Supersystems Biology and Systems Medicine in the Clinic: A Translational Journey

Systems biology tools are now being applied at individual and population levels to understand integrated biochemical function of complex organisms including man. Metabolic phenotyping offers an important window on systemic activity and both NMR and mass spectrometric methods have been successfully applied to characterize and quantify a wide range of metabolites in multiple biological compartments to explore the biochemical sequelae of human disease. There also is extensive cross-talk between the host and the gut microbiome at the metabolic control and signalling level that is modulated in exquisitely complex ways by genes and environment and link to disease risk factors. These symbiotic supraorganismal interactions greatly increase the degrees of freedom of the metabolic system that poses a significant challenge to fundamental notions on the nature of the human diseased state, the etiopathogenesis of common disease, and current systems modelling requirements for personalized medicine. We have developed new scalable and translatable strategies for “phenotyping the hospital patient journey” using top-down systems biology tools that capitalize on the use of both metabolic modelling and pharmaco-metabonomics for diagnostic and prognostic biomarker generation to aid clinical decision making at the point-of-care. Such diagnostics (including those for near real-time applications as in surgery and critical-care) can be extremely sensitive for the detection of diagnostic and prognostic biomarkers in a variety of conditions. They are a powerful adjunct to conventional procedures for disease assessment that are required for future developments in “precision medicine.” Many biomarkers have deeper mechanistic significance and may also generate new therapeutic leads or metrics of efficacy for clinical trial deployment. Furthermore the complex and subtle gene-environment interactions that generate disease risks in the general human population also express themselves in the metabolic phenotype. As such the “Metabolome Wide Association Study” approach gives us a powerful new tool to generate disease risk biomarkers from epidemiological sample collections and for assessing the health of whole populations. Such population risk models and biomarkers can also feedback to individual patient healthcare models, thus closing the personal and public healthcare modeling triangle.