1. Bayesian Networks and Flow Cytometry Paul McDonagh Bioinformatics and Computational Biology, Amgen Seattle.

2. Multiparametric Flow-cytometry Up to 10 colors measured per cell Used by Karen Sachs and Gary Nolan to characterize signaling networks in T-cells Signaling networks were activated and concurrently perturbed using kinase inhibitors Thousands of cells measured per stimulation and perturbation Able to reconstruct the signaling network topology using the single- cell measurements and a computational technique called Bayesian Networks – Dana Pe’er Flow cytometry provides the first clear way to build Bayesian Networks from biological data.

3. Computational Challenges Effectively searching network space to find the most likely network for the data is difficult. –Software packages and distributed computing required Missing measurements of other molecules creates hidden variables that lead to confusing network topologies. –Need to find ways to extend analyses beyond 10 colors Many similar scoring networks have different topologies creating an ensemble of likely answers. –Ways to present the results of Bayesian Network reconstruction to scientists trying to interpret the data are required.

4. Systems Biology Companies Companies that specialize in these types of problems Gene Network Sciences, Ithaca NY Entelos (possibly) Microsoft Research, Redmond WA