However, here we have found that airway and blood neutrophils expressed a common DAPT limited set of 206 genes. A comparison with expression in control blood neutrophils revealed that airway and blood neutrophils showed also a difference in the variance of these 206 genes, again suggesting a transcriptome profile of airway neutrophils not coincident with that of blood neutrophils. Interestingly, antibiotic therapy did not substantially change this pattern, indicating a non responsiveness of airway neutrophils, likely due to lack of therapeutic amounts of drug in the lung. There are a few important limitations to this work. First, this study was done on a small cohort. Validation of the results reported herein will need a higher number of patients, either homozygous for the F508del mutation or bearing other mutations. Moreover, some patients of our study did not respond to the intravenous antibiotic treatment. We have not excluded nonresponders, which would have further limited the consistency of the study. Second, we did not compare patients in acute exacerbation with patients in stable conditions, thus we could not identify the baseline gene expression profile. Nevertheless, these results should be further expanded to comprehend whether these genes have applicability as biomarkers in clinical trials for antibiotics and antiinflammatory drugs. Ideally, models of metabolism should predict metabolite levels, characterize the thermodynamic requirements of pathways and processes, be testable with experimental data, and provide insight into the principles of cellular function and self-organization. Simulations based on the law of mass action, such as kinetic simulations, can in principle meet these requirements. However, these simulations require knowledge of the thousands of rate constants involved in the reactions. The measurement of rate constants is very labor intensive, and hence rate constants for most enzymatic reactions are not available. Moreover, the same prima facie enzymes from different species, or even different strains, have differing rate constants. For example, for dihydrofolate reductase, the turnover rates for the substrate 7,8dihydrofolate measured in vitro vary five orders of magnitude across species – from 284 s21 to less than 1 s21. If one were to model the metabolism of an organism using kinetic simulations, the rate constants for each enzyme would first need to be measured. Currently, flux-based approaches are the methods of choice for modeling metabolism because they do not require the use of rate constants.