ArticleMethods in molecular biology (Clifton, N.J.)2023
Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics.
Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 7 citations in OpenAlex.
- Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants.iScience · 2026Article
- Comparative Proteomics Highlights that GenX Exposure Leads to Metabolic Defects and Inflammation in Astrocytes.Environmental science & technology · 2024Article
- A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.NPJ systems biology and applications · 2024Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
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Abstract
Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.
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