Ask not what you can do for your reconstruction, but what your reconstruction can do for you – with apologies to JFK

Part I described the network reconstruction process, Part II detailed its conversion into a mathematical format, and Part III discussed the characterization of network properties using constraint-based methods. What is all this effort good for? A well-curated genome-scale model, in principle, can be used to study all the phenotypic functions that can be produced from the genome of the target organism. Thus, the possible range of applications of GEMs is broad.

In June 2013, 645 papers had appeared that used COBRA tools to explain existing data or predict biological functions [52]. Analysis of these publications showed that the history of GEMs and their uses can be divided into three phases. Shortly after the appearance of the first GEMs in 1999 and 2000, there was a period of creativity around algorithms and analysis method development. In the mid-2000s, experimental validation studies began to accumulate resulting in the availability of well-curated and validated GEMs. Around 2010, a series of studies begun to appear that demonstrated that a variety of predictions of biological functions could be made using GEMs.

In this part of the book we describe the use of genome-scale networks. Chapters 22 and 23 describe how environmental and genetic parameters are represented and studied with genome-scale models. GEMs have proven to be remarkably useful for studying these two types of parameters. Media composition and growth requirements can be studied productively using GEMs. The prediction of the outcome of phenotypic screens of KO strain collections using GEMs with tens of thousands of outcomes represent perhaps the largest-scale effort for predicting biological functions.

Then we move onto specific application areas in a series of four chapters. First, we discuss how GEMs give a useful context for the analysis of omics data sets. Mapping such data against a known background proves to increase the resolution and use of the data.