The protein identification that could be specific biomarkers to disease or physiological disorder is complex and request an integral use of various tools, approaches and informations. The recently developed proteomic technology features high-throughput parallel analysis of thousands of proteins in individual sampling or from population and thus opens up the possibility of providing more details at a global level on the molecular mechanisms. With regularly updated public databases, bioinformatics can contribute to these processes by providing functional information of target candidates and correlating this information to the biological pathways.
The goal is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to novel therapeutics. This deregulation in how cell process and react to extracellular information is a hallmark of multiple pathological conditions. Our main application is cancer, but we also work on metabolic and auto-immune diseases.
With these methods we hope to address questions such as:
1- What are the origins of the profound differences in signal transduction between healthy and diseased cells and in particular, in the context of cancer, between normal and transformed cells?
2- What are the differences in signal transduction among cancer types, and from patient to patient? Can we use these differences to predict disease progression?
3- Do these differences reveal valuable targets for drug development? Can we study the side effects of drugs using these models?