Paul Jeffreys

BioSimGrid: grid-enabled biomolecular simulation data storage and analysis

In computational biomolecular research, large amounts of simulation data are generated to capture the motion of proteins. These massive simulation data can be analysed in a number of ways to reveal the biochemical properties of the proteins. However, the legacy way of storing these data (usually in the laboratory where the simulations have been run) often hinders a wider sharing and easier cross-comparison of simulation results. The data is commonly encoded in a way specific to the simulation package that produced the data and can only be analysed with tools developed specifically for that simulation package. The BioSimGrid platform seeks to provide a solution to these challenges by exploiting the potential of the Grid in facilitating data sharing. By using BioSimGrid either in a scripting or web environment, users can deposit their data and reuse it for analysis. BioSimGrid tools manage the multiple storage locations transparently to the users and provide a set of retrieval and analysis tools for processing the data in a convenient and efficient manner. This paper details the usage and implementation of BioSimGrid using a combination of commercial databases, the Storage Resource Broker and Python scripts, gluing the building blocks together. It introduces a case study of how BioSimGrid can be used for better storage, retrieval and analysis of biomolecular simulation data.

Grid computing and biomolecular simulation

Biomolecular computer simulations are now widely used not only in an academic setting to understand the fundamental role of molecular dynamics on biological function, but also in the industrial context to assist in drug design. In this paper, two applications of Grid computing to this area will be outlined. The first, involving the coupling of distributed computing resources to dedicated Beowulf clusters, is targeted at simulating protein conformational change using the Replica Exchange methodology. In the second, the rationale and design of a database of biomolecular simulation trajectories is described. Both applications illustrate the increasingly important role modern computational methods are playing in the life sciences.

A Web/grid portal implementation of BioSimGrid: a biomolecular simulation database

The overall aim of the BioSimGrid project (www.biosimgrid.org) is to exploit the grid infrastructure to enable comparative analysis of the results of biomolecular simulations. In particular, we present the implementation of current BioSimGrid Web portal. The portal has a SOA (service oriented architecture) framework built on the layer of OGSA (open grid service architecture) and OGSA-DAI (open grid service architecture database access and integration) middleware. The PortalLib has been developed to allow RAD (rapid application development) of portal applications. The portal also integrates PKI (public key infrastructure) and supports two levels of distributed SSO (single sign on): grid certificate-based SSO for high security, and user/pass based SSO for maximal flexibility.