E.coli K-12 MG1655 Metabolic Network Analysis


Genome-wide metabolic modeling is emerging as a tool for manipulating gene and metabolite interactions. We are interested in knocking out pathways in the models that would affect overproduction and underproduction of metabolites in glycolysis. We hope our results will yield novel ways to increase the efficiency output of chemicals and metabolites that can be utilized for industry.

In silico models have become increasingly popular as this methodology not only has the potential to increase the rate at which we discover new phenomena but also curb laboratory costs. Additionally, the treatments developed through this route can be tailored for each individual patient, increasing the efficacy of the therapeutic. In silico models are aided by many efforts such as KEGG (Kyoto Encyclopedia of Genes and Genomes), which is an integrative collection of much omics data spanning over 4 broad categories (systems information, perturbed systems information, chemical information, and genomic information). The KEGG database draws upon its comprehensive experimental knowledge on metabolism and gene catalogs of sequenced genomes to effectively predict higher level processes like the metabolic pathways of a cell and even an organism. Consequently, it is now possible to simulate and analyze the effects of disturbances on an organism’s metabolic pathway through flux balance analysis, which is further explained in Materials.

KEGG has also enhanced the field of metabolic engineering by streamlining the process of targeting promising gene knockouts. There are currently many attempts to figure ways to maximize yield of many metabolites such as ethanol and lycopene without compromising the health of the organism.

KEGG File on known metabolic pathways in E.Coli

KEGG File on known metabolic pathways in E.Coli


KEGG file on the Glycolysis Pathway in E. Coli



The concept of the project was to develop a dynamic model of the metabolic network in E. coli. This would allow the manipulation of certain reactions to determine their overall effect. One of the key features in the project was to build a model to interpret the Systems Biology Markup Language (SBML) file and construct it into a matrix containing the complete set of metabolic reactions in order simulate gene knockouts. This in turn would investigate whether it is possible to maximize the yield of metabolites without compromising the fate of the organism.


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