Abstract
The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.
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Campit, S., Chandrasekaran, S. (2020). Inferring Metabolic Flux from Time-Course Metabolomics. In: Nagrath, D. (eds) Metabolic Flux Analysis in Eukaryotic Cells. Methods in Molecular Biology, vol 2088. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0159-4_13
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DOI: https://doi.org/10.1007/978-1-0716-0159-4_13
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