The biological question I try to tackle is the analysis of the interplay among inflammatory, pro-survival and apoptotic signals (EGF, Interleukin, TNF, FasL, etc.) in both primary and cancerous human cells, to gain insight into how signaling proteins are modulated in time and space and how signals interact to determine cell fate in health and disease.
The starting point for my research is a large body of quantitative data recently collected by other members of the Sorger lab describing time-dependent signaling and phenotypic responses in hepatocytes, macrophages, and epithelial cells, treated with combinations of pro-apoptotic and pro-inflammatory cytokines.
We are now setting up and validating a 'rough', Boolean model of the connectivity among signaling proteins that transduce survival and apoptotic signals. Subsequently, we will refine the Boolean model into a kinetic model that captures time-dependent interactions among specific proteins based on mass action kinetics.
The computational model we plan to construct will be the largest of its kind, summarizing a large set of heterogenous information. It will have the potential to uncover principles not accessible via smaller models and more limited data sets. For example, how similar ligands lead to radically different cellular response despite engaged a common set of intracellular proteins and how the same cytokine can give rise to different responses in different cell types.
We found that it is not trivial to link in an efficient manner high-throughput data to mathematical models: the data has to be stored in a structure manner with additional information (metadata), processed (normalized, etc.), visualized and then exported for analysis. To facilitate these steps we have developed a matlab toolbox called DataRail.