Advances in experimental technologies in conjunction with the increasing number of sequenced genomes permitted uncovering
new molecular interactions aiding in the characterization of individual cellular components.
Until recently, in bioprocess engineering, cells were modeled as black box entities responsible for consuming substrates and
producing certain compounds, ignoring the underlying biological mechanisms. Nonetheless, the development of cellular
mechanistic dynamic models has been hampered by the lack of specific experimental data and imprecise knowledge of the
mechanistic rate laws underlying several reactions. This fact hardens the applicability of engineering concepts to cellular
systems.
Even incomplete cellular models provide valuable insights to help consolidate ongoing efforts in Biotechnology, namely, the
growing tendency in industry to replace chemical synthesis techniques by biotechnological ones. These tendencies are driven
by sustainability and profitability concerns, regarding the production of certain chemical compounds like bulk chemicals and
pharmaceuticals.
It is important to bear in mind that the metabolism of wild-type microorganisms is geared to its survival and reproduction
without engaging in the production of compounds outside this scope. Thus, the metabolism usually has to be modified in
order to meet the desired industrial outcome, typically the overproduction of a target compound.
In this work an optimization algorithm based on Evolutionary Computation approaches was previously developed in order
to enhance the production of a target metabolite based on a dynamic metabolic model.
An extension of mechanistic Escherichia coli model is currently being developed in order to study how to improve the
production of industrial relevant compounds
