Analysis of Signaling Pathways in Human T-Cells using Bayesian Network Modeling of Single Cell Data

We perform network inference (‘reverse-engineering’) on phospho-specific multi-dimensional flow cytometry measurements of signaling molecules in human T cells using Bayesian networks. Inferred networks are found to have good agreement with known pathways derived from the literature. used to formulate testable influence hypotheses in a pathway of interest or to help elucidate pathways and points of cross-talk between pathways. We applied Bayesian networks, a probabilistic modeling tool, to flow cytometry measurements of signaling molecules in human T-cells under various stimulatory and inhibitory conditions. Recent advances in multi dimensional flow cytometry enable the measurement o