Targeting the changes in the metabolic network is an attractive therapeutic avenue in treating autoimmune disorders like SLE that is orthogonal to current immunosuppressant-based treatments or even more novel biologics targeting specific immunological cells or pathways. However, our current understanding of metabolic differences in SLE and healthy T cells is limited to studies performed on individual pathways in isolation. While it remains experimentally challenging to directly probe the entire metabolic network, computational methods can integrate several pieces of multi-omics information and biophysical constraints to predict network activity. We believe that these approaches will serve as an important tool in forming global understanding of T cell metabolism in health and disease.