In-silico media optimization for continuous cultures using genome scale
metabolic networks: the case of CHO-K1
The cell culture is the central piece of a biotechnological industrial
process. It includes upstream (e.g. media preparation, fixed costs,
etc.) and downstream steps (e.g. product purification, waste disposal,
etc.). In the continuous mode of cell culture, a constant flow of fresh
media replaces culture fluid until the system reaches a steady state.
This steady state is the standard operation mode which, under very
general conditions, is a function of the ratio between the cell density
and the dilution rate and depends on the media supplied to the culture.
To optimize the production process it is widely accepted that the
concentration of the metabolites in this media should be careful tuned.
A poor media may not provide enough nutrients to the culture, while a
media too rich in nutrients may be a waste of resources because, either
the cells do not use all of the available nutrients, or worse, they
over-consume them producing toxic byproducts. In this work we show how
an in-silico study of a genome scale metabolic network coupled to the
dynamics of a chemostat could guide the strategy to optimize the media
to be used in a continuous process. Given a known media we model the
concentrations of the cells in a chemostat as a function of the dilution
rate. Then, we cast the problem of optimizing the production process
within a linear programming framework in which the goal is to minimize
the cost of the media keeping fixed the cell concentration for a given
dilution rate in the chemostat. We evaluate our results in two metabolic
models: first a simplified model of mammalian cell metabolism, and then
in a realistic genome-scale metabolic networks of mammalian cells, the
Chinese Hamster Ovary (CHO) cell line. We explore the latter in more
detail given specific meaning to the predictions of the concentrations
of several metabolites.