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Constraint-based models such as flux balance analysis
(FBA) are a powerful tool to study biological metabolic
networks. Under the hypothesis that cells operate at an optimal
growth rate as the result of evolution and natural selection, this
model successfully predicts most cellular behaviours in growth rate.
However, the model ignores the fact that cells can change their
cellular metabolic states during evolution, leaving optimal
metabolic states unstable. Here, we consider all the cellular
processes that change metabolic states into a single term `noise',
and assume that cells change metabolic states by randomly walking in
feasible solution space. By simulating a state of a cell randomly
walking in the constrained solution space of metabolic networks, we
found that in a noisy environment cells in optimal states tend to
travel away from these points. On considering the competition
between the noise effect and the growth effect in cell evolution, we
found that there exists a trade-off between these two effects. As a
result, the population of the cells contains different cellular
metabolic states, and the population growth rate is at suboptimal
states. 相似文献
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