Multi-Layer Network Modules to Identify Markers for Personalized Medication in Complex Diseases

Co-Principal Investigator:
Michael A. Langston, Department of Computer Science, University of Tennessee
Abstract:
The symptoms of complex diseases such as allergy, obesity and cancer depend on the products of multiple interacting genes. High-throughput techniques have in fact implicated hundreds of genes. There are also considerable individual variations. A clinical implication of this may be inadequate treatment response, which is increasingly recognized as a cause of increased suffering and cost. Ideally, physicians should be able to personalize medication based on a few diagnostic markers. Finding these markers is a formidable challenge. We hypothesize that translational clinical studies based on high-throughput genomics, advanced computing and systems biology may help identify markers for personalized medication in complex diseases. We organize disease-associated genes in networks that are analyzed in a top-down manner. First, modules of interacting genes with distinct biological functions are identified. Then the modules are dissected to find pathways and finally upstream genes with key regulatory functions. An important focus is to develop methods to form multi-layer modules that integrate information about disease-associated changes on the DNA, RNA and protein levels. Since these levels interact, studies of the different levels can be interactively used to cross-validate the modules. This involves both genetic and experimental studies, but the ultimate test of the modules will be whether they can be used for clinical predictions. For example, changes in RNA expression may be caused by a SNP in a regulatory region. If so, the corresponding protein is tried as a marker to personalize medication. We have chosen allergic rhinitis as a model of complex disease because it is common, well-defined and readily examined in clinical and experimental studies. Our methods may be generally applicable to complex diseases.
Participants:
The PI for this project is Mikael Benson at the University of Göteborg, Sweden. Other Co-PIs are
Lachlan Coin at the Imperial College of London, UK,
Eivind Hovig at the Rikshospitalet, Radiomhospitalet, Norway, and
Birthe Sönnichsen at Cenix BioScience GMBH, Dresden, Germany.