Over the last years the number and quality of information and entertainment systems in automobiles has been rising constantly. This presents the challenge to provide safe and user-friendly interaction techniques, the implementation of which can lead to a higher level of efficiency, safety and user experience. One novel and promising approach is to use the drivers gaze as input for interaction with infotainment systems. We implemented a virtual car interior model to test the efficiency and user experience of gaze interaction with automotive infotainment systems. In a user study with 20 participants we compared a gaze-based interaction style to a haptic interaction technique. The usability of both techniques turned out to be very similar, while the user experience and the efficiency varied in parts. We used an eye-tracking device to investigate gaze behavior, but due to some technical problems with the device our quantitative findings are not as reliable and robust as we would have hoped for and have to be interpreted with care. Our qualitative data indicated a preference for gaze interaction.

In this chapter, we focus on Bayesian model selection for biological dynamical systems. We do not present an overview over existing methods, but showcase their comparison and the application to ordinary differential equation (ODE) systems, as well as the inference of the parameters in the ODE system. For this, our method of choice is the Bayes factor, computed by Thermodynamic Integration. We first present several model selection methods, both alternatives to the Bayes factor as well as several methods for calculating the Bayes factor, foremost among them said Thermodynamic Integration. As a simple example for the selection problem, we resort to a choice between normal distributions, which is analytically tractable. We apply our chosen method to a medium sized ODE model selection problem from radiation science and demonstrate how predictions can be drawn from the model selection results.

Statistical inference in high dimensional dynamical systems is often hindered by the unknown dependency structure of model parameters. In particular, the inference of parameterized differential equations (DEs) via Markov chain Monte Carlo (MCMC) samplers often suffers from high proposal rejection rates and is exacerbated by strong autocorrelation structures within the Markov chains leading to poor mixing properties. In this paper, we develop a novel vine-copula based adaptive MCMC approach for efficient parameter inference in dynamical systems with strong parameter interdependence. We exploit the concept of a vine-copula decomposition of distribution densities in order to generate problem-specific proposals for a hybrid independence/random walk Metropolis-Hastings (MH) sampler. The key advantage of this approach is that the corresponding MH proposals generate independent samples from the posterior distribution more efficiently than common competitors. All copula densities can be updated during the sampling procedure for fine-tuning. The performance of our method is assessed on two small-scale examples and finally evaluated on a delay DE model for the JAK2-STAT5 signaling pathway fitted to time-resolved western blot data. We compare our copula-based approach to an independence sampler, a second-order moment-based random walk MH algorithm, and an adaptive MH sampler.

BACKGROUND:In radiation protection, biokinetic models for zirconium processing are of crucial importance in dose estimation and further risk analysis for humans exposed to this radioactive substance. They provide limiting values of detrimental effects and build the basis for applications in internal dosimetry, the prediction for radioactive zirconium retention in various organs as well as retrospective dosimetry. Multi-compartmental models are the tool of choice for simulating the processing of zirconium. Although easily interpretable, determining the exact compartment structure and interaction mechanisms is generally daunting. In the context of observing the dynamics of multiple compartments, Bayesian methods provide efficient tools for model inference and selection.RESULTS:We are the first to apply a Markov chain Monte Carlo approach to compute Bayes factors for the evaluation of two competing models for zirconium processing in the human body after ingestion. Based on in vivo measurements of human plasma and urine levels we were able to show that a recently published model is superior to the standard model of the International Commission on Radiological Protection. The Bayes factors were estimated by means of the numerically stable thermodynamic integration in combination with a recently developed copula-based Metropolis-Hastings sampler.CONCLUSIONS:In contrast to the standard model the novel model predicts lower accretion of zirconium in bones. This results in lower levels of noxious doses for exposed individuals. Moreover, the Bayesian approach allows for retrospective dose assessment, including credible intervals for the initially ingested zirconium, in a significantly more reliable fashion than previously possible. All methods presented here are readily applicable to many modeling tasks in systems biology.

MicroRNAs are a large class of post-transcriptional regulators that bind to the 3' untranslated region of messenger RNAs. They play a critical role in many cellular processes and have been linked to the control of signal transduction pathways. Recent studies indicate that microRNAs can function as tumor suppressors or even as oncogenes when aberrantly expressed. For more general insights of disease-associated microRNAs, we analyzed their impact on human signaling pathways from two perspectives. On a global scale, we found a core set of signaling pathways with enriched tissue-specific microRNA targets across diseases. The function of these pathways reflects the affinity of microRNAs to regulate cellular processes associated with apoptosis, proliferation or development. Comparing cancer and non-cancer related microRNAs, we found no significant differences between both groups. To unveil the interaction and regulation of microRNAs on signaling pathways locally, we analyzed the cellular location and process type of disease-associated microRNA targets and proteins. While disease-associated proteins are highly enriched in extracellular components of the pathway, microRNA targets are preferentially located in the nucleus. Moreover, targets of disease-associated microRNAs preferentially exhibit an inhibitory effect within the pathways in contrast to disease proteins. Our analysis provides systematic insights into the interaction of disease-associated microRNAs and signaling pathways and uncovers differences in cellular locations and process types of microRNA targets and disease-associated proteins.

In recent years, microRNAs have been shown to play important roles in physiological as well as malignant processes. The PhenomiR database http://mips.helmholtz-muenchen.de/phenomir provides data from 542 studies that investigate deregulation of microRNA expression in diseases and biological processes as a systematic, manually curated resource. Using the PhenomiR dataset, we could demonstrate that, depending on disease type, independent information from cell culture studies contrasts with conclusions drawn from patient studies.

The analysis of complex networks is of major interest in various fields of science. In many applications we face the challenge that the exact topology of a network is unknown but we are instead given information about distances within this network. The theoretical approaches to this problem have so far been focusing on the reconstruction of graphs from shortest path distance matrices. Often, however, movements in networks do not follow shortest paths but occur in a random fashion. In these cases an appropriate distance measure can be defined as the mean length of a random walk between two nodes — a quantity known as the mean first hitting time. In this contribution we investigate whether a graph can be reconstructed from its mean first hitting time matrix and put forward an algorithm for solving this problem. A heuristic method to reduce the computational effort is described and analyzed. In the case of trees we can even give an algorithm for reconstructing graphs from incomplete random walk distance matrices.

Zusammenfassung. In der vorliegenden Arbeit werden subjektive Erfahrungen und objektive Reaktionsparameter von 8-Stunden- und 12-Stunden-Schichtarbeitern erfaßt. Die Annahme ist, daß die Schichtdauer einen wesentlichen Einfluß auf die psychische und physische Gesundheit und auf das Leistungsvermögen von Schichtarbeitern hat. Insgesamt 28 Schichtarbeiter im 8-Stunden- beziehungsweise 12-Stunden-Schichtdienst führten täglich über die Zeit eines Monats Tagebuch über ihr Befinden, die subjektive Kontrolle über die Situation, Müdigkeit, Handlungsfreiheit und subjektiv erlebte Fehleranfälligkeit. Weiterhin wurden Reaktionsfähigkeiten aller Teilnehmer mittels objektiver Reaktionstests erfaßt. Die Ergebnisse bestätigen, daß die Schichtdauer von großer Bedeutung für das psychische Erleben einer Person ist und zudem die Reaktionsfähigkeit beeinflußt. 12-Stunden-Schichtarbeiter zeigten in allen Parametern zumindest tendenziell bessere Werte als 8-Stunden-Schichtarbeiter. Vor allem die Müdigkeit erwies sich als bedeutsamer Faktor, der in Abhängigkeit von der Schichtdauer variiert.