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Fluxomics refers to a range of methods in experimental and computational biology that attempt to infer or predict the rates of metabolic reactions in biological systems. This includes a number of different methods, broadly divided into stoichiometric and kinetic paradigms. Within the stoichiometric paradigm, a number of relatively simple linear algebra methods utilise restricted metabolic networks or genome-scale metabolic network models to perform flux balance analysis and the array of techniques derived from it. 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. 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. Especially 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 . ==References== 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「fluxomics」の詳細全文を読む スポンサード リンク
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