Redirector: designing cell factories by reconstructing the metabolic objective

PLoS Comput Biol. 2013;9(1):e1002882. doi: 10.1371/journal.pcbi.1002882. Epub 2013 Jan 17.

Abstract

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. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Down-Regulation
  • Fatty Acids / biosynthesis
  • Metabolic Networks and Pathways*
  • Models, Biological*
  • Up-Regulation

Substances

  • Fatty Acids

Grant support

This work was supported by grant DE-FG02-02ER63445 (DOE, Title: Microbial Ecology, Proteogenomics & Computational Optima) and SA5283-11210 (NSF-SynBERC, Title: Synthetic Biology Engineering Research Center). The funders had no role in this study design, data collection and analysis, decision to publish, or preparation of the manuscript.