The OptForce procedure identifies the minimal set of genetic interventions that shape the metabolism of a microorganism so as it guarantees a pre-specified biochemical product target yield. The procedure is designed to make use of flux measurements, whenever available, for the wild-type strain. OptForce works by comparing the allowable range of flux variability for a wild-type strain and the overproducing network. By comparing fluxes one-at-a-time (see Figure 1) we first identify reactions whose flux must increase (MUSTU), decrease (MUSTL) or completely shut-off (MUSTX) consistent with the overproduction target. For fluxes with overlapping ranges in the overproducing network, we compare sums (or differences) of two fluxes at a time (see Figure 2 and Non-overlapping sums imply that the flux of either one reaction OR the other MUST increase. These reaction pairs are appended within the MUSTUU set. We use a similar classification procedure to identify the pairs of reactions that populate MUSTUL and MUSTLL sets.

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Note that not all reactions in the MUST sets need to be actively engineered for overproduction. Typically, only a subset needs to be engineer and propagate this effect to all other members of the MUST sets by virtue of stoichiometry and other constraints. We use a bilevel optimization framework to identify the minimal set of engineering interventions that FORCE the wild-type network phenotype towards the overproducing network.

Related Publications:

Ranganathan. S, Suthers. P. F., and Maranas. C. D. (2010), "OptForce: An optimization procedure for identifying all genetic manipulations leading to targeted overproductions", PLoS Computational Biology 6(4): e1000744. doi:10.1371/journal.pcbi.1000744

Ranganathan. S, and Maranas. C. D. (2010), "Microbial 1-butanol production: Identification of non-native production routes and in silico engineering interventions", Biotechnology Journal 5(7) 716. doi:10.1002/biot.201000171

Xu .P, Ranganathan .S, Fowler. Z. L., Maranas. C. D., Koffas. M. A. (2011), "Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA", Metabolic Engineering doi:10.1016/j.ymben.2011.06.008

Chowdhury A., Zomorrodi A.R., Maranas C.D. (2015), "Bilevel optimization techniques in computational strain design", Computers and Chemical Engineering, 72:363-372.


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