Activation of immune cells is accompanied by a metabolic reconfiguration of their cellular energy metabolism including shifts in glycolysis and mitochondrial respiration that critically regulate functional effector responses. However, while current mass spectrometry strategies identify overall or flux-dependent metabolite profiles of cells or tissues, they fail to comprehensively identify the checkpoint nodes and enzymes that are responsible for different metabolic outputs. Here, we demonstrate that a data-driven inverse modelling approach from mass spectrometry metabolomics data can be used to identify a causal biochemical node that influence overall metabolic profiles and reactions. In our study we applied this strategy to TSC2/mTORC1-dependent macrophage polarization. Using multiomics metabolomics, proteomics and transcriptomics analysis as well as enzymatic activity measurements we demonstrate that TSC2, a negative regulator of mTORC1 signaling, critically influences the cellular metabolism of macrophages by regulating the enzyme phosphoglycerate dehydrogenase (PHGDH), a rate-limiting enzyme that diverts carbon from glycolysis for de novo serine/glycine biosynthesis. This is the first evidence that the metabolic kinase mTORC1 positively regulates PHGDH activity in macrophages. Importantly, PHGDH itself is a central regulator of macrophage polarization. Anti-inflammatory (M2) macrophages have high PHGDH activity that is required for the expression of typical anti-inflammatory molecules. Inhibition of PHGDH activity suppressed marker genes in IL-4 stimulated M2 macrophages. This identifies PHGDH as a metabolic signature of M2 macrophages. The presented concept of data-driven inverse modelling and multiomics analysis allows for the systematic integration of genome-scale metabolic reconstruction, prediction and analysis of causal biochemical regulation.