Digital twins for in vivo metabolic flux estimations in patients with brain cancer.

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 met

SUBMITTER: Meghdadi B 

PROVIDER: S-EPMC12695069 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature