Regulatory interactions

Regulatory interactions are probed using a combinatorial technique that enumerates all the possible regulators of a given gene/protein from hight throughput expression profiles. The technique takes as input time series expression profiles under experimental perturbations (knock-out for instance), and outputs all the possible networks, where relationships between genes/proteins are represented by activation/inhibition rules. The activation/inhibition rules are implemented by Boolean functions, which allows one to study the steady state dynamics of the infered networks. The computational efficiency of the technique relies of the assumption that the number of regulator per gene/protein is bounded by some constant. Our combinatorial enumeration has been applied to infer gene regulatory networks for Yeast cell cycle, IL2 stimulated T cell regulatory response, and LPS stimulated macrophages. More information can be found in the following papers:
  1. Martin S., Zhang Z., Martino A., Faulon J.L. Boolean Dynamics of Genetic Regulatory Networks Inferred from Microarray Time Series Data, Bioinformatics, 23, 866-74, 2007 [PMID: 17267426] (link to journal)
  2. Faulon J.L.,, Zhang Z., Martino A., Timlin J.A., Haaland D.M.,, Martin S., Davidson G., May E., Slepoy A. Reverse Engineering Biological Networks: T-cell response to IL-2 stimulation. SANDIA Report 2005- 5238379, Sandia National Laboratories, Albuquerque, NM. (.pdf manuscript).
  3. Martin S, Davidson G, May E, Werner-Washburne M., Faulon J.L. Inferring Genetic Networks from Microarray Data. Proceedings IEEE CSB2004, 3, 566-569, 2004. (.pdf manuscript).
  4. Faulon J.L., Martin S., Carr RD. Dynamical Robustness in Gene Regulatory Networks. Proceedings IEEE CSB2004, 3, 626-627, 2004. (.pdf manuscript).>



Signal transduction

The NF-κB signaling network plays an important role in many different compartments of the immune system during immune activation. Using a computational model of the NF-κB signaling network involving two negative regulators, IκBα and A20, we performed sensitivity analyses with three different sampling methods and present a ranking of the kinetic rate variables by the strength of their influence on the NF-κB signaling response. We also present a classification of temporal response profiles of nuclear NF-κB concentration into six clusters, which can be regrouped to three biologically relevant clusters. More information can be found in the following paper:
  1. Joo J., Plimpton S., Martin S., Swiler L., Slepoy A., Faulon J.L., Sensitivity analysis of computational model of the NF-κB-IκB-A20 signal transduction network, Annals of NY Academy of Sciences, in press 2007 [PMID: 17934057] (link to journal)