Drug Response - A Tool for Understanding the Systems Biology of Type 2 Diabetes
Many recent advances in diabetes research have generated a body of knowledge indicating that diabetes is a heterogeneous disease with a complex genetic component. Environmental factors such as lifestyle and diet also play a major role in the development and progression of diabetes. Although diabetes is defined only by measuring glucose in the blood (or HbA1c as a surrogate), it is apparent that many factors can lead to hyperglycemia, and this single measurement does not accurately describe the cause(s) of the disease. In fact, several biological systems appear to be involved in the progression and development of type 2 diabetes, including several organs/tissues, hormones, as well as several intracellular molecular pathways. The limited understanding of the complexities of these systems and their interactions has been a major barrier in the development of optimal treatments in type 2 diabetes. A systems biology approach potentially could model these complex networks and thus help in characterizing key elements that affect the energy homeostasis. Within this new discipline, data sets obtained by the application of high-throughput technologies such as genomics, proteomics, and metabolomics are integrated to develop models that can be used to explain specific biological or physiological endpoints.
It may seem that at this stage of knowledge creating a model that can capture the complexity of the systems involved in the development of type 2 diabetes might be too difficult. As a result, within this workshop we would like to explore the possibility of focusing systems biology approaches on populations from clinical studies in which a specific agent (e.g., metaformin) is used as a single variable and the molecular profile, genes, and protein networks could be characterized before and after treatment.
More generally, within this workshop we intend to bring together experts in the areas of computational biology, clinical trials, proteomics, metabolomics, genomics, and physiology to identify how systems biology studies could be pursued in the context of diabetes.