Abstract
The search for optimization principles in microbial metabolism, such as biomass or ATP yields or growth rate optimization, has attracted substantial research efforts in the recent years. Here we use the results of C13 labeling experiments together with genome scale metabolic networks of S cerevisiae and E coli in order to assess if there are relationships between systemic variables that are present in both organisms. Strong correlations between the total flux per unit of substrate and the ATP turnover rate per unit of substrate and between the growth rate divided by the total flux and the total flux per unit of substrate were observed for both organisms. We also observed that the common assumption of biomass yield optimization is not consistent with the experiments.
Highlights
The search for optimization principles in microbial metabolism, such as biomass or ATP yields or growth rate optimization, has attracted substantial research efforts in the recent years
In this paper we use experimental flux distributions measured in two different microorganisms, S cerevisiae and E coli, with the aim of identifying possible additional criteria, that could be used in order to improve the predictive power of genome-scale metabolic models
It has been shown that experimental flux distributions observed in the central carbon metabolism of E. coli seem to be more consistent with the maximization of ATP production rate per flux unit[8], this predictions were done using www.nature.com/scientificreports a metabolic network with only 10 degrees of freedom
Summary
The search for optimization principles in microbial metabolism, such as biomass or ATP yields or growth rate optimization, has attracted substantial research efforts in the recent years. In this paper we use experimental flux distributions measured in two different microorganisms, S cerevisiae and E coli, with the aim of identifying possible additional criteria (objective functions or extra constrains), that could be used in order to improve the predictive power of genome-scale metabolic models. It has been shown that experimental flux distributions observed in the central carbon metabolism of E. coli seem to be more consistent with the maximization of ATP production rate per flux unit[8], this predictions were done using www.nature.com/scientificreports a metabolic network with only 10 degrees of freedom (while genomescale networks have typically hundreds of degrees of freedom). The main problem of assessing the suitability of objective functions by comparing predicted and measured flux distributions is the fact that in order to predict a flux distribution, one or more constraints (at least the uptake rate of a limiting substrate) have to be used together with the tested objective function[9,11] and formally any objective function is potentially able to generate correct predictions, provided that it is combined with a suitable set of constraints
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