Designing an inducer-feeding schedule to enhance production of recombinant protein in Escherichia coli by microbial reaction engineering.
Metabolic constraints during the production of recombinant protein in Escherichia coli impede the efficient utilization of resources by the cells, thus reducing their production potential. In order to minimize these adverse effects, we have proposed to segregate the cell population into two groups: the first one formed by non-induced cells, growing at a high specific growth rate and rapidly contributing cells to the system, and the second one formed by fully induced cells, growing slowly but using the cell machinery to express the target protein. An adequate balance between these two populations should maximize the protein expression in a given system. This segregation is accomplished experimentally by taking advantage of the "all or none" phenomenon, in which at subsaturated inducer conditions the cells are either fully induced or fully uninduced. Based on this two-population theory, a mathematical model was developed in which a parameter alpha was defined as the fraction of the fully induced cells in the total population. In this study three different induction strategies were investigated and their effect on the protein production was established. It was found that the linear increase of this fraction, achieving maximum induction (alpha = 1) only at the end of the fermentation and with a slope m = 0.15 gave the best results. Finally these results were validated experimentally with the finding that they closely match the mathematical simulation with a 26% increase in protein production with respect to the conventional induction approach described.
Gonzalez
Jesus M
JM
Department of Chemical Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA.
Hsu
James T
JT
D000465:Algorithms; D019149:Bioreactors / Q000382:microbiology*; D018920:Coculture Techniques; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism*; D017022:Flow Injection Analysis / Q000379:methods; D008954:Models, Biological; D015202:Protein Engineering / Q000379:methods*; D011994:Recombinant Proteins / Q000302:isolation & purification* / Q000378:metabolism*
D011994:Recombinant Proteins