Formulation and use of GEMs as a four-step process. Step 1, the process is based on a variety of high-throughput data sets (i.e., omics data) and a comprehensive assessment of the literature (i.e., bibliomic data). Step 2, all of the data types are used to reconstruct the list of biochemical transformations that make up a network as well as their genetic basis¹. In principal, the network is unique. Step 3, the data contained in the reconstruction can be formally represented (i.e., in the form of matrices and logical statements) that can be mathematically characterized by a variety of methods. Step 4, the computational model enables a broad spectrum of applications, as reviewed in this article. Figure adapted from²

History of the E. coli metabolic reconstruction. Shown are six milestone efforts contributing to the reconstruction of the E. coli metabolic network. For each of the six reconstructions ^12-19, the number of included reactions (blue diamonds), genes (green triangles) and metabolites (purple squares) are displayed. Also listed are noteworthy properties that each successive reconstruction provided over previous efforts. For example, Varma & Palsson^{13, 14} included amino acid and nucleotide biosynthesis pathways in addition to the content that Majewski & Domach¹² characterized. The start of the genomic era⁹² (1997) marked a significant increase in included reconstruction components for each successive iteration. The reaction, gene and metabolite values for pre-genomic era reconstructions were estimated from the content outlined in each publication and in some cases, encoding genes for reactions were unclear.

Uses of the E. coli reconstructions divided into five categories. (A) A drawing of a predicted effect from a loss of function mutation in a simple system is shown. Metabolic engineering studies have investigated in silico strain design using E. coli metabolic reconstructions to overproduce desired products^20-30. (B) Recent studies utilizing the reconstruction in a prospective manner have aimed to use the current biochemical and genetic information included in the metabolic network along with additional data types to drive biological discovery, such as predicting genes encoding for orphan reactions^{32, 33, 35-37}. (C) Utilizing the reconstruction in phenotypic studies, computational analyses have examined gene^{19, 46, 51, 53, 63}, metabolite^{44, 60} and reaction^{39, 47, 48, 58} essentiality along with considering thermodynamics^{19, 40, 47, 49, 52, 54, 55, 57, 61} to make better predictions about the physiological state (i.e., the active pathways) of the cell for a given environmental condition. (D) The E. coli reconstructions have been used to analyze and interpret the intrinsic properties of biological networks. One example being finding coupled reaction activities⁶⁶ (as shown in the drawing) across different growth conditions. (E) Using the network reconstruction, evolutionary studies have examined the cellular network in the context of adaptive evolution events⁸¹, horizontal gene transfer^{80, 81} and minimal metabolic network evolution (as shown in the drawing)⁸².

This heatmap characterizes the incorporation of different computational methods into studies utilizing genome-scale models of E. coli. A dark box indicates that a particular method (one method per row) was utilized in a corresponding study (one citation per column); the frequency of usage of a particular method is given on the right. Studies were grouped into one of five general categories and studies examining phenotypic behavior were further divided into three subgroups. Studies that contributed new experimental growth data are also marked along the bottom offset row.

The comparison of GEM computation and organism-specific experimental measurements identifies agreements and disagreements. The phenotypic outcomes are tabulated for genetic perturbations examined in a given environment (e.g., growth or no growth). A ‘+’ indicates that a given phenotype is not affected by the perturbation, and ‘−’ indicates it does. Each outcome of comparison has a different implication; 1: consistency check - a perturbation has no affect on the property being measured and modeling predicts the same; 4: validation - the perturbation affects the experimental outcome and modeling with the GEM predicts this outcome; 2: identification of missing content - when GEM modeling fails to predict the positive confirmation of the property being measured, this outcome indicates that there is missing content in the GEM and can lead to the identification of specific areas for biological discovery; 3: identification of errors, inconsistencies or missing context-specific information – a positive prediction for the measured property and an opposite experimental observation indicates a possible error in the current organism-specific knowledge or that additional context-specific information is lacking from the GEM or modeling method (e.g., transcriptional regulation).