Fig. 3: Workflow of iMAT-based metabolic network modeling.

AD Alzheimer’s disease, CN control, ERC entorhinal cortex. Description of workflow of iMAT-based metabolic network modeling to predict significantly altered enzymatic reactions relevant to de novo cholesterol biosynthesis, catabolism, and esterification in the AD brain. a Our human GEM network included 13417 reactions associated with 3628 genes ([1]). Genes in each sample are divided into three categories based on their expression: highly expressed (>75th percentile of expression), lowly expressed (<25th percentile of expression), or moderately expressed (between 25th and 75th percentile of expression) ([2]). Only highly- and lowly expressed genes are used by iMAT algorithm to categorize the reactions of the Genome-Scale Metabolic Network (GEM) as active or inactive using an optimization algorithm. Since iMAT is based on the prediction of mass-balanced based metabolite routes, the reactions indicated in gray are predicted to be inactive ([3]) by iMAT to ensure maximum consistency with the gene expression data; two genes (G1 and G2) are lowly expressed, and one gene (G3) is highly expressed and therefore considered to be post-transcriptionally downregulated to ensure an inactive reaction flux ([5]). The reactions indicated in black are predicted to be active ([4]) by iMAT to ensure maximum consistency with the gene expression data; 2 genes. (G4 and G5) are highly expressed and one gene (G6) is moderately expressed and therefore considered to be post-transcriptionally upregulated to ensure an active reaction flux ([6]). b Reaction activity (either active (1) or inactive (0) is predicted for each sample in the dataset ([7]). This is represented as a binary vector that is brain region and disease-condition specific; each reaction is then statistically compared using a Fisher Exact Test to determine whether the activity of reactions is significantly altered between AD and CN samples ([8]).