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The search for deterministic chaos in economic and financial time series has attracted much interest over the past decade. However, clear evidence of chaotic structures is usually prevented by large random components in the time series. In the first part of this paper we show that even if a sophisticated algorithm estimating and testing the positivity of the largest Lyapunov exponent is applied to time series generated by a stochastic dynamical system or a return series of a stock index, the results are difficult to interpret. We conclude that the notion of sensitive dependence on initial conditions as it has been developed for deterministic dynamics, can hardly be transfered into a stochastic context. Therefore, in the second part of the paper our starting point for measuring dependencies for stochastic dynamics is a distributional characterization of the dynamics, e.g. by heteroskedastic models for economic and financial time series. We adopt a sensitivity measure proposed in the literature which is an information-theoretic measure of the distance between probability density functions. This sensitivity measure is well defined for stochastic dynamics, and it can be calculated analytically for the classes of stochastic dynamics with conditional normal distributions of constant and state-dependent variance. In particular, heteroskedastic return series models such as ARCH and GARCH models are investigated. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

In this thesis, I present two methods of fast variable elimination in stochastic systems. Their application to models of population dynamics from ecology, epidemiology and population genetics, is explored. In each application, care is taken to develop the models at the microscale, in terms of interactions between individuals. Such an approach leads to well-defined stochastic systems for finite population sizes. These systems are then approximated at the mesoscale, and expressed as stochastic differential equations. It is in this setting the elimination techniques are developed. In each model a deterministically stable state is assumed to exist, about which the system is linearised. The eigenvalues of the system's Jacobian are used to identify the existence of a separation of timescales. The fast and slow directions are then given locally by the associated eigenvectors. These are used as approximations for the fast and slow directions in the full non-linear system. The general aim is then to remove these fast degrees of freedom and thus arrive at an approximate, reduced-variable description of the dynamics on a slow subspace of the full system. In the first of the methods introduced, the conditioning method, the noise of the system is constrained so that it cannot leave the slow subspace. The technique is applied to an ecological model and a susceptible-exposed-infectious-recovered epidemiological model, in both instances providing a reduced system which preserves the behaviour of the full model to high precision. The second method is referred to as the projection matrix method. It isolates the components of the noise on the slow subspace to provide its reduced description. The method is applied to a generalised Moran model of population genetics on islands, between which there is migration. The model is successfully reduced from a system in as many variables as there are islands, to an effective description in a single variable. The same methodology is later applied to the Lotka-Volterra competition model, which is found under certain conditions to behave as a Moran model. In both cases the agreement between the reduced system and stochastic simulations of the full model is excellent. It is stressed that the ideas behind both the conditioning and projection matrix methods are simple, their application systematic, and the results in very good agreement with simulations for a range of parameter values. When the methods are compared however, the projection matrix method is found in general to provide better results.

Gene transcription in individual living cells is inevitably a stochastic and dynamic process. Little is known about how cells and organisms learn to balance the fidelity of transcriptional control and the stochasticity of transcription dynamics. In an effort to elucidate the contribution of environmental signals to this intricate balance, a Three State Model was recently proposed, and the transcription system was assumed to transit among three different functional states randomly. In this work, we employ this model to demonstrate how the stochastic dynamics of gene transcription can be characterized by the three transition parameters. We compute the probability distribution of a zero transcript event and its conjugate, the distribution of the time durations in gene on or gene off periods, the transition frequency between system states, and the transcriptional bursting frequency. We also exemplify the mathematical results by the experimental data on prokaryotic and eukaryotic transcription. The analysis reveals that no promoters will be definitely turned on to transcribe within a finite time period, no matter how strong the induction signals are applied, and how abundant the activators are available. Although stronger extrinsic signals could enhance promoter activation rate, the promoter creates an intrinsic ceiling that no signals could cross over in a finite time. Consequently, among a large population of isogenic cells, only a portion of the cells, but not the whole population, could be induced by environmental signals to express a particular gene within a finite time period. We prove that the gene on duration follows an exponential distribution, and the gene off intervals show a local maximum that is best described by assuming two sequential exponential process. The transition frequencies are determined by a system of stochastic differential equations, or equivalently, an iterative scheme of integral operators. We prove that for each positive integer n , there associates a unique time, called the peak instant, at which the nth transcript synthesis cycle since time zero proceeds most likely. These moments constitute a time series preserving the nature order of n.

In this thesis, we consider a class of stochastic dynamics running backwards, so called backward stochastic differential equations (BSDEs) in the literature. We demonstrate BSDEs can be reformulated as functional differential equations defined on path spaces, and therefore solving BSDEs is equivalent to solving the associated functional differential equations. With such observation we can solve BSDEs on general filtered probability space satisfying the usual conditions, and in particular without the requirement of the martingale representation. We further solve the above functional differential equations numerically, and propose a numerical scheme based on the time discretization and the Picard iteration. This in turn also helps us solve the associated BSDEs numerically. In the second part of the thesis, we consider a class of BSDEs with quadratic growth (QBSDEs). By using the functional differential equation approach introduced in this thesis and the idea of the Cole-Hopf transformation, we first solve the scalar case of such QBSDEs on general filtered probability space satisfying the usual conditions. For a special class of QBSDE systems (not necessarily scalar) in Brownian setting, we do not use such Cole-Hopf transformation at all, and instead introduce the weak solution method, which is to use the strong solutions of forward backward stochastic differential equations (FBSDEs) to construct the weak solutions of such QBSDE systems. Finally we apply the weak solution method to a specific financial problem in the credit risk setting, where we modify the Merton's structural model for credit risk by using the idea of indifference pricing. The valuation and the hedging strategy are characterized by a class of QBSDEs, which we solve by the weak solution method.

<p>How organisms adapt and survive in continuously fluctuating environments is a central question of evolutionary biology. Additionally, organisms have to deal with the inherent stochasticity in all cellular processes. The purpose of this thesis is to gain insights into how organisms can use epigenetics and the stochasticity of gene expression to deal with a fluctuating environment. To accomplish this, two cases at different temporal and structural scales were explored: (1) the early transcriptional response to an environmental stimulus in single cells, and (2) the evolutionary dynamics of a population adapting to a recurring fluctuating environment. Mathematical models of stochastic gene expression, population dynamics, and evolution were developed to explore these systems.</p><p>First, the information available in sparse single cell measurements was analyzed to better characterize the intrinsic stochasticity of gene expression regulation. A mathematical and statistical model was developed to characterize the kinetics of a single cell, single gene behavior in response to a single environmental stimulus. Bayesian inference approach was used to deduce the contribution of multiple gene promoter states on the experimentally measured cell-to-cell variability. The developed algorithm robustly estimated the kinetic parameters describing the early gene expression dynamics in response a stimulus in single neurons, even when the experimental samples were small and sparse. Additionally, this algorithm allowed testing and comparing different biological hypotheses, and can potentially be applied to a variety of systems.</p><p>Second, the evolutionary adaptation dynamics of epigenetic switches in a recurrent fluctuating environment were studied by observing the evolution of gene regulatory circuit in a population under multiple environmental cycles. The evolutionary advantage of using epigenetics to exploit the natural noise in gene expression was tested by competing this strategy against the classical genetic adaptation through mutations in a variety of evolutionary conditions. A trade-off between minimizing the adaptation time after each environmental transition and increasing the robustness of the phenotype during the constant environment between transitions was observed. Surviving lineages evolved bistable, epigenetic switching to adapt quickly in fast fluctuating environments, whereas genetic adaptation with high robustness was favored in slowly fluctuating environments.</p> / Dissertation

Neste trabalho, analisamos três modelos definidos sobre redes e governados por dinÂmicas estocásticas. Nosso principal interesse repousa no estudo das transições de fase e no comportamento crítico desses modelos. O primeiro deles é o autômato celular pobabilístico da Domany-Kinzel, ao qual aplicamos o método de expansões em série. Em seguida, estudamos o comportamento para tempos longos de alguns processos de reação-difusão por meio de simulação numérica. Tais processos podem ser relevantes para o entendimento da compactação em sistemas granulares. Finalmente, também através de dimulações numéricas, analisamos o processo de contato conservativo, que é uma versão do modelo original definida em um ensemble onde o número de partículas é conservado. / In this work, we analyzed three lattice models governed by stochastic dynamics. Our main interest lies on the study of the phase transitions and critical behavior of these models. The first of them is the Domany-Kinzel probabilistic cellular automaton, to which we applied the method of series expansions. Next, we studied the long time behavior of some reaction-diffusion processes by means of numerical simulations. Such processes may be relevant to the understanding of granular compaction. Finally, also by means of numerical simulations, we have analyzed the conserved contact process, which is a version of the original model defined on an ensemble where the number of particles is conserved.

Neste trabalho, analisamos três modelos definidos sobre redes e governados por dinÂmicas estocásticas. Nosso principal interesse repousa no estudo das transições de fase e no comportamento crítico desses modelos. O primeiro deles é o autômato celular pobabilístico da Domany-Kinzel, ao qual aplicamos o método de expansões em série. Em seguida, estudamos o comportamento para tempos longos de alguns processos de reação-difusão por meio de simulação numérica. Tais processos podem ser relevantes para o entendimento da compactação em sistemas granulares. Finalmente, também através de dimulações numéricas, analisamos o processo de contato conservativo, que é uma versão do modelo original definida em um ensemble onde o número de partículas é conservado. / In this work, we analyzed three lattice models governed by stochastic dynamics. Our main interest lies on the study of the phase transitions and critical behavior of these models. The first of them is the Domany-Kinzel probabilistic cellular automaton, to which we applied the method of series expansions. Next, we studied the long time behavior of some reaction-diffusion processes by means of numerical simulations. Such processes may be relevant to the understanding of granular compaction. Finally, also by means of numerical simulations, we have analyzed the conserved contact process, which is a version of the original model defined on an ensemble where the number of particles is conserved.

Biological ion channels are responsible for, and regulate the communication system in the body. In this thesis I develop, test and apply theoretical models of ion channels, that can relate their structure to their functional properties. Brownian dynamics simulations are introduced, in which the motions of individual ions are simulated as they move through the channel and in baths attached to each end. The techniques for setting boundary conditions which maintain ion concentrations in the baths and provide a driving potential are tested. Provided the bath size is large enough, all boundary conditions studied yield the same results. ¶ Continuum theories of electrolytes have previously been used to study ion permeation. However, I show that these continuum models do not accurately reproduce the physics taking place inside ion channels by directly comparing the results of both equilibrium Poisson-Boltzmann theory, and non-equilibrium Poisson-Nernst-Planck theory to simulations. In both cases spurious shielding effects are found to cancel out forces that play an important role in ion permeation. In particular, the `reaction field' created by the ion entering the narrow channel is underestimated. Attempts to correct these problems by adding extra force terms to account for this reaction field also fail. ¶ A model of the L-type calcium channel is presented and studied using Brownian dynamics simulations and electrostatic calculations. The mechanisms of permeation and selectivity are explained as the result of simple electrostatic interactions between ions and the fixed charges in the protein. The complex conductance properties of the channel, including the current-voltage and current-concentration relationships, the anomalous mole fraction behaviour between sodium and calcium ions, the attenuation of calcium currents by monovalent ions and the effects of mutating glutamate residues, are all reproduced. ¶ Finally, the simulation and electrostatic calculation methods are used to study the gramicidin A channel. It is found that the continuum electrostatic calculations break down in this narrow channel, as the concept of applying a uniform dielectric constant is not accurate in this situation. Thus, the permeation properties of the channel are examined using Brownian dynamics simulations without electrostatic calculations. Future applications and improvements of the Brownian dynamics simulation technique are also described.

A stochastic differential equation with vanishing martingale term is studied. Specifically, given a domain D, the asymptotic scaling properties of both the exit time from the domain and the exit distribution are considered under the additional (non-standard) hypothesis that the initial condition also has a scaling limit. Methods from dynamical systems are applied to get more complete estimates than the ones obtained by the probabilistic large deviation theory. Two situations are completely analyzed. When there is a unique critical saddle point of the deterministic system (the system without random effects), and when the unperturbed system escapes the domain D in finite time. Applications to these results are in order. In particular, the study of 2-dimensional heteroclinic networks is closed with these results and shows the existence of possible asymmetries. Also, 1-dimensional diffusions conditioned to rare events are further studied using these results as building blocks. The approach tries to mimic the well known linear situation. The original equation is smoothly transformed into a very specific non-linear equation that is treated as a singular perturbation of the original equation. The transformation provides a classification to all 2-dimensional systems with initial conditions close to a saddle point of the flow generated by the drift vector field. The proof then proceeds by estimates that propagate the small noise nature of the system through the non-linearity. Some proofs are based on geometrical arguments and stochastic pathwise expansions in noise intensity series.