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. Author manuscript; available in PMC: 2022 Jan 20.
Published in final edited form as: Cell Syst. 2021 Jan 20;12(1):68–81.e11. doi: 10.1016/j.cels.2020.12.001

Figure 1. An automated bioinformatics pipeline integrates multi-omics data into personalized FBA models of TCGA patient tumors.

Figure 1.

(A) Implementation of flux balance analysis (FBA), including utilization of a stoichiometric representation of the Recon3D human metabolic network reconstruction, application of reaction constraints to obtain a solution space of flux values, and maximization of an objective function within this valid solution space. (B) Flux balance analysis (FBA) calculates the objective value, i.e., the maximum value of the objective function. Flux variability analysis (FVA) calculates the minimum and maximum possible fluxes through each metabolic reaction while maintaining the objective function at its maximum value. (C) Pipeline for integrating multi-omics data from The Cancer Genome Atlas (TCGA) and publically-available repositories into personalized FBA models of TCGA patient tumors. (D) Classification of TCGA patient tumors into radiation-sensitive and -resistant classes based on observed decrease/increase in tumor size following radiation therapy. See also Figures S1–S2.