Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches

Shoval Lagziel, Won Dong Lee, Tomer Shlomi
<span title="2019-07-04">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sim25qtjzrbhfpgmid2b4zhpd4" style="color: black;">BMC Biology</a> </i> &nbsp;
The study of tumorigenic rewiring of metabolic flux is at the heart of cancer metabolic research. Here, we review two widely used computational flux inference approaches: isotope tracing coupled with Metabolic Flux Analysis (13C-MFA) and COnstraint-Based Reconstruction and Analysis (COBRA). We describe the applications of these complementary modeling techniques for studying metabolic adaptations in cancer cells due to genetic mutations and the tumor microenvironment, as well as for identifying
more &raquo; ... ovel enzymatic targets for anti-cancer drugs. We further highlight the advantages and limitations of COBRA and 13C-MFA and the main challenges ahead. Inferring metabolic flux in cancer research Cellular metabolism is a dynamic system in which metabolic nutrients are being constantly consumed and catabolized to generate energy (Fig. 1a) . Proliferating cancer cells further activate anabolic pathways to produce metabolic precursors for synthesizing macromolecules, including DNA, RNA, proteins, and lipids [1, 2] . This is facilitated via a complex metabolic network consisting of thousands of biochemical reactions [3, 4] . The dynamics of metabolism can be described in terms of the rate of metabolic reactions, typically referred to as metabolic flux (denoting the rate of transformation of a substrate to product metabolites in units of moles per unit of time per cell). A major goal of cancer metabolic research is understanding
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