Computational intelligence for metabolic pathway design: Application to the pentose phosphate pathway

Metabolic engineering is increasingly being used for the production of industrial products such as pharmaceuticals and enzymes. These chemicals have traditionally been chemically synthesized, but the application of synthetic biology techniques to microbes facilitates faster, cheaper production. Mode...

Full description

Saved in:
Bibliographic Details
Published in2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp. 1 - 6
Main Authors Skelton, D. J., Hallinan, J. S., Park, S., Wipat, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Biochemistry
Biological system modeling
data integration
Data models
Evolutionary computation
genomics
Gold
Mathematical model
metabolic engineering
Organisms
Production
synthetic biology
Online AccessGet full text

Cover

More Information
Summary:Metabolic engineering is increasingly being used for the production of industrial products such as pharmaceuticals and enzymes. These chemicals have traditionally been chemically synthesized, but the application of synthetic biology techniques to microbes facilitates faster, cheaper production. Modelling and the integration of existing data can help inform the design of synthetic pathways. We applied an evolutionary algorithm to a flux balance model of metabolism in the industrially important bacterium Bacillus subtilis. Our target metabolites are sedoheptulose-7-phosphate and riboflavin, components of the pentose phosphate pathway. The algorithm combines the results of the flux balance analysis with phylogenetic information derived from data warehouses, to predict several potential interventions to the metabolic network, mostly involving knockouts of genes related to the pathway.
DOI:10.1109/CIBCB.2016.7758101