Connecting top-down and bottom-up models of systems biology

Two distinct approaches are being used to study complex cellular systems. The first, top-down approach automatically analyzes large-scale datasets for correlations between genes and proteins. However, it is often difficult to design experiments from these results. The second, bottom-up approach painstakingly crafts detailed models that can be simulated computationally. Although such simulations can suggest wet lab experiments, developing the models is a manual process that can take many years. These approaches have largely been developed separately until now. CTE faculty T. M. Murali and John Tyson are collaborating with Jean Peccoud (Virginia Bioinformatics Institute) to meld the strengths of these two approaches into a single framework, thereby allowing efficient and automated data-driven analysis to augment models that can be simulated. At present, we are using these developments to study the process of cell division in baker's yeast.