Genes required for an organism's growth and survival are termed essential and represent potential intervention targets. Following in the footsteps of the genomics era, the "next-gen" genomic era provides vast amounts of genetic information. Sequencing of a representative bacterial pathogen genome has been superseded by sequencing of whole strain collections, whether from environmental or clinical sources (Harris et al., Science 327:469-474, 2010; Lewis et al., J Hosp Infect 75:37-41, 2010; Beres et al., Proc Natl Acad Sci U S A 107:4371-4376, 2010; Qi et al., PLoS Pathog 5:e1000580, 2009; He et al., Proc Natl Acad Sci U S A 107:7527-7532, 2010; Barrick et al., Nature 461:1243-1247, 2009; Sheppard et al., Mol Ecol 22:1051-1064, 2013). However, the challenge of using this information to gain biological insight remains. Nonetheless, this information, in combination with experimental data from the literature, can serve as the framework for gaining a better understanding of an organism's biology. Generic metabolic pathways have long been known, and a number of websites (e.g., KEGG and BioCyc) attempt to map information from genome annotation to metabolic pathways (Kanehisa et al., Nucleic Acids Res 40:D109-D114, 2010; Karp et al., Nucleic Acids Res 33:6083-6089, 2005). Extending this analysis to incorporate metabolic flux models further allows in silico prediction of potential essential genes. Such efforts are of value, either to highlight novel generic antimicrobials or to seek novel treatments for non-paradigm organisms. Such in silico approaches are attractive as they can highlight pathways and genes that would otherwise only be identified by costly and time-consuming laboratory methods.