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Megan Riel-Mehan, Kevan Shokat (600 16th St San Francisco CA 94158)
Our knowledge of signaling networks has become more and more complex as we generate more and more large databases of genetic and protein interactions. Understanding the logic of networks is key to studying oncogenesis, drug resistance, finding new targets for cancer therapies and cell fate decisions in stem cell research. As important as these networks are, we we do not have very good means to visually make sense of them. In order for the data that is generated by systems biology and bioinformatics to be useful, scientists need to have tools that go beyond our traditional diagrams. Using Maya’s particle simulations I have recreated established transcription network motifs, and kinase signaling networks. The animations clearly show what the consequences of turning one node on and off are in a way that static representations do not. Additionally, they are easily to manipulate, so that new networks can be created quickly. My long term goal is to create an easy to use network simulation plugin that can help scientists better understand the networks they are studying, and better communicate those understandings to others. I believe insights often come only once one has an intu