  Partial measurements of biochemical reaction networks are ubiquitous and
limit our ability to reconstruct the topology of the reaction network and the
strength of the interactions amongst both the observed and the unobserved
molecular species. Here, we show how we can utilize noisy time series of such
partially observed networks to determine which species of the observed part
form its boundary, i.e. have significant interactions with the unobserved part.
This opens a route to reliable network reconstruction. The method exploits the
memory terms arising from projecting the dynamics of the entire network onto
the observed subnetwork. We apply it to the dynamics of the Epidermal Growth
Factor Receptor (EGFR) network and show that it works even for substantial
noise levels.
