Since the late 1990s, there has been an explosion in the development of technologies that measure cellular content on a genome scale. These methods generate large amounts of data, generally referred to as omic data; sometimes referred to as content. The quality and coverage of omic data sets has improved steadily with time. Individual data points in omic data sets are treated as independent variables. They can then be correlated statistically to find patterns in the presence of cellular components. Such statistical correlations do not confer causality. GEMs, given their mechanism-based construction, can be used as a context for the analysis of polyomic data sets [358]. Such contextualization can lead to the establishment of causation. Furthermore, GEMs can integrate mechanistically multiple omic data sets, thus aiding in the determination of how various components come together to produce cellular functions and phenotypic states. For some, this big data to knowledge conversion represents a grand challenge in biology. Here we will cover the basic principles of omic data analysis using GEMs. More detailed reviews have appeared describing these data-mapping methods and their use [41, 175, 209, 232, 253].

Context for Content

Over the past 10–15 years, many ingenious methods have been developed to profile the molecular content of cells and to determine the interactions between components. There are vast repositories of the resulting omic data available on various websites, and there are many sources that describe the omics data types, how they are generated, and their availability. We will not repeat these here, but instead turn our attention to the use of COBRA methods to analyze omics data sets.

Networks as a backdrop or context for omics data-mapping Two main network approaches are used to extract biological insights from omic data sets [348]: inference based and knowledge-based. Both approaches use an interconnected network of biological components to interpret omic data sets.