Computational Models of Genome Organization and Transcription

We integrate our experimental results with mechanistic computational models through an iterative "watch, perturb and learn" approach. After all, a mechanistic model is just a mathematical description of our assumptions. Our vision is to take an iterative approach: we apply our imaging technologies for precision measurements of dynamics and mechanisms. We use the measurements to develop and parameterize mechanistic models. To test our understanding, we make model predictions, which we go back and test. Although many model predictions may fail, the manner in which they fail, tells us what which of our mechanistic assumptions were wrong. Thus, by iteratively going through this combined experimental and computional approach, our aim is to eventually derive mechanistically accurate and quantitatively predictive models of genome folding and gene expression.