TimePath: Reconstructing temporal signaling pathways

Method overview Method overview

Most methods for reconstructing signaling and regulatory response networks from high throughput data generate static models which cannot distinguish between early and late stages in the response. Here we present TimePath, a new method that integrates time series and static datasets to reconstruct dynamic models of host immune response. TimePath works by selecting a subset of pathways that, together, explain the observed dynamic responses using an Integer Programming formulation. Applying TimePath to study human response to HIV-1 led to accurate reconstruction of several known signaling and regulatory pathways and to the identification of additional novel proteins. Each of these pathways and proteins were assigned by TimePath to a specific temporal phase in the response. We experimentally validated many of these assignments shedding new light on the function of specific proteins and demonstrating that treatments beyond the phase assigned by TimePath are much less effective in reducing viral loads.

The figure above shows the method overview (left) and temporal signaling network (right) that TimePath learns for the response of the cell to infection by the HIV-1 virus.