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Listeria monocytogenes  is a microorganism of great concern for the food industry and the cause of human foodborne disease. Therefore, novel methods of control are needed, and systems biology is one such approach to identify them. Using a combination of computational techniques and laboratory methods, genome-scale metabolic models (GEMs) can be created, validated, and used to simulate growth environments and discern metabolic capabilities of microbes of interest, including  L .  monocytogenes . The objective of the work presented here was to generate GEMs for six different strains of  L .  monocytogenes , and to both qualitatively and quantitatively validate these GEMs with experimental data to examine the diversity of metabolic capabilities of numerous strains from the three different serovar groups most associated with foodborne outbreaks and human disease. Following qualitative validation, 57 of the 95 carbon sources tested experimentally were present in the GEMs, and; therefore, these were the compounds from which comparisons could be drawn. Of these 57 compounds, agreement between  in silico  predictions and  in vitro  results for carbon source utilization ranged from 80.7% to 91.2% between strains. Nutrient utilization agreement between  in silico  predictions and  in vitro  results were also conducted for numerous nitrogen, phosphorous, and sulfur sources. Additionally, quantitative validation showed that the  L .  monocytogenes  GEMs were able to generate  in silico  predictions for growth rate and growth yield that were strongly and significantly (p < 0.0013 and p < 0.0015, respectively) correlated with experimental results. These findings are significant because they show that these GEMs for  L .  monocytogenes  are comparable to published GEMs of other organisms for agreement between  in silico  predictions and  in vitro  results. Therefore, as with the other GEMs, namely those for  Escherichia coli ,  Staphylococcus aureus ,  Vibrio vulnificus , and  Salmonella  spp., they can be used to determine new methods of growth control and disease treatment.

Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes