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.