For decades, molecular biologists have been uncovering the mechanics of
biological systems. Efforts to bring their findings together have led to
the development of multiple databases and information systems that
capture and present pathway information in a computable network format.
Concurrently, the advent of modern omics technologies has empowered
researchers to systematically profile cellular processes across
different modalities. Numerous algorithms, methodologies, and tools have
been developed to use prior knowledge networks in the analysis of omics
datasets. Interestingly, it has been repeatedly demonstrated that the
source of prior knowledge can greatly impact the results of a given
analysis. For these methods to be successful it is paramount that their
selection of prior knowledge networks is amenable to the data type and
the computational task they aim to accomplish. Here we present a
five-level framework that broadly describes network models in terms of
their scope, level of detail, and ability to inform causal predictions.
To contextualize this framework, we review a handful of network-based
omics analysis methods at each level, while also describing the
computational tasks they aim to accomplish.