Model-based Strategies for Improving Bioprocess Yield and Robustness

Bioprocess development for biofuels and biochemicals typically requires several rounds of metabolic engineering to meet process targets including product yield, titer and productivity, all of which impact the process economics. Similar advances in computational modeling techniques have allowed the development of genome-scale models of metabolism in several organisms.Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. .In this talk, the use of such models for metabolic engineering will be presented. In the first part, a novel nested nonlinear optimization method for metabolic engineering resulting in hundreds of different strain design strategies for biochemicals production will be presented.  We will also examine the role of redundant production pathways from a design perspective and present computational results on how these pathways are valuable for robust design. Another approach is constrained Minimal Cut Sets (cMCSs) where the goal is identify modifications to improve yield. However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype. We generalize the cMCSs approach to constrained regulatory MCSs (cRegMCSs), where up/downregulation of reaction rates can be combined along with reaction deletions. We show that flux up/downregulations can virtually be treated as cuts allowing their direct integration into the algorithmic framework of cMCSs. These results show the value of combining strain prioritization methods with strain design methods to facilitate the experimental implementation of the strain designs.