Abstract
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.
Original language | English |
---|---|
Title of host publication | 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Subtitle of host publication | proceedings |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Print) | 9781467394727 |
DOIs | |
Publication status | Published - 2016 |
Event | The annual IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2016) - Chiang Mai, Thailand Duration: 5 Oct 2016 → 7 Oct 2016 |
Conference
Conference | The annual IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2016) |
---|---|
City | Chiang Mai, Thailand |
Period | 5/10/16 → 7/10/16 |
Keywords
- evolutionary computation
- data integration
- metabolic engineering
- genomics
- synthetic biology
- Evolutionary computation