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Publication
Digital twins for in vivo metabolic flux estimations in patients with brain
cancer.
Authors Meghdadi B, Al-Holou WN, Scott AJ, Mittal A, Liang N, Sravya P, Achreja A,
O'Brien A, Do K, Wu Z, Feng J, Qi NR, Tarnal V, Venneti S, Miller CR, Sarkaria
JN, Zhou W, Lawrence TS, Lyssiotis CA, Wahl DR, Nagrath D
Submitted By Submitted Externally on 1/30/2026
Status Published
Journal Cell Metabolism
Year 2026
Date Published 1/6/2026
Volume : Pages 38 : 228 - 246.e17
PubMed Reference 41330373
Abstract Recent advancements in metabolic flux estimations in vivo are limited to
preclinical models, primarily due to challenges in tissue sampling, tumor
microenvironment (TME) heterogeneity, and non-steady-state conditions. To
address these limitations and enable flux estimation in human patients, we
developed two machine learning-based frameworks. First, the digital twin
framework (DTF) integrates first-principles stoichiometric and isotopic
simulations with convolutional neural networks to estimate fluxes in patient
bulk samples. Second, the single-cell metabolic flux analysis (13C-scMFA)
framework combines patient single-cell RNA sequencing (scRNA-seq) data with
13C-isotope tracing, allowing single-cell-level flux quantification. These
studies allow quantification of metabolic activity in neoplastic glioma cells,
revealing frequently elevated purine synthesis and serine uptake, compared with
non-malignant cells. Our models also identify metabolic heterogeneity among
patients and mice with brain cancer, in turn predicting treatment responses to
metabolic inhibitors. Our frameworks advance in vivo metabolic flux analysis,
may lead to novel metabolic therapies, and identify biomarkers for
metabolism-directed therapies in patients.




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