SUMMARY: WEbcoli is a WEb application for in silico designing, analyzing and engineering Escherichia coli metabolism. It is devised and implemented using advanced web technologies, thereby leading to enhanced usability and dynamic web accessibility. As a main feature, the WEbcoli system provides a user-friendly rich web interface, allowing users to virtually design and synthesize mutant strains derived from the genome-scale wild-type E.coli model and to customize pathways of interest through a graph editor. In addition, constraints-based flux analysis can be conducted for quantifying metabolic fluxes and charactering the physiological and metabolic states under various genetic and/or environmental conditions. AVAILABILITY: WEbcoli is freely accessible at http://webcoli.org. CONTACT: [email protected].

BLAST is one of the most widely used tools in bio informatics to find biologically similar sequences from the genome databases for a given query sequence. Although elaborate dynamic programming and heuristic search in the BLAST rapidly identify significant similar sequences, performance is still an important issue as the size of genome databases and sequences to be compared increases. We propose HGBS (hardware-oriented grid BLAST system) that significantly improves the BLAST performance by applying the grid computing technique. HGBS utilizes a PC-based grid environment constructed by a specialized hardware chip, not a grid middleware such as the Globus toolkit, to provide enhanced performance, good extensibility, and lower implementation cost. Performance analysis shows that there is a significant performance advantage in HGBS; for huge number of BLAST operations in 89-prokaryote genomes comparison, HGBS connecting 27 PCs needs just a week to complete the comparisons, but a single server system takes about 33 weeks.

A searc h condition in object-oriented queries consists of nested predicates, eac h of which is a predicate on a path expression.In this paper, w e present a new selectivit y estimation technique for nested predicates.Selectivity of a nested predicate, neste d selectivity, is de ned as the ratio of the number of quali ed objects of the starting class in the path expression to the total number of objects of the class.The new technique takes into account the eects of direct representation of manyto-many relationships.Many-to-many relationships frequently occur in object-oriented databases, but have n o t been properly handled in con ven tional selectivit yestimation techniques.F or many-to-many relationships, we generalize the block-hit function originally proposed by B. Yao allowing the cases where one object belongs to more than one block.The most signi cant advantage of our technique is that the accuracy of the estimation is far enhanced with only a small additional overhead.We present an ecient method for obtaining the statistical information that is needed for our estimation technique.We analyze the accuracy of our estimation technique and compare the result with those of conven tional ones.The experimental result shows there is a signi cant d eviation in the estimation obtained by c o n ven tional ones, con rming the advan tage of our technique.

We present a tunable two-dimensional class hierarchy indexing technique (2D-CHI) for object-oriented databases. We use a two-dimensional file organization as the index structure. 2D-CHI deals with the problem of clustering objects in a two-dimensional domain space consisting of the key attribute domain and the class identifier domain. In conventional class indexing techniques using one-dimensional index structures such as the B/sup +/-tree, the clustering property is exclusively owned by one attribute. These indexing techniques do not efficiently handle the queries that address both the attribute keys and the class identifiers. 2D-CHI enhances query performance by adjusting the degree of clustering between the key value domain and the class identifier domain based on the precollected usage pattern. For performance evaluation, we first compare 2D-CHI with the conventional class indexing techniques using an analytic cost model based on the assumption of uniform object distribution, and then verify the cost model through experiments using the multilevel grid file as the two-dimensional index. We further perform experiments with nonuniform object distributions. Our experiments show that our proposed method does indeed build optimal class index structures regardless of query types and object distributions. We strongly believe that our paper significantly contributes to building a self-tunable database system by supporting automatically tunable index structure.

Although various types of path indexes-indexes on path expressions-have been proposed for efficient processing of object-oriented queries, conventional join algorithms do not effectively utilize them. We propose a new join algorithm called OID join algorithm that effectively utilizes (multiple) path indexes in object-oriented databases. When (multiple) path indexes are available for a query, the OID join algorithm may reduce the query evaluation cost significantly by taking full advantage of the path indexes. We present a cost analysis for the OID join algorithm and compare it with those of conventional ones.

Ray tracing requires many ray-object intersection tests. A way of reducing the number of ray-object intersection tests is to subdivide the space occupied by objects into many nonoverlapping subregions, called voxels, and to construct an octree for the subdivided space. We propose the Octree-R, an octree-variant data structure for efficient ray tracing. The algorithm for constructing the Octree-R first estimates the number of ray-object intersection tests. Then, it partitions the space along the plane that minimizes the estimated number of ray-object intersection tests. We present the results of experiments for verifying the effectiveness of the Octree-R. In the experiment, the Octree-R provides a 4% to 47% performance gain over the conventional octree. The result shows the more skewed the object distribution (as is typical for real data), the more performance gain the Octree-R achieves.