Tobias Uhlig

Novel Functions of Prototype Foamy Virus Gag Glycine- Arginine-Rich Boxes in Reverse Transcription and Particle Morphogenesis

ABSTRACT Prototype foamy virus (PFV) Gag lacks the characteristic orthoretroviral Cys-His motifs that are essential for various steps of the orthoretroviral replication cycle, such as RNA packaging, reverse transcription, infectivity, integration, and viral assembly. Instead, it contains three glycine-arginine-rich boxes (GR boxes) in its C terminus that putatively represent a functional equivalent. We used a four-plasmid replication-deficient PFV vector system, with uncoupled RNA genome packaging and structural protein translation, to analyze the effects of deletion and various substitution mutations within each GR box on particle release, particle-associated protein composition, RNA packaging, DNA content, infectivity, particle morphology, and intracellular localization. The degree of viral particle release by all mutants was similar to that of the wild type. Only minimal effects on Pol encapsidation, exogenous reverse transcriptase (RT) activity, and genomic viral RNA packaging were observed. In contrast, particle-associated DNA content and infectivity were drastically reduced for all deletion mutants and were undetectable for all alanine substitution mutants. Furthermore, GR box I mutants had significant changes in particle morphology, and GR box II mutants lacked the typical nuclear localization pattern of PFV Gag. Finally, it could be shown that GR boxes I and III, but not GR box II, can functionally complement each other. It therefore appears that, similar to the orthoretroviral Cys-His motifs, the PFV Gag GR boxes are important for RNA encapsidation, genome reverse transcription, and virion infectivity as well as for particle morphogenesis.

Simulation and optimization of content delivery networks considering user profiles and preferences of internet service providers

A Content Delivery Network (CDN) is a dynamic and complex service system. It causes a huge amount of traffic on the network infrastructure of Internet Service Providers (ISPs). Oftentimes, CDN providers and ISPs struggle to find an efficient and appropriate way to cooperate for mutual benefits. This challenge is key to push the quality of service (QoS) for the end-user. We model, simulate, and optimize the behavior of a CDN to provide cooperative solutions and to improve the QoS. Therefor, we determine reasonable server locations, balance the amount of servers and improve the user assignments to the servers. These aspects influence run time effects like caching at the server, response time and network load at specific links. Especially, user request history and profiles are considered to improve the overall performance. Since we consider multiple objectives, we aim to provide a diverse set of pareto optimal solutions using simulation based optimization.

A novel approach to solve K-center problems with geographical placement

The facility location problem is a well-known challenge in logistics that is proven to be NP-hard. In this paper we specifically simulate the geographical placement of facilities to provide adequate service to customers. Determining reasonable center locations is an important challenge for a management since it directly effects future service costs. Generally, the objective is to place the central nodes such that all customers have convenient access to them. We analyze the problem and compare different placement strategies and evaluate the number of required centers. We use several existing approaches and propose a new heuristic for the problem. For our experiments we consider various scenarios and employ simulation to evaluate the performance of the optimization algorithms. Our new optimization approach shows a significant improvement. The presented results are generally applicable to many domains, e.g., the placement of military bases, the planning of content delivery networks, or the placement of warehouses.

JARTA: a Java library to model and fit autoregressive-to-anything processes

JARTA is a Java library to model and fit Autoregressive-To-Anything (ARTA) processes. These processes are able to capture the dependency structure of a system, in contrast to commonly used models, that assume independently distributed random values. This study uses a simulation model of a warehouse to demonstrate the importance of capturing dependencies when modeling stochastic processes. Consequently there is a need for a suitable modeling approach. With JARTA we provide a modern software package to model processes with an appropriate dependency structure. Its two main goals are providing a clean code base for integration in other projects and high transparency for educational purposes. To support these goals JARTA is published under an open source license at http://sourceforge.net/projects/jarta/.

Evaluation of modeling tools for autocorrelated input processes

Queuing systems of any domain oftentimes exhibit correlated arrivals that considerably influence system behavior. Unfortunately, the vast majority of simulation modeling applications and programming languages do not provide the means to properly model the corresponding input processes. In order to obtain valid models, there is a substantial need for tools capable of modeling autocorrelated input processes. Accordingly, this paper provides a review of available tools to fit and model these processes. In addition to a brief theoretical discussion of the approaches, we provide tool evaluation from a practitioners perspective. The assessment of the tools is based on their ability to model input processes that are either fitted to a trace or defined explicitly by their characteristics, i.e., the marginal distribution and autocorrelation coefficients. In our experiments we found that tools relying on autoregressive models performed the best.

An efficient solution for K-center problems

The facility location problem is a well-known challenge in logistics that is proven to be NP-hard. In this paper we specifically simulate the geographical placement of facilities to provide adequate service to customers. We analyze the problem and compare different placement strategies and evaluate the number of required centers. We simulate several existing approaches and propose a new heuristic for the problem.

Reducing computation time in simulation-based optimization of manufacturing systems

The analysis of production systems using discrete, event-based simulation is wide spread and generally accepted as a decision support technology. It aims either at the comparison of competitive system designs or the identification of a best possible parameter configuration of a simulation model. Here, combinatorial techniques of simulation and optimization methods support the user in finding optimal solutions, but typically result in long computation times, which often prohibits a practical application in industry. To close this gap, this paper presents a fast converging procedure combining a Genetic Algorithm with a material flow simulation including an interactive analysis of simulation runs. An early termination of simulation runs is used for unpromising parameter configurations. The integrated implementation allows automated, distributed simulation runs for practical, complex production systems. A use-case shows the proof of concept with a reference model and demonstrates the resulting speed-up of this approach.

Simulation-based optimization for groups of cluster tools in semiconductor manufacturing using simulated annealing

Simulation-based optimization is an established approach to handle complex scheduling problems. The problem examined in this study is scheduling jobs for groups of cluster tools in semiconductor manufacturing including a combination of sequencing, partitioning, and grouping of jobs with additional constraints. We use a specialized fast simulator to evaluate the generated schedules which allows us to run a large number of optimization iterations. For optimization we propose a simulated annealing algorithm to generate the schedules. It is implemented as a special instance of our adaptable evolutionary algorithm framework. As a consequence it is easy to adapt and extend the algorithm. For example, we can make use of various already existing problem representations that are geared to excel at certain aspects of our problem. Furthermore, we are able to parallelize the algorithm by using a population of optimization runs.

Optimized travel to meetings on a common location of geographical distributed participants

Members of international organizations often meet in person at a common location for discussions. There is frequently disagreement over the place and time of the meeting due to the different travel efforts of the members. They usually travel by plane and their travel expenses depend on the flight connections. This paper presents an approach to calculate the optimized location and time, where and when distributed partners should meet. The presented system considers the requirements and specifications of each individual member. It respects earliest starting time of an event and non night flights. The optimized result is evaluated with regard to multiple objectives. We focus on the minimization of costs and travel time. Our search algorithm identifies individual travel data for all members for a potential event. The output provides recommendations for the global best appointments and offers further information for the partners. Our system saves expenses and time for all members and allows adjustment as well as compensation.

Evaluation of the general applicability of Dragoon for the k -center problem

The k-center problem is a fundamental problem we often face when considering complex service systems. Typical challenges include the placement of warehouses in logistics or positioning of servers for content delivery networks. We previously have proposed Dragoon as an effective algorithm to approach the k-center problem. This paper evaluates Dragoon with a focus on potential worst case behavior in comparison to other techniques. We use an evolutionary algorithm to generate instances of the k-center problem that are especially challenging for Dragoon. Ultimately, our experiments confirm the previous good results of Dragoon, however, we also can reliably find scenarios where it is clearly outperformed by other approaches.