Dynamic states of ErbB1 phosphorylation predicted by spatial-stochastic modeling

Mon, 02 Dec 2013 19:00:00 -0500

The primary and secondary goals this work addresses are discovery and research infrastructure, respectively. This work tackles these goals in a very efficient, integrative manner. Establishing this stochastic simulation framework, then utilizing the simulations to investigate ErbB receptors allows for a versatile tool to be developed, validated, and applied. ErbB receptors are directly implicated in cancers due to their signaling pathways, which suppress apoptosis, among other cellular functions. We address this critical process, working to understand how the kinetics and dynamics of the ErbB receptors initiate these signaling pathways. Although we have chosen to investigate repeated interactions of ErbB receptors and the impact of membrane landscape here, the established model can be used continuously as a key research tool in the future. While developing this simulator, care was taken to create a user-friendly environment, allowing not only programmers but also experimentalists to be comfortable using the tool. Creating a user-friendly tool is essential to integrating the workspace between programmers and experimentalists.

One of the major extended goals of this work is to be able to suggest novel experimental setups and procedures directly from the predictions of the model. The current work highlights the models capability of investigating beyond the current experimental limits through the exploration of the asymmetric phosphorylation mechanism. This model can also be used as a tool on the experimental workbench by allowing experimentalists to quickly investigate new observations and conclusions from experiments. Predictions from the model will give the experimentalist an indication of the outcome of further research, without the extra time and money. Utilizing tools like this simulator will allow for faster, more efficient research and development to occur through an experimental-modeling handshake and development cycle. As new data is available, this simulator can be updated and validated, the new model predictions can then be used to suggest novel experiments. The cycle continues with the new data from the novel experiments being used to further validate/update the model.