Accelerating the Smith-Waterman algorithm with interpair pruning and band optimization for the all-pairs comparison of base sequences.

Background: The Smith-Waterman algorithm is known to be a more sensitive approach than heuristic algorithms for local sequence alignment algorithms. Despite its sensitivity, a greater time complexity associated with the Smith-Waterman algorithm prevents its application to the all-pairs comparisons of base sequences, which aids in the construction of accurate phylogenetic trees. The aim of this study is to achieve greater acceleration using the Smith-Waterman algorithm (by realizing interpair block pruning and band optimization) compared with that achieved using a previous method that performs intrapair block pruning on graphics processing units (GPUs).

Efficient acceleration of mutual information computation for nonrigid registration using CUDA.

In this paper, we propose an efficient acceleration method for the nonrigid registration of multimodal images that uses a graphics processing unit. The key contribution of our method is efficient utilization of on-chip memory for both normalized mutual information (NMI) computation and hierarchical B-spline deformation, which compose a well-known registration algorithm. We implement this registration algorithm as a compute unified device architecture program with an efficient parallel scheme and several optimization techniques such as hierarchical data organization, data reuse, and multiresolution representation.

A versatile platform for multilevel modeling of physiological systems: template/instance framework for large-scale modeling and simulation.

Building multilevel models of physiological systems is a significant and effective method for integrating a huge amount of bio-physiological data and knowledge obtained by earlier experiments and simulations. Since such models tend to be large in size and complicated in structure, appropriate software frameworks for supporting modeling activities are required. A software platform, PhysioDesigner, has been developed, which supports the process of creating multilevel models.

A platform for in silico modeling of physiological systems III.

Physiome and systems biology have been recognized as emerging and important research areas that can integrate quantitatively growing knowledge about biological structure and physiological functions at multiple scales of time and space. For the integration, it is important to build physiologically plausible and sharable mathematical models that can be used for dynamic simulations of functions at multi-scale and multi-level. Here we describe new features of our open platform insilicoML (ISML) and insilicoIDE (ISIDE) that have been presented previously.

A platform for in silico modeling of physiological systems II. CellML compatibility and other extended capabilities.

The number of biological models published in peer reviewed journals and complexity of each of those models are rapidly increasing, making it difficult to reproduce simulation results of the published models and to reuse the models by third persons. This paper is a continuation of our previous report on a software platform development as a solution to such difficulties. We describe progresses of our development.

Specifications of insilicoML 1.0: a multilevel biophysical model description language.

An extensible markup language format, insilicoML (ISML), version 0.1, describing multi-level biophysical models has been developed and available in the public domain. ISML is fully compatible with CellML 1.

A parallel implementation of 2-D/3-D image registration for computer-assisted surgery.

Image registration is a technique usually used for aligning two different images taken at different times and/or from different viewing points. A key challenge for medical image registration is to minimise computation time with a small alignment error in order to realise computer-assisted surgery. In this paper, we present the design and implementation of a parallel two-dimensional/three-dimensional (2-D/3-D) image registration method for computer-assisted surgery.

High-performance computing service over the internet for intraoperative image processing.

This paper presents a framework for a cluster system that is suited for high-resolution image processing over the Internet during surgery. The system realizes high-performance computing (HPC) assisted surgery, which allows surgeons to utilize HPC resources remote from the operating room. One application available in the system is an intraoperative estimator for the range of motion (ROM) adjustment in total hip replacement (THR) surgery.