Simulating complex biological models consisting of multiple ordinary differential equations can aid in the prediction of the pharmacological/biological responses; however, they are often hampered by the availability of reliable kinetic parameters. In the present study, we aimed to discover the properties of behaviors without determining an optimal combination of kinetic parameter values (parameter set). The key idea was to collect as many parameter sets as possible. Given that many systems are biologically stable and resilient (BSR), we focused on the dynamics around the steady state and formulated objective functions for BSR by partial linear approximation of the focused region. Using the objective functions and modified global cluster Newton method, we developed an algorithm for a thorough exploration of the allowable parameter space for biological systems (TEAPS). We first applied TEAPS to the NF-κB signaling model. This system shows a damped oscillation after stimulation and seems to fit the BSR constraint. By applying TEAPS, we found several directions in parameter space which stringently determines the BSR property. In such directions, the experimentally fitted parameter values were included in the range of the obtained parameter sets. The arachidonic acid metabolic pathway model was used as a model related to pharmacological responses. The pharmacological effects of nonsteroidal anti-inflammatory drugs were simulated using the parameter sets obtained by TEAPS. The structural properties of the system were partly extracted by analyzing the distribution of the obtained parameter sets. In addition, the simulations showed inter-drug differences in prostacyclin to thromboxane A2 ratio such that aspirin treatment tends to increase the ratio, while rofecoxib treatment tends to decrease it. These trends are comparable to the clinical observations. These results on real biological models suggest that the parameter sets satisfying the BSR condition can help in finding biologically plausible parameter sets and understanding the properties of biological systems.

BACKGROUND: In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL). RESULTS: The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28/*28 which reduces the expression of UGT1A1 down to 30%. CONCLUSION: These results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for in silico prediction of drug interactions. The following future researches for the in silico prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.

The 11 papers in this special section focus on biomedical computations on the grid.

We have developed the RIKEN AJAX browser computing (RABC) system. This system enables the simple realization of large scale Global Computing systems, like the SETI@home project, that take advantage of unused computing resources. Its greatest advantage is that an agent program, which has been traditionally required, does not need to be installed. Volunteers can participate by simply browsing to a Web page provided by the RABC system. This system allows greater CPU cycle resources to be acquired than traditional methods. We have evaluated a performance difference between JavaScript and legacy computer languages.

Multiple sequence alignment problems in computational biology have been focused recently because of the rapid growth of sequence databases. By computing alignment, we can understand similarity among the sequences. In this paper, we describe a compact system with an FPGA board and a host computer for multiple sequence alignment based on Carrillo-Lipman method. In our system, two dimensional dynamic programming is repeatedly applied along other dimensions to realize multidimensional search with a simple and common architecture, and unnecessary parts of the search space for finding the optimal alignment are skipped using Carrillo-Lipman method to reduce the computation time.

A method is described for enumerating the frequencies of DNA subsequences on a system comprising a host computer and a field programmable gate array (FPGA) board with one FPGA. Frequencies of subsequences with lengths of up to K0+ K1+ K2(24 in the current implementation) are enumerated in three phases. In these three phases, subsequences with lengths of up to K0, K0+ K1, and K0+ K1+ K2, respectively, are enumerated; these three phases are executed simultaneously on a pipelined circuit, resulting in high performance. The enumeration of frequent subsequences in databases, which are becoming larger and larger, will enable subsequences that are unique and/or repeatedly used in many parts of the sequences to be found.

BACKGROUND: Spatio-temporal dynamics within cells can now be visualized at appropriate resolution, due to the advances in molecular imaging technologies. Even single-particle tracking (SPT) and single fluorophore video imaging (SFVI) are now being applied to observation of molecular-level dynamics. However, little is known concerning how molecular-level dynamics affect properties at the cellular level. RESULTS: We propose an algorithm designed for three-dimensional simulation of the reaction-diffusion dynamics of molecules, based on a particle model. Chemical reactions proceed through the interactions of particles in space, with activation energies determining the rates of these chemical reactions at each interaction. This energy-based model can include the cellular membrane, membranes of other organelles, and cytoskeleton. The simulation algorithm was tested for a reversible enzyme reaction model and its validity was confirmed. Snapshot images taken from simulated molecular interactions on the cell-surface revealed clustering domains (size approximately 0.2 microm) associated with rafts. Sample trajectories of raft constructs exhibited "hop diffusion". These domains corralled the diffusive motion of membrane proteins. CONCLUSION: These findings demonstrate that our approach is promising for modelling the localization properties of biological phenomena.

BACKGROUND: Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. RESULTS: This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. CONCLUSION: Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.

BACKGROUND: Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. RESULTS: We propose two prediction methods for selecting effective siRNA target sequences from many possible candidate sequences, one based on the supervised learning of a radial basis function (RBF) network and other based on decision tree learning. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The proposed methods were evaluated by applying them to recently reported effective and ineffective siRNA sequences for various genes (15 genes, 196 siRNA sequences). We also propose the combined prediction method of the RBF network and decision tree learning. As the average prediction probabilities of gene silencing for the effective and ineffective siRNA sequences of the reported genes by the proposed three methods were respectively 65% and 32%, 56.6% and 38.1%, and 68.5% and 28.1%, the methods imply high estimation accuracy for selecting candidate siRNA sequences. CONCLUSION: New prediction methods were presented for selecting effective siRNA sequences. As the proposed methods indicated high estimation accuracy for selecting candidate siRNA sequences, they would be useful for many other genes.

Alignment problems in computational biology have been focused recently because of the rapid growth of sequence databases. By computing alignment, we can understand similarity among the sequences. Many systems for alignment have been proposed to date, but most of them are designed for two-dimensional alignment (alignment between two sequences). In this paper, we describe a compact system with an off-the-shelf FPGA board and a host computer for more than three-dimensional alignment based on dynamic programming. In our approach, high performance is achieved (1) by configuring optimal circuit for each dimensional alignment, and (2) by two phase search in each dimension by reconfiguration. In order to realize multidimensional search with a common architecture, two-dimensional dynamic programming is repeated along other dimensions. With this approach, we can minimize the size of units for alignment and achieve high parallelism. Our system with one XC2V6000 enables about 300-fold speedup as compared with single Intel Pentium 4 2GHz processor for four-dimensional alignment, and 100-fold speedup for five-dimensional alignment.

MOTIVATION: To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation. RESULTS: We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data.

UNLABELLED: OBIYagns (yet another gene network simulator) is a biochemical system simulator that comprises a multiple-user Web-based graphical interface, an ordinary differential equation solver and a parameter estimators distributed over an open bioinformatics grid (OBIGrid). This grid-based biochemical simulation system can achieve high performance and provide a secure simulation environment for estimating kinetic parameters in an acceptable time period. OBIYagns can be applied to larger system biology-oriented simulation projects. AVAILABILITY: OBIYagns example models, methods and user guide are available at https://access.obigrid.org/yagns/ SUPPLEMENTARY INFORMATION: Please refer to Bioinformatics online.

MOTIVATION: Clustering sequences of a full-length cDNA library into alternative splice form candidates is a very important problem. RESULTS: We developed a new efficient algorithm to cluster sequences of a full-length cDNA library into alternative splice form candidates. Current clustering algorithms for cDNAs tend to produce too many clusters containing incorrect splice form candidates. Our algorithm is based on a spliced sequence alignment algorithm that considers splice sites. The spliced sequence alignment algorithm is a variant of an ordinary dynamic programming algorithm, which requires O(nm) time for checking a pair of sequences where n and m are the lengths of the two sequences. Since the time bound is too large to perform all-pair comparison for a large set of sequences, we developed new techniques to reduce the computation time without affecting the accuracy of the output clusters. Our algorithm was applied to 21 076 mouse cDNA sequences of the FANTOM 1.10 database to examine its performance and accuracy. In these experiments, we achieved about 2-12-fold speedup against a method using only a traditional hash-based technique. Moreover, without using any information of the mouse genome sequence data or any gene data in public databases, we succeeded in listing 87-89% of all the clusters that biologists have annotated manually. AVAILABILITY: We provide a web service for cDNA clustering located at https://access.obigrid.org/ibm/cluspa/, for which registration for the OBIGrid (http://www.obigrid.org) is required.

In this paper, we propose a new method for the inference of S-system models of large-scale genetic networks. This method employs a technique to decompose the genetic network inference problem into several subproblems, and then applies a genetic local search to each of the subproblems. A local search method utilizing the feature of the S-system model is used as one of the search operators in this genetic local search. Finally, the effectiveness of the proposed method is verified through a genetic network inference problem.

We are developing the 'Protein Explorer' system, a petaflops special-purpose computer system for molecular dynamics simulations. The Protein Explorer is a PC cluster equipped with special-purpose engines that calculate nonbonded interactions between atoms, which is the most time-consuming part of the simulations. A dedicated LSI 'MDGRAPE-3 chip' performs these force calculations at a speed of 165 gigaflops or higher. The system will have 6,144 MDGRAPE-3 chips to achieve a nominal peak performance of one petaflop. The system will be completed in 2006. In this paper, we describe the project plans and the architecture of the Protein Explorer.

In this paper, we propose a new genetic local search named GLSDC (a genetic local search with distance independent diversity control) by extending the basic idea of DIDC (a genetic algorithm with distance independent diversity control) to coarse grained parallelization. GLSDC employs a local search method as a search operator. GLSDC also uses genetic operators, i.e., a crossover operator and a generation alternation model. However, in GLSDC, the crossover operator is not used as a search operator, but is used only for converging the population. GLSDC has an ability to find multiple optima simultaneously by stacking good individuals that have been found by the local search. Finding multiple optima is often required when we try to solve real world problems. The effectiveness of the proposed method is verified through numerical experiments on several high dimensional benchmark problems.

UNLABELLED: KnowledgeEditor is a graphical workbench for biological experts to model biomolecular network graphs. The modeled network data are represented by SRML, and can be published via the internet with the help of plug-in module 'GSCope'. KnowledgeEditor helps us to model and analyze biological pathways based on microarray data. It is possible to analyze the drawn networks by simulating up-down regulatory cascade in molecular interactions. AVAILABILITY: KnowledgeEditor is available at http://gscope.gsc.riken.go.jp/.

UNLABELLED: A graphical tool to visualize highly complicated biomolecular network graphs is described. It helps us to understand the graphs from macroscopic and microscopic viewpoints by incorporating continuous transition from global to clipped hyperbolic projection. GSCope also helps us to find a molecule in the graphs by offering several searching functions. It is useful to publish biomolecular network graphs on the internet. AVAILABILITY: GSCope is available at http://gscope.gsc.riken.go.jp.

This paper describes the design philosophy for the grid system being developed by the Japan Committee on High-Performance Computing for Bioinformatics and Initiative for Parallel Bioinformatics (IPAB). The grid is an attractive solution to achieve a distributed bioinformatics environment with high performance parallel computers, large genomic databases, computation intensive applications such as homology search and molecular simulation. However, there is a lot of work to do in grid system design, especially in the wide area network environment. OBIGrid emphasizes the virtual organization aspect of the grid system and gives more priority to security and scalability than performance.

This paper describes a High-speed Prolog Machine (HIM) architecture and its hardware structure, which are developed as a product of Fifth Generation Computer System (FGCS} project in Japan.HIM realizes high performance and provides a practical programming environment.A major HIM feature is a large memory capacity and specialized hardware for unification and stack operations.HIM has a compiler-oriented architecture with high-level stack-control instructions.Furthermore, the HIM architecture provides side-effect operations, nonlocal-exit control and multiprocess support primitives, which are effective to develop system programs.The performance is estimated at 280 KLIPS (Kilo Logical Inferences Per Second) in executing a deterministic "concatenate" Prolog program.The HIM processor is implemented with high-speed CCML {Current Mode Logic) chips and with I00 nanosecond machine cycle.