|
Although pathways are often conceptualized as distinct entities responding to specific triggers, it is now appreciated that extensive inter-pathway crosstalk, feedback and other properties (e.g. developmental or metabolic state) of networks reflect underlying complexities that cannot be explained by consideration of individual pathways in isolation. Thus, gaining a global understanding of the network and its function requires integration of data from diverse high-throughput genomic and proteomic technologies into one holistic framework. Understanding "cellular computation" requires knowledge of the network structure and the influences among its components. In collaboration with Garry Nolan at Stanford University and Doug Lauffenburger at MIT we demonstrated the feasibility of directly deriving signaling networks from raw flow cytometry data using Bayesian networks . Our approach relied on using intracellular multicolor flow cytometry to quantitatively and simultaneously measure the abundance of phosphorylated proteins and phospholipid components in thousands of individual primary human immune cells. Perturbing these cells with molecular interventions drove the inference of influence and causality between them. Our method automatically discovered de novo, most traditionally established influences between the measured signaling components as well as discovering novel inter-pathway crosstalk: a causal connection of Erk1 on Akt that we confirmed experimentally. The network was correctly and rapidly reverse-engineered with no a priori knowledge of pathway connectivity. The application of Bayesian networks to single cell flow cytometry has distinct advantages, including an ability to measure events in primary cells after in vivo interventions (measuring context specific signaling biology in tissues), inference of causal influences and the ability to detect indirect as well as direct connections. Our methods success is due to two key factors:
We continue to use flow cytometry data as well as data from other proteomic and genomic technologies to address the central question: How does the cell integrate multiple exogenous and endogenous signals from different pathways to compute an appropriate response? We address this question both through experimental collaborations and using publicly available data.
|