Information Super-Diffusion on Structured Networks

We study diffusion of information packets on several classes of structured networks. Packets diffuse from a randomly chosen node to a specified destination in the network. As local transport rules we consider random diffusion and an improved local search method. Numerical simulations are performed in the regime of stationary workloads away from the jamming transition. We find that graph topology determines the properties of diffusion in a universal way, which is reflected by power-laws in the transit-time and velocity distributions of packets. With the use of multifractal scaling analysis and arguments of non-extensive statistics we find that these power-laws are compatible with super-diffusive traffic for random diffusion and for improved local search. We are able to quantify the role of network topology on overall transport efficiency. Further, we demonstrate the implications of improved transport rules and discuss the importance of matching (global) topology with (local) transport rules for the optimal function of networks. The presented model should be applicable to a wide range of phenomena ranging from Internet traffic to protein transport along the cytoskeleton in biological cells.Comment: 27 pages 7 figure

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

In cryo-electron microscopy (EM), molecular structures are determined from large numbers of projection images of individual particles. To harness the full power of this single-molecule information, we use the Bayesian inference of EM (BioEM) formalism. By ranking structural models using posterior probabilities calculated for individual images, BioEM in principle addresses the challenge of working with highly dynamic or heterogeneous systems not easily handled in traditional EM reconstruction. However, the calculation of these posteriors for large numbers of particles and models is computationally demanding. Here we present highly parallelized, GPU-accelerated computer software that performs this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI parallelization combined with both CPU and GPU computing. The resulting BioEM software scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time. The general mathematical framework and robust algorithms are not limited to cryo-electron microscopy but can be generalized for electron tomography and other imaging experiments

Reconstruction of an in silico metabolic model of _Arabidopsis thaliana_ through database integration

The number of genome-scale metabolic models has been rising quickly in recent years, and the scope of their utilization encompasses a broad range of applications from metabolic engineering to biological discovery. However the reconstruction of such models remains an arduous process requiring a high level of human intervention. Their utilization is further hampered by the absence of standardized data and annotation formats and the lack of recognized quality and validation standards.

Plants provide a particularly rich range of perspectives for applications of metabolic modeling. We here report the first effort to the reconstruction of a genome-scale model of the metabolic network of the plant _Arabidopsis thaliana_, including over 2300 reactions and compounds. Our reconstruction was performed using a semi-automatic methodology based on the integration of two public genome-wide databases, significantly accelerating the process. Database entries were compared and integrated with each other, allowing us to resolve discrepancies and enhance the quality of the reconstruction. This process lead to the construction of three models based on different quality and validation standards, providing users with the possibility to choose the standard that is most appropriate for a given application. First, a _core metabolic model_ containing only consistent data provides a high quality model that was shown to be stoichiometrically consistent. Second, an _intermediate metabolic model_ attempts to fill gaps and provides better continuity. Third, a _complete metabolic model_ contains the full set of known metabolic reactions and compounds in _Arabidopsis thaliana_.

We provide an annotated SBML file of our core model to enable the maximum level of compatibility with existing tools and databases. We eventually discuss a series of principles to raise awareness of the need to develop coordinated efforts and common standards for the reconstruction of genome-scale metabolic models, with the aim of enabling their widespread diffusion, frequent update, maximum compatibility and convenience of use by the wider research community and industry

Stochastic Dynamic Programming and Stochastic Fluid-Flow Models in the Design and Analysis of Web-Server Farms

A Web-server farm is a specialized facility designed specifically for housing Web servers catering to one or more Internet facing Web sites. In this dissertation, stochastic dynamic programming technique is used to obtain the optimal admission control policy with different classes of customers, and stochastic uid- ow models are used to compute the performance measures in the network. The two types of network traffic considered in this research are streaming (guaranteed bandwidth per connection) and elastic (shares available bandwidth equally among connections). We first obtain the optimal admission control policy using stochastic dynamic programming, in which, based on the number of requests of each type being served, a decision is made whether to allow or deny service to an incoming request. In this subproblem, we consider a xed bandwidth capacity server, which allocates the requested bandwidth to the streaming requests and divides all of the remaining bandwidth equally among all of the elastic requests. The performance metric of interest in this case will be the blocking probability of streaming traffic, which will be computed in order to be able to provide Quality of Service (QoS) guarantees. Next, we obtain bounds on the expected waiting time in the system for elastic requests that enter the system. This will be done at the server level in such a way that the total available bandwidth for the requests is constant. Trace data will be converted to an ON-OFF source and fluid- flow models will be used for this analysis. The results are compared with both the mean waiting time obtained by simulating real data, and the expected waiting time obtained using traditional queueing models. Finally, we consider the network of servers and routers within the Web farm where data from servers flows and merges before getting transmitted to the requesting users via the Internet. We compute the waiting time of the elastic requests at intermediate and edge nodes by obtaining the distribution of the out ow of the upstream node. This out ow distribution is obtained by using a methodology based on minimizing the deviations from the constituent in flows. This analysis also helps us to compute waiting times at different bandwidth capacities, and hence obtain a suitable bandwidth to promise or satisfy the QoS guarantees. This research helps in obtaining performance measures for different traffic classes at a Web-server farm so as to be able to promise or provide QoS guarantees; while at the same time helping in utilizing the resources of the server farms efficiently, thereby reducing the operational costs and increasing energy savings