Integrated Knowledge Bases for
Complex Biological Systems
The need to integrate complex data from diverse sources can be addressed through advances in network theory, computation and ontologies. Integrating genome sequence, expression and polymorphism data, and clinical phenotypes, we are designing and building software that lets researchers explore, extend and analyze complex interacting networks of biological information.
Knowledge bases for candidate pathways involved in innate immunity for the Glue Grant project, and for those implicated in pain and analgesia, are supported with tools to interrogate, analyze and curate data. These leverage the BioLingua environment developed by Prof Shrager (Stanford Symbolic Systems Program), which provides a powerful bioinformatics interface to databases at the SGTC, data from external reference sources, and results from experimental assays and clinical collaborations. This serves as a platform on which to build statistical tools for large-scale analysis of these complex networks.
Personnel
Lisa Diamond
Jochen Kumm
Jeff Shrager
J.P. Massar
Wenzhong Xiao
Michael Mindrinos
Impact/Significance
Integrate clinical & genomic understanding in a common framework
Understand complex gene interactions
Identify significant pathways and interactions
Identify drug discovery targets
Accomplishments
Integrated Java, Python, Biolingua, MySQL underlying infrastructure
Core gene lists, genomics annotations, novel SNPs and orthologs are available through the knowledge base interface
Established collaborations with clinical research teams investigating innate immunity response and pain and analgesia
genome Technology Center 
The need to integrate complex data from diverse sources can be addressed through advances in network theory, computation and ontologies. Integrating genome sequence, expression and polymorphism data, and clinical phenotypes, we are designing and building software that lets researchers explore, extend and analyze complex interacting networks of biological information. 