Designing cell factories by reconstructing metabolic objectives
Rockwell, Graham Nalley
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Predictive modeling enables a working understanding of the fundamental principles of biological systems that can be harnessed to create designer biological systems. Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. This work presents Redirector and Genetic Design Local Search (GDLSL new flux balance analysis-based frameworks for designing cell factories through the identification of optimal metabolic alterations. GDLS uses local search heuristics and an evolving population of designs to expand the capacity of metabolic optimization frameworks to find locally complete sets of gene targets across genome-scale models. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach that models metabolic alterations as changes in the balance of metabolic pressures on the system as represented by changes in the metabolic objective. Redirector treats engineering changes as acting in competition with cellular growth, adjusts their contributions to the metabolic objective, and progressively identifies an expanding set of modifications that direct flux towards metabolite production. Using the iAF1260 E. coli metabolic model with fatty acid production as a test case, Redirector discovers proven in vivo targets, novel supporting pathways and relevant interdependencies. We also show that many of these same proven targets would be very challenging or impossible to discover using limit based representations of metabolic alterations. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.
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