Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics.

Autor:	Lüleci HB; Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey., Uzuner D; Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey., Çakır T; Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey., Thambisetty M; Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA. thambisettym@mail.nih.gov.
Jazyk:	angličtina
Zdroj:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2023; Vol. 2561, pp. 173-189.
DOI:	10.1007/978-1-0716-2655-9_9
Abstrakt:	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. (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze:	MEDLINE
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