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Mathematical models of signaling networks and their application to disease

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Cells are equipped with complex molecular networks that process external information, allowing cells to react to their environment. Deregulation of this signal transduction system is involved in the development of a variety of diseases such as diabetes and cancer. In my talk I will discuss how to study these molecular networks using a combination of high-throughput proteomics and mathematical modelling. As a case study, I will show an application to hepatocellular carcinoma, the most frequent form of liver cancer. Mathematical models specific for healthy liver cells and hepatocellular carcinoma cell lines were generated and compared, revealing three key differences in the wiring of their signalling pathways. These differences are potential targets for new therapies.

This talk is part of the Computational and Systems Biology series.

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