S136: Rational approaches to improve bioprocess productivity

Wednesday, August 4, 2010: 10:30 AM
Bayview A (Hyatt Regency San Francisco)
Nikolaos Anesiadis1, Pratish Gawand1, Andrew Ekins2, Hideki Kobayashi3, Vincent J. J. Martin2, William R. Cluett1 and Radhakrishnan Mahadevan1, (1)Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada, (2)Department of Biology, Concordia University, Montreal, QC, Canada, (3)Extremobiosphere Research Center (XBR), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Bioprocess development typically requires several rounds of metabolic engineering to meet process targets, including product yield, titer and productivity, all of which impact the process economics. Other important factors in the bioprocess development are the design considerations associated with process scale-up.  Since most industrial processes are carried out in either batch or fed-batch mode starting from the seed cultures, productivity often becomes a limiting factor. In this talk, two approaches to improve productivity will be presented. In the first approach, the use of synthetic biology tools to manipulate bacterial metabolism and apply optimization and control principles at the genetic level by “re-wiring” the bacterial machinery will be presented. We have previously proposed a dynamic metabolic engineering strategy in which the artificial genetic toggle switch is exploited in a biphasic fashion to optimize the productivity. The dynamic switch employs the cells to direct the carbon towards biomass in the first phase of a batch and redirect it towards the desired product in the second phase. In the second approach, we will present a rational model-based approach to increase the rate of product synthesis by optimizing the substrate consumption rates. Co-utilization of substrates is one of the traits that can help improve productivity of bioprocesses. It is a desired attribute in industrial organisms, particularly the ones used for lignocellulosic bioethanol production. We have developed a rational approach based on the constraint-based model of metabolism to enforce co-utilization and have illustrated this approach using Escherichia coli as a model organism.