Neurology research and clinical practice are transforming toward postgenomics integrative biology. One such example is the study of human brain metabolism that is highly sophisticated due to reactions occurring in and between the astrocytes and neurons. Because of the inherent difficulty of performing experimental studies in human brain, metabolic network modeling has grown in importance to decipher the contribution of brain metabolite kinetics to human health and disease. Multiomics system science-driven metabolic models, using genome-scale and transcriptomics Big Data, offer the promise of new insights on metabolic networks in human brain. Added to this, the availability of omics technologies in both developed and developing world, neurology research, and clinical practice ought to be repositioned with a view to systems medicine. In this expert analysis, we present a critical and in-depth overview of the basic tenets of human brain metabolism, together with the most recent metabolic modeling strategies and computational studies of brain in health and neurological diseases. Human genome-scale metabolic models developed in a both global and brain-specific manner and multiomics synthesis of knowledge are highlighted in particular. We conclude by underscoring the value of multiomics modeling for metabolic diseases and computational investigations of the brain networks, with a view to unlocking the pathophysiology of Alzheimer's disease, Parkinson's disease, migraine, stroke, epilepsy, and multiple sclerosis, among other neurological disorders of importance for global health.
Keywords: genome-scale modeling; human brain metabolism; multiomics; neurological diseases; systems biology.