Motivation: Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages.
Results: We present TimePath, a new method that integrates time series and static datasets to reconstruct dynamic models of host response to stimulus. TimePath uses an Integer Programming formulation to select a subset of pathways that, together, explain the observed dynamic responses. Applying TimePath to study human response to HIV-1 led to accurate reconstruction of several known regulatory and signaling pathways and to novel mechanistic insights. We experimentally validated several of TimePaths predictions highlighting the usefulness of temporal models.
Presented in Partial Fulfillment of the CSD Speaking Skills Requirement