  The sheer amounts of biological data that are generated in recent years have
driven the development of network analysis tools to facilitate the
interpretation and representation of these data. A fundamental challenge in
this domain is the reconstruction of a protein-protein subnetwork that
underlies a process of interest from a genome-wide screen of associated genes.
Despite intense work in this area, current algorithmic approaches are largely
limited to analyzing a single screen and are, thus, unable to account for
information on condition-specific genes, or reveal the dynamics (over time or
condition) of the process in question. Here we propose a novel formulation for
network reconstruction from multiple-condition data and devise an efficient
integer program solution for it. We apply our algorithm to analyze the response
to influenza infection in humans over time as well as to analyze a pair of ER
export related screens in humans. By comparing to an extant, single-condition
tool we demonstrate the power of our new approach in integrating data from
multiple conditions in a compact and coherent manner, capturing the dynamics of
the underlying processes.
