Many cancer-associated genes and pathways remain to be identified in order to clarify the
molecular mechanisms underlying cancer progression. In this area, genome-wide
loss-of-function screens appear to be powerful biological tools, allowing the accumulation
of large amounts of data. However, this approach currently lacks analytical tools to
exploit the data with maximum efficiency, for which systems biology methods analyzing
complex cellular networks may be extremely helpful. In this article we report such a
systems biology strategy based on the construction of a Network for a biological process
and specific for a given cell system (cell type). The networks are created from
genome-wide loss-of-function screen datasets. We also propose tools to analyze network
properties. As one of the tools, we suggest a mathematical model for discrimination
between two distinct cell processes that may be affected by knocking down the activity of
a gene, i. e., a decreased cell number may be caused by arrested cell proliferation or
enhanced cell death. Next we show how this discrimination between the two cell processes
helps to construct two corresponding subnetworks. Finally, we demonstrate an application
of the proposed strategy to the identification and characterization of putative novel
genes and pathways significant for the control of lung cancer cell growth, based on the
results of a genome-wide proliferation/viability loss-of-function screen of human lung
adenocarcinoma cells.