"Mathematical modelling of beta-catenin and ras si.. (CANCERSYS)
"Mathematical modelling of beta-catenin and ras signalling in liver and its impact on proliferation, tissue organization and formation of hepatocellular carcinomas"
Start date: Nov 1, 2008,
End date: Oct 31, 2011
"The goal of the CANCERSYS project is to establish a multi-scale model for two major signalling pathways involved in the formation of hepatocellular carcinoma, the beta-catenin and ras signalling pathways. Integrative studies linking measurements in primary heptocytes with effects at the organ level will address the impact of these signalling networks on proliferation, tissue organization and formation of hepatocellular carcinoma. In a close collaboration of scientists from theoretical fields and life sciences, our approach will combine dynamic modelling of signalling networks with spatial-temporal modelling of the liver microarchitecture. For this purpose dynamic models of the beta-catenin and ras core modules and their interactions will be integrated into a single-cell based three-dimensional model of the liver lobule. This model will be used to predict the impact of beta-catenin and ras activation on tissue organization starting with the early events, such as enhanced proliferation as well as micromotility of single cells, followed by formation of nodules and finally of dedifferentiated hepatocarcinomas. Predictions obtained by the model will be validated by inducible Apclox/lox and Ha-rasQ61R mouse strains, which have already been established by members of our consortium and allow induction of hepatocellular carcinoma. In addition the model predictions of the combined influence of active beta-catenin and ras signalling will be validated using a mouse strain that allows induction of both pathways in hepatocytes. In an iterative process, the model will be validated and adjusted to the in vivo situation. The model aims at the identification of systems properties of beta-catenin and ras signalling exploited during carcinogenesis and will foster the prediction of strategies for effective intervention, thereby facilitating the design of novel therapeutic strategies to combat hepatocellular carcinoma."
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