Optimal Opinion Control: The Campaign Problem

Opinion dynamics is nowadays a very common field of research. In this article we formulate and then study a novel, namely strategic perspective on such dynamics: There are the usual normal agents that update their opinions, for instance according the well-known bounded confidence mechanism. But, additionally, there is at least one strategic agent. That agent uses opinions as freely selectable strategies to get control on the dynamics: The strategic agent of our benchmark problem tries, during a campaign of a certain length, to influence the ongoing dynamics among normal agents with strategically placed opinions (one per period) in such a way, that, by the end of the campaign, as much as possible normals end up with opinions in a certain interval of the opinion space. Structurally, such a problem is an optimal control problem. That type of problem is ubiquitous. Resorting to advanced and partly non-standard methods for computing optimal controls, we solve some instances of the campaign problem. But even for a very small number of normal agents, just one strategic agent, and a ten-period campaign length, the problem turns out to be extremely difficult. Explicitly we discuss moral and political concerns that immediately arise, if someone starts to analyze the possibilities of an optimal opinion control.Comment: 47 pages, 12 figures, and 11 table

Identifying Antimalarial Drug Targets by Cellular Network Analysis

Malaria is one of the most deadly parasitic infectious diseases and identifying novel drug targets is mandatory for the development of new drugs. To find drug targets, metabolic and signaling networks have been constructed. These networks have been investigated by graph theoretical methods. Furthermore, mechanistic models have been set up based on stoichiometric equations. At equilibrium, production and consumption of internal metabolites need to be balanced leading to a large set of flux equations, and this can be used for metabolic flux simulations to identify drug targets. Analysis of flux variability and knockout simulations were applied to detect potential drug targets whose absence reduces the predicted biomass production and hence viability of the parasite in the host cell. Furthermore, not only the parasite was studied, but also the interaction between the host and the parasite, and, based on experimental expression data, stage-specific metabolic models of the parasite were developed, particularly during the red-blood cell stage. In this chapter, these various network-based approaches for drug target prediction will be explained and summarized

Regulation patterns in signaling networks of cancer

<p>Abstract</p> <p>Background</p> <p>Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample.</p> <p>Results</p> <p>We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells.</p> <p>Conclusion</p> <p>Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed.</p

Identifying essential genes in bacterial metabolic networks with machine learning methods

<p>Abstract</p> <p>Background</p> <p>Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.</p> <p>Results</p> <p>We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for <it>Salmonella typhimurium</it>. We compared our predictions to the viability of experimental knock-outs of <it>S. typhimurium </it>and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.</p> <p>Conclusions</p> <p>Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.</p

Neue Beweglichkeit durch Technik?

Bereits seit Beginn der Forschung über Telearbeit wurde damit argumentiert, daß die Nutzung neuer Formen der Telekommunikation im Sinne eines Telekom-munikation-Transport-Tradeoffs auch das Bewegungsverhalten im Raum verän-dert. Welche Trends sind real zu erwarten

Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli

<p>Abstract</p> <p>Background</p> <p>Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.</p> <p>Results</p> <p>Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium <it>E. coli </it>to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of <it>E. coli </it>against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.</p> <p>Conclusion</p> <p>Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.</p

Neue Wege für die Einbindung des Schienengüterverkehrs in die Wertschöpfungsketten der Logistik: Neue Wege für die Einbindung des Schienengüterverkehrs in die Wertschöpfungsketten der Logistik

Der Schienengüterverkehr in Deutschland weist nach langer Stagnation erstmals wieder eine höhere Wachstumsrate als der Straßengüterverkehr auf. Die hierbei erreichten Positionen können jedoch langfristig nur ausgebaut werden, wenn es gelingt, die Zukunftsfähigkeit des Schienengüterverkehrs in den Lieferketten der Industrie und des Handels zu sichern. Der Überwindung existierender und neuer Engpässe bei der Nutzung der notwendigen Ressourcen, wie zum Beispiel der Gleisinfrastruktur, kommt dabei eine herausragende Bedeutung zu. Kurz- und mittelfristig gefragt sind daher Ansätze für eine effizientere Nutzung der vorhandenen Ressourcen. Im Beitrag werden dazu an den Schnittstellen zwischen Schienengüterverkehr und Logistik neue strategische und operative Ansätze für ein prozess- und strukturübergreifendes Ressourcenmanagement vorgestellt.For the first time after years of decline, rail freight transport in Germany is returning a higher growth rate than road transport. The success of recent years can only be perpetuated, however, if the growing demands of consignors can be fulfilled under conditions of increasing cost pressure. Meanwhile, the recent dynamic growth in transport volume is leading to first bottlenecks regarding the necessary resources for rail freight operation, e.g. railway infrastructure. New paths and solutions are thus required, with a strong focus on more efficient use of the existing resources. This paper presents new approaches to process and cross-company resource management, based on existing potentials at the interfaces between the operational concepts of freight railways, logistics and production

Disease-gene discovery by integration of 3D gene expression and transcription factor binding affinities

Abstract Motivation: The computational evaluation of candidate genes for hereditary disorders is a non-trivial task. Several excellent methods for disease-gene prediction have been developed in the past 2 decades, exploiting widely differing data sources to infer disease-relevant functional relationships between candidate genes and disorders. We have shown recently that spatially mapped, i.e. 3D, gene expression data from the mouse brain can be successfully used to prioritize candidate genes for human Mendelian disorders of the central nervous system. Results: We improved our previous work 2-fold: (i) we demonstrate that condition-independent transcription factor binding affinities of the candidate genes' promoters are relevant for disease-gene prediction and can be integrated with our previous approach to significantly enhance its predictive power; and (ii) we define a novel similarity measure—termed Relative Intensity Overlap—for both 3D gene expression patterns and binding affinity profiles that better exploits their disease-relevant information content. Finally, we present novel disease-gene predictions for eight loci associated with different syndromes of unknown molecular basis that are characterized by mental retardation. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Validity and responsiveness of the EQ-5D in assessing and valuing health status in patients with somatoform disorders

Background: The EQ-5D is a generic questionnaire providing a preference-based index score applicable to cost-utility analysis. This is the first study to validate the EQ-5D in patients with somatoform disorders. Methods: Data of the EQ-5D descriptive system, the British and the German EQ-5D index and the EQ Visual Analogue Scale, the Patient Health Questionnaire 15, the Patient Health Questionnaire 9, the Whiteley Index 7 and the Short Form 36 were collected from 294 patients at baseline, 244 at 6 months and 256 at 12 months after baseline. The discriminative ability of the EQ-5D was evaluated by comparison with a general population sample and by the ability to distinguish between different symptom severities. Convergent validity was analysed by assessing associations between the EQ-5D and the other instruments. Responsiveness was evaluated by analysing the effects on scores between two measurements in groups of patients reporting worse, same or better health. The Bonferroni correction was employed. Results: For all items of the EQ-5D except ‘self-care’, patients with somatoform disorders reported more problems than the general population. The EQ-5D showed discriminative ability in patients with different symptom severities. For nearly all reference instruments there were significant differences in mean scores between respondents with and without problems in the various EQ-5D items and strong correlations with the EQ Visual Analogue Scale and the EQ-5D index scores. Evidence for the responsiveness of the EQ-5D could only be found for patients with better health; effects were medium at the utmost. Conclusions: The EQ-5D showed a considerable validity and a limited responsiveness in patients with somatoform disorders. Trial registration: Current Controlled Trials ISRCTN5528079