45 research outputs found
Limits of aerobic metabolism in cancer cells
Cancer cells exhibit high rates of glycolysis and glutaminolysis. Glycolysis can provide energy and glutaminolysis can provide carbon for anaplerosis and reductive carboxylation to citrate. However, all these metabolic requirements could be in principle satisfied from glucose. Here we investigate why cancer cells do not satisfy their metabolic demands using aerobic biosynthesis from glucose. Based on the typical composition of a mammalian cell we quantify the energy demand and the OxPhos burden of cell biosynthesis from glucose. Our calculation demonstrates that aerobic growth from glucose is feasible up to a minimum doubling time that is proportional to the OxPhos burden and inversely proportional to the mitochondria OxPhos capacity. To grow faster cancer cells must activate aerobic glycolysis for energy generation and uncouple NADH generation from biosynthesis. To uncouple biosynthesis from NADH generation cancer cells can synthesize lipids from carbon sources that do not produce NADH in their catabolism, including acetate and the amino acids glutamate, glutamine, phenylalanine and tyrosine. Finally, we show that cancer cell lines have an OxPhos capacity that is insufficient to support aerobic biosynthesis from glucose. We conclude that selection for high rate of biosynthesis implies a selection for aerobic glycolysis and uncoupling biosynthesis from NADH generation
A physical model of cell metabolism
Cell metabolism is characterized by three fundamental energy demands: to sustain cell maintenance, to trigger aerobic fermentation and to achieve maximum metabolic rate. The transition to aerobic fermentation and the maximum metabolic rate are currently understood based on enzymatic cost constraints. Yet, we are lacking a theory explaining the maintenance energy demand. Here we report a physical model of cell metabolism that explains the origin of these three energy scales. Our key hypothesis is that the maintenance energy demand is rooted on the energy expended by molecular motors to fluidize the cytoplasm and counteract molecular crowding. Using this model and independent parameter estimates we make predictions for the three energy scales that are in quantitative agreement with experimental values. The model also recapitulates the dependencies of cell growth with extracellular osmolarity and temperature. This theory brings together biophysics and cell biology in a tractable model that can be applied to understand key principles of cell metabolism
Characterizing steady states of genome-scale metabolic networks in continuous cell cultures
We present a model for continuous cell culture coupling intra-cellular
metabolism to extracellular variables describing the state of the bioreactor,
taking into account the growth capacity of the cell and the impact of toxic
byproduct accumulation. We provide a method to determine the steady states of
this system that is tractable for metabolic networks of arbitrary complexity.
We demonstrate our approach in a toy model first, and then in a genome-scale
metabolic network of the Chinese hamster ovary cell line, obtaining results
that are in qualitative agreement with experimental observations. More
importantly, we derive a number of consequences from the model that are
independent of parameter values. First, that the ratio between cell density and
dilution rate is an ideal control parameter to fix a steady state with desired
metabolic properties invariant across perfusion systems. This conclusion is
robust even in the presence of multi-stability, which is explained in our model
by the negative feedback loop on cell growth due to toxic byproduct
accumulation. Moreover, a complex landscape of steady states in continuous cell
culture emerges from our simulations, including multiple metabolic switches,
which also explain why cell-line and media benchmarks carried out in batch
culture cannot be extrapolated to perfusion. On the other hand, we predict
invariance laws between continuous cell cultures with different parameters. A
practical consequence is that the chemostat is an ideal experimental model for
large-scale high-density perfusion cultures, where the complex landscape of
metabolic transitions is faithfully reproduced. Thus, in order to actually
reflect the expected behavior in perfusion, performance benchmarks of
cell-lines and culture media should be carried out in a chemostat
Cell population heterogeneity driven by stochastic partition and growth optimality
A fundamental question in biology is how cell populations evolve into
different subtypes based on homogeneous processes at the single cell level.
Here we show that population bimodality can emerge even when biological
processes are homogenous at the cell level and the environment is kept
constant. Our model is based on the stochastic partitioning of a cell component
with an optimal copy number. We show that the existence of unimodal or bimodal
distributions depends on the variance of partition errors and the growth rate
tolerance around the optimal copy number. In particular, our theory provides a
consistent explanation for the maintenance of aneuploid states in a population.
The proposed model can also be relevant for other cell components such as
mitochondria and plasmids, whose abundances affect the growth rate and are
subject to stochastic partition at cell division