Description
To identify a set of genetic manipulations that will result in a microbial strain with improved
production capabilities of a metabolite / product of industrial interest, is one of the
greatest challenges in Metabolic Engineering. This problem represents a complex combination
between the development of accurate metabolic and regulatory models / networks, plus
the need for appropriate simulation and optimization tools.
To achieve this end, Evolutionary Algorithms (EAs) and Simulation Annealing (SA) have
been previously proposed as tools to perform in silico Metabolic Engineering [1]. These
methods are used to identify sets of reaction deletions, towards the maximization of a desired
physiological objective function. In order to simulate the cell phenotype for each mutant
strain, including its growth and the by-products secretion, the Flux-Balance Analysis approach
is used, assuming that microorganisms have maximized their growth along evolution.
Currently, the available optimization algorithms work only with reaction deletions, i.e.
their result is a set of reactions that have to be removed from the metabolic model. Biologically,
it is possible to knockout genes, not reactions.
In this work, the transcriptional information is added to the underlying models using
gene-reaction rules based on a boolean logic representation. So, for each reaction we have
a Boolean expression, where the variables are the encoding genes and including the logical
AND and OR operators. The aim is to find the optimal / near-optimal set of gene knockouts
necessary to reach a given productivity goal. The results obtained are compared with the
ones using the deletion of reactions.
A set of computational experiments were performed, using four case studies and the production
of succinate and lactic acid as the metabolite to maximize and E. coli as the selected
organism. Genome-scale models including both reactions and gene-reaction rules [2] are used
to conduct the necessary FBA simulations.
The results show that several of the results from reaction deletion optimizations are not
feasible using the provided gene-reaction rules, i.e. the genes that would need to be removed
in order to delete the reaction also lead to the removal of other reactions causing side effects
that make the strain unviable.
Nevertheless, basing the optimization algorithms on gene knockouts, we were able to
reach solutions where the production of the desired compounds is similar to the ones using
reaction deletions.
; Maia, Paulo 
