Make your own network

For systems biologists desperate to get beyond transcriptional analysis directly into the world of signal transduction, hope comes from a Stanford/MIT collaboration. The team has successfully derived the structure of a signaling network in primary human cells from simultaneous measurements of multiple labeled proteins and lipids. The method detects a web of interactions, rather than the linear pathways considered in smaller scale experiments.

Karen Sachs and Douglas Lauffenburger, computational biologists at MIT (Cambridge, MA), wanted to use Bayesian networks to model biological systems. These graphical models have been used to predict transcriptional network structures based on mRNA levels.

The problem for those interested in signaling, says Sachs, was that the method is probabilistic, and thus requires many independent samples. This is what Omar Perez and Garry Nolan (Stanford University, Stanford, CA) could provide,...