Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors
Authored by Mengrou Shan, David Dai, Arunodai Vudem, Jeffrey D Varner, Abraham D Stroock
Date Published: 2018
DOI: 10.1371/journal.pcbi.1006584
Sponsors:
United States National Institutes of Health (NIH)
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Abstract
Cancer metabolism has received renewed interest as a potential target
for cancer therapy. In this study, we use a multi-scale modeling
approach to interrogate the implications of three metabolic scenarios of
potential clinical relevance: the Warburg effect, the reverse Warburg
effect and glutamine addiction. At the intracellular level, we construct
a network of central metabolism and perform flux balance analysis (FBA)
to estimate metabolic fluxes; at the cellular level, we exploit this
metabolic network to calculate parameters for a coarse-grained
description of cellular growth kinetics; and at the multicellular level,
we incorporate these kinetic schemes into the cellular automata of an
agent-based model (ABM), iDynoMiCS. This ABM evaluates the
reaction-diffusion of the metabolites, cellular division and motion over
a simulation domain. Our multi-scale simulations suggest that the
Warburg effect provides a growth advantage to the tumor cells under
resource limitation. However, we identify a non-monotonic dependence of
growth rate on the strength of glycolytic pathway. On the other hand,
the reverse Warburg scenario provides an initial growth advantage in
tumors that originate deeper in the tissue. The metabolic profile of
stromal cells considered in this scenario allows more oxygen to reach
the tumor cells in the deeper tissue and thus promotes tumor growth at
earlier stages. Lastly, we suggest that glutamine addiction does not
confer a selective advantage to tumor growth with glutamine acting as a
carbon source in the tricarboxylic acid (TCA) cycle, any advantage of
glutamine uptake must come through other pathways not included in our
model (e.g., as a nitrogen donor). Our analysis illustrates the
importance of accounting explicitly for spatial and temporal evolution
of tumor microenvironment in the interpretation of metabolic scenarios
and hence provides a basis for further studies, including evaluation of
specific therapeutic strategies that target metabolism.
Tags
Evolution
Heterogeneity
Microenvironment
growth
Mathematical-model
Hallmarks
Flux
Metabolic requirements
Aerobic glycolysis
Quiescent cells