SANGAM: A System for Integrating Web Services to Investigate Stress-Circuitry-Gene Coupling


October 7, 2005


In 1993, NIH launched the Human Brain Project (HBP) to develop and support neuroinformatics as a new science to make experimental data pertaining to the brain publicly available on the Internet. The success of HBP is demonstrated by the Society of Neuroscience maintaining a directory of 83 databases and 48 knowledge bases developed and maintained by different academic, government, and commercial institutions. A challenge is how to integrate data from these diverse sources to answer a scientific enquiry. SANGAM focuses on this challenge from the perspective of Stress-Circuitry-Gene coupling. It strives to address the following scientific question: Does every type of stress stimulus recruit the same set of brain circuits and activate the same genes, or do such circuits and genes vary across different stressors? An answer to this question helps clinicians and drug manufacturers to develop better treatments and drugs for stress disorders. Currently, a prototype of SANGAM is operational and in-use by our neuroscientists. A key insight from developing SANGAM is a general framework for neuroscience information integration consisting of 3 components: Run-time integration (RTI), Plan Composition (PLC), and Schema and Data Mapper (SDM). We present an overview of these components along with performance results from both centralized and distribution (using WSE 2.0) implementation of RTI component.


Shahram Ghandeharizadeh

Shahram Ghandeharizadeh received his Ph.D. degree in Computer Science from the University of Wisconsin, Madison, in 1990. Since then, he has been on the faculty at the University of Southern California. Shahram is a recipient of the National Science Foundation Young Investigator’s award for his research on physical design of parallel database systems. His primary research interest is the field of neuroinformatics, emphasizing the use of Web Services to facilitate publication, use, and integration of autonomous data sources.