Reconstructing the temporal progression of HIV-1 immune response pathways.
Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages.



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.



Data, Supplementary text and the TimePath software are available from http://sb.cs.cmu.edu/timepath



zivbj@cs.cmu.edu



Supplementary data are available at Bioinformatics online.
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Siddhartha
S
Jain
Department of Computer Science, University of Coimbra, Coimbra, Portugal.
Joel
J
Arrais
Department of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, USA.
Narasimhan J
NJ
Venkatachari
Department of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, USA.
Velpandi
V
Ayyavoo
Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Ziv
Z
Bar-Joseph
D018450:Disease Progression; D020869:Gene Expression Profiling; D015497:HIV-1; D006801:Humans; D008962:Models, Theoretical; D012984:Software