As our ability to gather data on biological systems advances rapidly, we contend with massive amounts of data and complex biological systems that are often too large and complex to analyze and manage without the use of modern Computational Bioscience and Information Technology (IT). Powerful computing and communication infrastructure as well as efficient algorithms are necessary to model, simulate, and study realistic biological systems and such large volumes of data.

Today's biomedical research requires a modern IT environment and sufficient computing resources for rapid data analysis and management, as well as dissemination of biomedical information. Modern Computational Bioscience and state-of-art IT resources will give our research organization a critical edge in the discovery of biomedical knowledge.

To accomplish these goals, we are devoting our IT and Computational Bioscience resources to the following strategic areas for unlocking and solving the mysteries contained within the human genome:

Computational Bioscience
A powerful scientific computing environment provides researchers ways to model more realistic biological systems with larger datasets, discover complex patterns hidden in large datasets, determine multivariate associations among data elements, share knowledge with other researchers, and disseminate information to the public.

High-performance Computing
In close collaboration with the Arizona State University, OCIO and the High-throughput Biocomputing Program (HBP) of CCB plans and provides high performance computing facilities with Linux super-clusters and SMP parallel computing machines, including high-density storage systems to accommodate large volumes of data. High-speed LANs and WAN connections are provided for rapid data communications. These technologies eliminate bottlenecks in the plumbing of the research system architecture.

OCIO guides HBP in (1) developing and applying high performance computational methods and algorithms to analyze and visualize large volumes of scientific data and to model and study complex biological system; (2) providing high performance scientific computing resources, expertise and assistance to the TGen scientific staff; and (3) collaborating with the TGen scientific staff and other research institutions in applying high performance computing to biomedical problems.

Knowledge-based Database
Knowledge-based databases under an integrated data warehousing environment and smart data mining tools are powerful resources in modern biomedical research. They provide researchers ways to extract and use information from vast amounts of knowledge and data collected from wide-ranging sources such as public and commercial databases (e.g., genome sequences, chromosome, SNP, genetic diseases, EST cluster, gene mapping, gene expression, proteomic, molecular biology, and clinical and literature databases). OCIO guides CCB in providing a scientific data warehousing environment that integrates and delivers variety of data types for both global and specialized local data views and analyses.

Web-based Central Computing
Web-based central client-server computing has been proven to be a powerful and cost effective way to deliver high-performance computing, knowledge-based databases, and application software tools for biomedical data analysis and visualization. Web-based central computing allows researchers to use powerful computational resources without spending time to learn underlying software and computer systems.

Bioinformatics Core (BIC) Support
The Goals of BIC are to (1) develop and provide generalized solutions to biomedical informatics and data management problems at TGen; (2) provide biomedical informatics expertise and assistance to the TGen scientific staff; (3) investigate and provide promising technologies; (4) lead biomedical informatics activities and set programming standards at TGen; and (5) engage in active collaborations with the TGen scientific staff and other research institutions.