The Use of Comprehensive Molecular Profiling with Network and Control Theory to Better Understand GWI and Model Therapeutic Strategies
Annual rept. 1 Jul 2011-30 Jun 2012
VETERANS ADMINISTRATION MEDICAL CENTER MIAMI FL
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The objective of this study is to improve our understanding of GWI pathogenesis in two ways through integration across several of the body s regulatory systems of data and knowledge collected from disparate sources, and by mapping of the coordinated interactions between these physiologic systems and the potential for altered wiring of these signaling networks in GWI. Using comprehensive molecular profiling, network and control theory the overarching objective of this proposal is to define the precise nature of these irregularities in immune and neuroendocrine signaling as well as the altered activation states of the corresponding cells such that treatment courses can be designed to redirect the system as a whole to normal pattern of coordinated activity. Recruitment has been ongoing since July 18, 2011, forty four patients have been consented to participate in the study twenty five of the subjects recruited are symptomatic with GWI and nineteen of the subjects are healthy controls. Moving beyond a conventional one piece at a time approach, we are using information and dynamic systems theory to identify altered structure and function in endocrine immune networks. Dr. Broderick, our co investigator s approach to analyzing the data involves looking not only at parts that might be defective but also how these parts are integrated and regulated in respect to one another. Our current work uses biomarkers in blood to take snapshots of connected networks regulating the body s immune, autonomic and endocrine function. To properly assess these networks, Dr. Broderick has employed statistical tools to quantify the degree of difference between networks in a healthy state versus an illness state. This is leading us to specific pathways that underlie pathogenic immune conversations and we are using our experience in computational biochemistry to conduct detailed investigations of limiting step reactions.
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