  We propose a new scheme to infer the metabolic fluxes of cell cultures in a
chemostat. Our approach is based on the Maximum Entropy Principle and exploits
the understanding of the chemostat dynamics and its connection with the actual
metabolism of cells. We show that, in continuous cultures with limiting
nutrients, the inference can be done with {\it limited information about the
culture}: the dilution rate of the chemostat, the concentration in the feed
media of the limiting nutrient and the cell concentration at steady state.
Also, we remark that our technique provides information, not only about the
mean values of the fluxes in the culture, but also its heterogeneity. We first
present these results studying a computational model of a chemostat. Having
control of this model we can test precisely the quality of the inference, and
also unveil the mechanisms behind the success of our approach. Then, we apply
our method to E. coli experimental data from the literature and show that it
outperforms alternative formulations that rest on a Flux Balance Analysis
framework.
