Complex dynamical biomolecular systems govern virtually all biological processes on developmental and physiological time scales. A paramount problem is to understand how structural and dynamical properties of such systems affect their roles in cellular function and dysfunction. Our group has developed network inference approaches by integrating the information from multiple types of measurement data using a variety of modeling formalisms.  Such models can be used for developing optimal therapeutic strategies intended to control system behavior in disease.

Featured Projects

  • Probabilistic Boolean Networks

    Probabilistic Boolean Networks are a class of models of genetic regulatory networks.

  • Network Visualization

    Both of the network visualization tools below have been developed by our lab member Bill Longabaugh. Publications: Longabaugh, W.J.R. Combing the hairball with BioFabric: a new approach for visualization of large networks. BMC Bioinformatics, 13:275, 2012. Publications: Paquette, S.M., Leinonen, K., Longabaugh, W.J.R. BioTapestry now provides a web application and improved drawing and layout tools  F1000Research 5:39, 2016. Longabaugh, W.J.R. BioTapestry: A Tool to Visualize the Dynamic Properties of Gene Regulatory…