OptGene: a framework for in silico metabolic engineering
Year: 2008
Type: other
Abstract: Introduction In metabolic engineering problems, due to the complexity of metabolic networks, it is often difficult to identify a priori which genetic manipulations will originate a given desired phenotype. Genome-scale metabolic models (Patil et al., 2004), available for several microorganisms, can be used to simulate the metabolic phenotype and therefore help the tasks of metabolic engineering. This simulation can be performed by calculating the fluxes through all metabolic reactions using techniques like the Flux Balance Analysis (FBA) (Edwards et al, 2002) or the MOMA (Segre et al., 2002 approaches, among others. Several algorithms, like the framework OptKnock (Burgard et al., 2003) have been developed that use metabolic models to enable the identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2256957379