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PREDICTIVE Computer modelling identifies which biological network elements should be tested in animal models. TRANSLATABLE The model can be applied to other cancers now that it has been proven in mammalian cells. TARGETED Minimises unnecessary costly and time-consuming in vivo testing. SWIFT Target identification times can be reduced by a factor of 4. When Cancer Cells Begin to Colonise In fact, few cancer sufferers will die as a result of their primary tumour. Metastasis (where the cancerous cells spread) is responsible for most deaths. It is also, unfortunately, the least understood process within cancer research. SBI researchers set out to create a model which they could use to better understand this process. A mathematical model was created that could both replicate the movement of cancerous cells, and simulate how to block these cells and thus prevent metastasis. This model was then calibrated using experimental data from proteomics experiments. The resultant model is both predictive and translatable. It allows cancer researchers to identify which network elements should be inhibited in animal model testing to further the research. This reduces the number of in vivo experiments necessary, telling us which measurements are informative or otherwise. This dramatically shortens the target identification process from years to a matter of months. Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland Tel: +353 1 716 6979 Email: [email protected] www.ucd.ie/sbi