methods, broadly divided into stoichiometric and kinetic paradigms. Within the stoichiometric paradigm, a number of relatively simple linear algebra methods use restricted metabolic networks or genome-scale metabolic network models to perform flux balance analysis and the array of techniques derived from it. These linear equations are useful for steady state conditions. Dynamic methods are not yet usable. On the more experimental side, metabolic flux analysis allows the empirical estimation of reaction rates by stable isotope labelling. Within the kinetic paradigm, kinetic modelling of metabolic networks can be purely theoretical, exploring the potential space of dynamic metabolic fluxes under perturbations away from steady state using formalisms such as biochemical systems theory. Such explorations are most informative when accompanied by empirical measurements of the system under study following actual perturbations, as is the case in metabolic control analysis. Constraint based reconstruction and analysis Collected methods in fluxomics have been described as "COBRA" methods, for constraint based reconstruction and analysis. A number of software tools and environments have been created for this purpose. Although it can only be measured indirectly, metabolic flux is the critical link between genes, proteins and the observable phenotype. This is due to the fluxome integrating mass-energy, information, and signaling networks. Fluxomics has the potential to provide a quantifiable representation of the effect the environment has on the phenotype because the fluxome describes the genome environment interaction. In the fields of metabolic engineering and systems biology, fluxomic methods are considered a key enabling technology due to their unique position in the ontology of biological processes, allowing genome scale stoichiometric models to act as a framework for the integration of diverse biological datasets. Examples of use in research One potential application of fluxomic techniques is in drug design. Rama et al. used FBA to study the mycolic acid pathway in Mycobacterium tuberculosis. Mycolic acids are known to be important to M. tuberculosis survival and as such its pathway has been studied extensively. This allowed the construction of a model of the pathway and for FBA to analyze it. The results of this found multiple possible drug targets for future investigation. FBA was used to analyze the metabolic networks of multidrug-resistant Staphylococcus aureus. By performing in silico single and double gene deletions many enzymes essential to growth were identified. References Bioinformatics Systems biology Computational biology