Networks and Context
The fragments which make up a network often come from different
biological contexts—here context is an umbrella term that implies
different models, diseases, conditions, observation modalities and
perturbations. For example, a group of researchers elucidates the
phosphorylation event that drives a signaling cascade using an array of
molecular techniques. Another research group identifies an inhibitor of
this phosphorylation event. Another identifies a handful of
transcription factors which assemble to produce this inhibitor, and so
on until a pathway model starts to take shape. An important
consideration in the implementation of this pathway is the context from
which each of its components arose. If each of these groups were working
with cell lines derived from different tissues, treated with different
perturbing agents, or grown under different environmental conditions -
could their results be stitched together into a common network? How to
assemble these fragments properly, and when and which type of context
restrictions should be used for a particular problem, are complicated
questions with no clearcut answer. It is often necessary to join
elements from different contexts to create networks that appropriately
match the scope of the high throughput data. For datasets with a narrow
scope within well-studied processes, such as a targeted metabolomics
assay quantifying components of glucose metabolism, it may be possible
to find a manually assembled quantitative model which combines fragments
from a consistent context. However, for most -omics applications, which
often involve untargeted high-throughput datasets, we often need to use
the context-insensitive network and derive the context from the data.