1 research outputs found
Evolutionary approaches for strain optimization using dynamic models under a metabolic engineering perspective
One of the purposes of Systems Biology is the quantitative
modeling of biochemical networks. In this effort, the use of dynamical
mathematical models provides for powerful tools in the prediction of the
phenotypical behavior of microorganisms under distinct environmental
conditions or subject to genetic modifications.
The purpose of the present study is to explore a computational environment
where dynamical models are used to support simulation and optimization
tasks. These will be used to study the effects of two distinct
types of modifications over metabolic models: deleting a few reactions
(knockouts) and changing the values of reaction kinetic parameters. In
the former case, we aim to reach an optimal knockout set, under a defined
objective function. In the latter, the same objective function is used, but
the aim is to optimize the values of certain enzymatic kinetic coefficients.
In both cases, we seek for the best model modifications that might lead to
a desired impact on the concentration of chemical species in a metabolic
pathway. This concept was tested by trying to maximize the production
of dihydroxyacetone phosphate, using Evolutionary Computation approaches.
As a case study, the central carbon metabolism of Escherichia
coli is considered. A dynamical model based on ordinary differential equations
is used to perform the simulations. The results validate the main
features of the approach