High energy consumption nowadays alongside with concerns on the environment had caused rising demand for
synthetic alternative fuels. These include biofuels that can be produced from a variety of engineered microbes such as
Escherichia coli. In the metabolic engineering field, this is done by genetically modifying the target microbes to obtain
optimal production of a particular biochemical. Conventional metabolic engineering approaches often intuitive, but with
advancements in modern biology, vast amount of informative data generated from time to time to describe the metabolism
system of the microbes more thoroughly. Discoveries from interpreting these available data using computational
approaches are highly beneficial to metabolic engineers, especially professionals working in the in silico metabolic
engineering field. Within the past decade, many computational approaches and routines have been proposed and
developed in providing a platform to discover rational strategies to aid biologists in engineering the metabolic network.
Here, efforts to find the optimal butanol production route in E. coli as well as several optimization algorithms currently
available for finding optimal solution to enhance biochemical production in designated target microbe are discussed. This
review aims to show different optimization algorithms developed for in silico metabolic engineering and their applications
in microbial fuel production.
Keywords: Biofuels, computational biology, in silico metabolic engineering, metabolic networks, microbial strain
improvement, optimization, system biology, Bi-Level Optimization, Dynamic FBA, molecular docking, Enzyme fitness function
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