Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.

BACKGROUND: The tuberculin skin test (TST) is the most widely used test for detecting tuberculosis (TB) infection. Accurate interpretation of TST requires consideration of three dimensions-the size of the skin reaction, the positive predictive value (PPV) and risk of disease. METHODS: We developed a web-based algorithm incorporating epidemiological, medical and radiographic risk factors to help in the interpretation of positive TST results in adults (http://www.meakins.mcgill.ca/meakins/NEW TST Calculator/homeE.htm). We used summary estimates from published reviews on the prevalence of latent TB infection, the likelihood of false-positive TST and risk of active TB disease. RESULTS: The algorithm calculations show that the most important determinants of risk of active disease are the presence of medical and radiographic risk factors, while the size of the reaction is of modest importance. In persons who have received bacille Calmette-Guérin vaccination after infancy, the algorithm calculations show that the PPV will be low. In such persons, the risk of disease is predicted to be very low, unless there are medical or radiographic risk factors that increase the risk of reactivation. CONCLUSIONS: Our web-based algorithm can generate clinically useful estimates of the annual and cumulative lifetime risk of developing TB in adults with a positive TST.