A compact model of Escherichia coli core and biosynthetic metabolism
Fig 3
Layers of annotation and biological knowledge supporting the stoichiometric model in iCH360.
A: Annotations for the model reactions point to the BioCyc, MetaNetX, and KEGG databases. Bars show the number of annotations, highlighting the share of annotations that were added to or updated from the parent model iML1515. B: Some of the biological knowledge parsed from EcoCyc (and manually curated) included in the model-supporting functional annotation graph. The graph captures catalytic relationships between reactions and enzymes, protein subunit compositions, protein-gene mappings, and small-molecule regulation interactions, among others. Shown here as an example are the branches of the graph corresponding to the Glutamate Dehydrogenase (GLUDy) and Glutamate Synthase (GLUSy) reactions. C: Examples of catalytic relationships functionally annotated as either primary or secondary in the graph. Note that all catalytic relationships were classified as primary by default, unless sufficient evidence was found to annotate them as secondary. D: Functional annotation of catalytic edges as primary or secondary can be used to improve phenotypic predictions. Left: Classification of catalytic edge disruptions in the network resulting from simulated knockout of genes associated with essential reactions in the model across 9 growth conditions (see text for a description of each disruption class). Right: Comparison of predicted disruption outcomes against a large dataset of mutant fitness data [27] shows that the different types of disruption tend to lead to significantly different fitness changes. Whiskers in the box plot denote the range of data located 1.5 times above and below the interquartile range. Black dots represent data points lying outside this range.