transport fortzusetzen. Für dieses kooperative Verhalten entwickeln wir eine stochastische Beschreibung und leiten explizit die verlängerte Transportstrecke für einen aktiv ziehenden und einen passiv diffundierenden Motor her. Wir verallgemeinern unsere Beschreibung für den Fall von mehreren ziehenden und diffundierenden Motoren und finden ein exponentielles Anwachsen der zurückgelegten Strecke in Abhängigkeit von der Anzahl der beteiligten Motoren.

The human sense of hearing is characterized by its exquisite sensitivity, sharp frequency selectivity, and wide dynamic range. These features depend on an active process that in the inner ear boosts vibrations evoked by auditory stimuli. Spontaneous otoacoustic emissions constitute a demonstrative manifestation of this physiologically vulnerable mechanism. In the cochlea, sensory hair bundles transduce sound-induced vibrations into neural signals. Hair bundles can power mechanical movements of their tip, oscillate spontaneously, and operate as tuned nonlinear amplifiers of weak periodic stimuli. Active hair-bundle motility constitutes a promising candidate with respect to the biophysical implementation of the active process underlying human hearing. The responsiveness of isolated hair bundles, however, is seriously hampered by intrinsic fluctuations. In this thesis, we present theoretical and experimental results concerning the noise-imposed limitations of nonlinear amplification by active sensory hair bundles. We analyze the effect of noise within the framework of a stochastic description of hair-bundle dynamics and relate our findings to generic aspects of the stochastic dynamics of oscillatory systems. Hair bundles in vivo are often elastically coupled by overlying gelatinous membranes. In addition to theoretical results concerning the dynamics of elastically coupled hair bundles, we report on an experimental study. We have interfaced dynamic force clamp performed on a hair bundle from the sacculus of the bullfrog with real-time stochastic simulations of hair-bundle dynamics. By means of this setup, we could couple a hair bundle to two virtual neighbors, called cyber clones. Our theoretical and experimental work shows that elastic coupling leads to an effective noise reduction. Coupled hair bundles exhibit an increased coherence of spontaneous oscillations and an enhanced amplification gain. We therefore argue that elastic coupling by overlying membranes constitutes a morphological specialization for reducing the detrimental effect of intrinsic fluctuations.

The human sense of hearing is characterized by its exquisite sensitivity, sharp frequency selectivity, and wide dynamic range. These features depend on an active process that in the inner ear boosts vibrations evoked by auditory stimuli. Spontaneous otoacoustic emissions constitute a demonstrative manifestation of this physiologically vulnerable mechanism. In the cochlea, sensory hair bundles transduce sound-induced vibrations into neural signals. Hair bundles can power mechanical movements of their tip, oscillate spontaneously, and operate as tuned nonlinear amplifiers of weak periodic stimuli. Active hair-bundle motility constitutes a promising candidate with respect to the biophysical implementation of the active process underlying human hearing. The responsiveness of isolated hair bundles, however, is seriously hampered by intrinsic fluctuations. In this thesis, we present theoretical and experimental results concerning the noise-imposed limitations of nonlinear amplification by active sensory hair bundles. We analyze the effect of noise within the framework of a stochastic description of hair-bundle dynamics and relate our findings to generic aspects of the stochastic dynamics of oscillatory systems. Hair bundles in vivo are often elastically coupled by overlying gelatinous membranes. In addition to theoretical results concerning the dynamics of elastically coupled hair bundles, we report on an experimental study. We have interfaced dynamic force clamp performed on a hair bundle from the sacculus of the bullfrog with real-time stochastic simulations of hair-bundle dynamics. By means of this setup, we could couple a hair bundle to two virtual neighbors, called cyber clones. Our theoretical and experimental work shows that elastic coupling leads to an effective noise reduction. Coupled hair bundles exhibit an increased coherence of spontaneous oscillations and an enhanced amplification gain. We therefore argue that elastic coupling by overlying membranes constitutes a morphological specialization for reducing the detrimental effect of intrinsic fluctuations.

We consider p independent Brownian motions in ℝd. We assume that p ≥ 2 and p(d- 2) < d. Let ℓt denote the intersection measure of the p paths by time t, i.e., the random measure on ℝd that assigns to any measurable set A ⊂ ℝd the amount of intersection local time of the motions spent in A by time t. Earlier results of Chen derived the logarithmic asymptotics of the upper tails of the total mass ℓt(ℝd) as t →∞. In this paper, we derive a large-deviation principle for the normalised intersection measure t-pℓt on the set of positive measures on some open bounded set B ⊂ ℝd as t →∞ before exiting B. The rate function is explicit and gives some rigorous meaning, in this asymptotic regime, to the understanding that the intersection measure is the pointwise product of the densities of the normalised occupation times measures of the p motions. Our proof makes the classical Donsker-Varadhan principle for the latter applicable to the intersection measure. A second version of our principle is proved for the motions observed until the individual exit times from B, conditional on a large total mass in some compact set U ⊂ B. This extends earlier studies on the intersection measure by König and Mörters.

Cellular decision-making help cells to infer functionally different phenotypes in response to microenvironmental cues and noise present in the system and the environment, with or without genetic change. In Cellular Biology, there exists a list of open questions such as, how individual cell decisions influence the dynamics at the population level (an organization of indistinguishable cells) and at the tissue level (a group of nearly identical cells and their corresponding extracellular matrix which simultaneously accomplish a set of biological operations)? As collective cell migration originates from local cellular orientation decisions, can one generate a mathematical model for collective cell migration phenomena without elusive undiscovered biophysical/biochemical mechanisms and further predict the pattern formations which originates inside the collective cell migration? how optimal microenvironmental sensing is related to differentiated tissue at the spatial scale ? How cell sensing radius and total entropy production (which precisely helps us to understand the operating regimes where cells can take decisions about their future fate) is correlated, and how can one understand the limits of sensing radius at robust tissue development ? To partially tackle these sets of questions, the LEUP (Least microEnvironmental Uncertainty Principle) hypothesis has been applied to different biological scenaros. At first, the LEUP has been enforced to understand the spatio-temporal behavior of a tissue exhibiting phenotypic plasticity (it is a prototype of cell decision-making). Here, two cases have been rigorously studied i.e., migration/resting and migration/proliferation plasticity which underlie the epithelial-mesenchymal transition (EMT) and the Go-or-Grow dichotomy. On the one hand, for the Go-or-Rest plasticity, a bistable switching mechanism between a diffusive (fluid) and an epithelial (solid) tissue phase has been observed from an analogous mean-field approximation which further depends on the sensitivity of the phenotypes to the microenvironment. However, on the other hand, for the Go-or-Grow plasticity, the possibility of Turing pattern formation is inspected for the “solid” tissue phase and its relation to the parameters of the LEUP-driven cell decisions. Later, LEUP hypothesis has been suggested in the area of collective cell migration such that it can provide a tool for a generative mathematical model of collective migration without precise knowledge about the mechanistic details, where the famous Vicsek model is a special case. In this generative model of collective cell migration, the origin of pattern formation inside collective cell migration has been investigated. Moreover, this hypothesis helps to construct a mathematical model for the collective behavior of spherical \textit{Serratia marcescens} bacteria, where the basic understanding of migration mechanisms remain unknown. Furthermore, LEUP has been applied to understand tissue robustness, which in turn shows the way how progenitor cell fate decisions are associated with environmental sensing. The regulation of environmental sensing drives the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue, which are verified later with the experimental data. LEUP driven stochastic thermodynamic formalism also shows that the thermodynamic robustness of differentiated tissues depends on cell metabolism, cell sensing properties and the limits of the cell sensing radius, which further ensures the robustness of differentiated tissue spatial order. Finally, all important results of the thesis have been encapsulated and the extension of the LEUP has been discussed.:Contents Statement of authorship vii Abstract ix I. Introduction to cell decision-making 1 1. What is cell decision-making ? 3 1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2. Examplesofcelldecision-making. . . . . . . . . . . . . . . . . . . . . . 4 1.2.1. PhenotypicPlasticity . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2. Cellularmigration:orientationdecisions . . . . . . . . . . . . . 5 1.2.3. Celldifferentiation . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3. Challengesandopenquestions . . . . . . . . . . . . . . . . . . . . . . 7 1.4. Solutionstrategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.5. Structureofthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 II. Least microEnvironmental Uncertainty Principle (LEUP) 11 2. Least microEnvironmental Uncertainty Principle (LEUP) 13 2.1. HypothesisbehindLEUP . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2. Mathematicalformulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1. CellasBayesiandecisionmaker . . . . . . . . . . . . . . . . . . 14 2.2.2. VariationalprincipleforLEUP . . . . . . . . . . . . . . . . . . . . 16 III. LEUP in biological problems 17 3. Phenotypic plasticity : dynamics at the level of tissue from individual cell decisions 19 3.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2. Individualbasedmodel(IBM) . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3. Mean-fieldapproximation . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1. Phenotypicswitchingdynamics . . . . . . . . . . . . . . . . . . 26 3.3.2. Cellmigrationdynamics . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.3. Superpositionofphenotypicswitchingdynamicsandcellmi- gration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4. Spatio-temporaldynamicsofcellmigration/proliferationplasticity . . 28 3.4.1. CaseI:Largeinteractionradius . . . . . . . . . . . . . . . . . . 29 3.4.2. CaseII:Finiteinteractionradius . . . . . . . . . . . . . . . . . . 30 3.4.3. Phenotypicswitchingdynamicsintheabsenceofmicroenvi- ronmentalsensing . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4. Cellular orientation decisions: origin of pattern formations in collective cell migrations 39 4.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.1.1. Self-propelledparticlemodelwithleupbaseddecision-making 41 4.1.2. Orderparametersandobservables . . . . . . . . . . . . . . . . 42 4.1.3. Statisticaltest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2. ComparisonwithVicsekmodel . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1. Patternsindifferentparameterregimes . . . . . . . . . . . . . 45 4.3. Application:thesphericalbacteriacase. . . . . . . . . . . . . . . . . . 47 4.4. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5. Cell differentiation and sensing: tissue robustness from optimal environ- mental sensing 53 5.1. LEUPbasedmathematicalmodelforcelldifferentiation . . . . . . . . 56 5.1.1. StatisticalresultsfromLEUP . . . . . . . . . . . . . . . . . . . . 59 5.2. RelationbetweenLEUPandcellsensing . . . . . . . . . . . . . . . . . 60 5.3. LEUPdrivenfluctuationtheorem: confirmsthethermodynamicro- bustnessofdifferentiatedtissues . . . . . . . . . . . . . . . . . . . . . 61 5.3.1. Application: differentiated photoreceptor mosaics are ther- modynamicallyrobust . . . . . . . . . . . . . . . . . . . . . . . . 65 5.4. Thelimitforcellsensingradius . . . . . . . . . . . . . . . . . . . . . . . 67 5.4.1. Application:Theaveragesensingradiusoftheavianconecell 69 5.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Discussions 75 7. Supplementary Material 91 8. Erklärung 115

Cancer as a disease causes about 8.8 million deaths worldwide per year, a number that will largely increase in the next decades. Although the cellular processes involved in tumor emergence are more and more understood, the implications of specific changes at the cellular scale on tumor emergence at the tissue scale remain elusive. Main reasons for this lack of understanding are that the cellular processes are often hardly observable especially in the early phase of tumor development and that the interplay between cellular and tissue scale is difficult to deduce. Cell-based mathematical models provide a valuable tool to investigate in which way observable phenomena on the tissue scale develop by cellular processes. The implications of these models can elucidate underlying mechanisms and generate quantitative predictions that can be experimentally validated. In this thesis, we infer the role of genetic and phenotypic cell changes on tumor development with the help of cell-based Markov chain models which are calibrated by tissue-scale data. In the first part, we utilize data on the diagnosed fractions of benign and malignant tumor subtypes to unravel the consequences of genetic cell changes on tumor development. We introduce extensions of Moran models to investigate two specific biological questions. First, we evaluate the tumor regression behavior of pilocytic astrocytoma which represents the most common brain tumor in children and young adults. We formulate a Moran model with two absorbing states representing different subtypes of this tumor, derive the absorption probabilities in these states and calculate the tumor regression probability within the model. This analysis allows to predict the chance for tumor regression in dependency of the remaining tumor size and implies a different clinical resection strategy for pilocytic astrocytoma compared to other brain tumors. Second, we shed light on the hardly observable early cellular dynamics of tumor development and its consequences on the emergence of different tumor subtypes on the tissue scale. For this purpose, we utilize spatial and non-spatial Moran models with two absorbing states which describe both benign and malignant tumor subtypes and estimate lower and upper bounds for the range of cellular competition in different tissues. Our results suggest the existence of small and tissue-specific tumor-originating niches in which the fate of tumor development is decided long before a tumor manifests. These findings might help to identify the tumor-originating cell types for different cancer types. From a theoretical point of view, the novel analytical results regarding the absorption behavior of our extended Moran models contribute to a better understanding of this model class and have several applications also beyond the scope of this thesis. The second part is devoted to the investigation of the role of phenotypic plasticity of cancer cells in tumor development. In order to understand how phenotypic heterogeneity in tumors arises we describe cell state changes by a Markov chain model. This model allows to quantify the cell state transitions leading to the observed heterogeneity from experimental tissue-scale data on the evolution of cell state proportions. In order to bridge the gap between mathematical modeling and the analysis of such data, we developed an R package called CellTrans which is freely available. This package automatizes the whole process of mathematical modeling and can be utilized to (i) infer the transition probabilities between different cell states, (ii) predict cell line compositions at a certain time, (iii) predict equilibrium cell state compositions and (iv) estimate the time needed to reach this equilibrium. We utilize publicly available data on the evolution of cell compositions to demonstrate the applicability of CellTrans. Moreover, we apply CellTrans to investigate the observed cellular phenotypic heterogeneity in glioblastoma. For this purpose, we use data on the evolution of glioblastoma cell line compositions to infer to which extent the heterogeneity in these tumors can be explained by hierarchical phenotypic transitions. We also demonstrate in which way our newly developed R package can be utilized to analyze the influence of different micro-environmental conditions on cell state proportions. Summarized, this thesis contributes to gain a better understanding of the consequences of both genetic and phenotypic cell changes on tumor development with the help of Markov chain models which are motivated by the specific underlying biological questions. Moreover, the analysis of the novel Moran models provides new theoretical results, in particular regarding the absorption behavior of the underlying stochastic processes.

Sowohl in der neuronalen als auch in der Kalzium Signalübertragung werden Informationen durch kurze Pulse oder Spikes, übertragen. Obwohl beide Systeme grundlegende Eigenschaften der Spike-Erzeugung teilen, wurden Integrate-and-fire (IF)-Modelle bisher nur auf neuronale Systeme angewendet. Diese Modelle bleiben auch dann behandelbar, wenn sie um Prozesse erweitert werden, die in Übereinstimmung mit Experimenten Spike-Zeiten mit korrelierten Interspike-Intervallen (ISI) erzeugen. Die statistische Analyse solcher nicht erneuerbarer Modelle ist Gegenstand dieser Arbeit. Das zweite Kapitel konzentriert sich auf die Berechnung des seriellen Korrelationskoeffizienten (SCC) in neuronalen Systemen. Es wird ein adaptives Modell betrachtet, das durch einen korrelierten Eingangsstrom getrieben wird. Es zeigt sich, dass neben den langsamen Prozessen auch die Dynamik des Modells den SCC bestimmt. Obwohl die Theorie für schwach gestörte IF-Modelle entwickelt wurde, kann sie auch auf stärker gestörte leitfähigkeitsbasierte Modelle angewendet werden und ist damit in der Lage, ein breites Spektrum biophysikalischer Situationen zu beschreiben. Im dritten Kapitel wird ein IF-Modell zur Beschreibung von Kalzium-Spikes formuliert, das die stochastische Freisetzung von Kalzium aus dem endoplasmatischen Retikulum (ER) und dessen Entleerung berücksichtigt. Die beobachtete Zeitskalentrennung zwischen Kalziumfreisetzung und Spikegenerierung motiviert eine Diffusionsnäherung, die eine analytische Behandlung des Modells ermöglicht. Die experimentell beobachtete Transiente, in der sich die ISIs einem stationären Wert annähern, kann durch die Entleerung des ER beschrieben werden. Es wird untersucht, wie die Statistiken der Transienten mit den stationären Intervallkorrelationen zusammenhängen. Es zeigt sich, dass eine stärkere Anpassung der Intervalle und eine kurze Transiente mit stärkeren Korrelationen einhergehen. Der Vergleich mit experimentellen Daten bestätigt diese Trends qualitativ. / In both neuronal and calcium signaling, information is transmitted by short pulses, so-called spikes. Although both systems share some basic principles of spike generation, integrate-and-fire (IF) models have so far only been applied to neuronal systems. These models remain analytically tractable even when extended to include processes that lead to the generation of spike times with correlated interspike intervals (ISIs) as observed in experiments. The statistical analysis of such non-renewal models is the subject of this thesis. In the second chapter we focus on the calculation of the serial correlation coefficient (SCC) in neural systems. We consider an adaptive model driven by a correlated input current. We show that in addition to the two slow processes, the dynamics of the model also determines the SCC. Although the theory is developed for weakly perturbed IF models, it can also be applied to more strongly perturbed conductance-based models and is thus able to account for a wide range of biophysical situations. In the third chapter, we formulate an IF model to describe the generation of calcium spikes, taking into account the stochastic release of calcium from the endoplasmic reticulum (ER) and its depletion. The observed time-scale separation between calcium release and spike generation motivates a diffusion approximation that allows an analytical treatment of the model. The experimentally observed transient, during which the ISIs approach a steady state value, can be captured by the depletion of the ER. We study how the transient ISI statistics are related to the stationary interval correlations. We show that a stronger adaptation of the intervals as well as a short transient are associated with stronger interval correlations. Comparison with experimental data qualitatively confirms these trends.