l-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli
1 Institute for Innovation, Ajinomoto Co. Inc., Suzuki-cho 1-1, Kawasaki-ku, Kawasaki City, Kanagawa 210-8681, Japan
2 Department of Bioinformatics, Tokyo Medical and Dental University, Yushima 1-5-45, Bunkyo-ku, Tokyo 113-8510, Japan
3 Pharmaceutical Custom Manufacturing Department, Ajinomoto Co. Inc., Kyobashi 1-chome 15-1, Chuo-ku, Tokyo 104-8315, Japan
4 Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co. Inc., Suzuki-cho 1-1, Kawasaki-ku, Kawasaki City, Kanagawa 210-8681, Japan
5 Faculty of Symbiotic Systems Science, Fukushima University, Kanayagawa 1, Fukushima City, Fukushima 960-1296, Japan
6 Current address: Department of Health Record Informatics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-cho 4-1, Aoba-ku, Sendai-City, Miyagi 980-8575, Japan
BMC Systems Biology 2013, 7:92 doi:10.1186/1752-0509-7-92Published: 22 September 2013
Understanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required.
We constructed an L-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for L-glutamic acid production; the results of this process corresponded with previous experimental data regarding L-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of L-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model L-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in L-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model.
In this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation.