OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions
By Sridhar Ranganathan, Patrick F. Suthers, Costas D. Maranas. PLoS Comput. Biol., 2010
430 citations recorded by the source corpus.
Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/127c7d2255dea331e249c25c5bbdd8b1b64349d6
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In Molecular Biology
Citations in the corpus
- Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “A number of design algorithms have been built to work with metabolic models and predict the genetic and environmental modifications to increase performance (Burgard et al., 2003; Ranganathan et al., 2010).”
- OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities (2012, positive): “Taking a step further, OptCom can be readily modified to identify the minimal number of direct interventions (i.e., knock-up/down/outs) to the community leading to the elevated production of a desired compound (e.g., by considering the overproduction of desired compound as the outer problem objectiv”