Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization
By Anthony P. Burgard, Prit Pharkya, Costas D. Maranas. Biotechnology and Bioengineering, 2003
1,297 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/21e620d46f7617b82e929959887bf1a0b844e34b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Bernhard O. Palsson
- Costas D. Maranas
- Jason A. Papin
- V. Schachter
- Pierre-Yves Bourguignon
- Maxime Durot
- Prit Pharkya
- Nathan E. Lewis
In Molecular Biology
- Bernhard O. Palsson
- Costas D. Maranas
- Jonathan M. Monk
- V. Schachter
- Pierre-Yves Bourguignon
- Maxime Durot
- Jens Nielsen
- Jason A. Papin
Citations in the corpus
- Efficient searching and annotation of metabolic networks using chemical similarity (2015, background): “…have been developed to optimize existing pathways, engineer novel pathways and identify drug targets by simulating the effect of remodeling the underlying metabolic network and analyzing its topology (Burgard et al. , 2003; Cho et al. , 2010; Guimera` et al. , 2007; Ranganathan et al. , 2010).”
- Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “Second, when the pathway was introduced, the organism did not produce BDO at high rates; thus, a ‘rational’ approach to producing a metabolic design was pursued using the E. coli CBM 45 and the OptKnock algorithm 74 .”
- Flux coupling analysis of genome-scale metabolic network reconstructions. (2004, background): “…(Edwards et al. 2001; Ibarra et al. 2002); (4) assessing the performance limits of metabolic networks in response to gene additions or deletions (Bur-gard and Maranas 2001); and (5) suggesting gene knockout strategies for enhancing biochemical production (Burgard et al. 2003; Pharkya et al. 2004).”
- Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “However, as OptKnock optimizes both the biomass objective function and product yield, strain designs occasionally have alternative optima with other secretion products, leading to a potential low product secretion rate ( FIG.”
- Applications of genome-scale metabolic reconstructions (2009, methodology): “The Optknock analysis predicted that increasing ATP demand would increase NADPH oxidation rates, and subsequent alteration of membrane-bound F0F1 ATP synthase 6 Molecular Systems Biology 2009 & 2009 EMBO and Macmillan Publishers Limited achieved the predicted increase in G. sulfurreducens respiratio”
- OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (2010, methodology): “maximization of biomass yield [21] or minimization of metabolic adjustments MOMA [42]) to estimate flux redirection upon strain engineering.”
- On the solution of convex bilevel optimization problems (2015, neutral): “[5]), greenhouse gas emissions (Hibino et al.”
- Evolutionary programming as a platform for in silico metabolic engineering (2005, methodology): “[22,23] (named OptKnock) represents one of the first rational modeling frameworks for suggesting gene knockouts leading to the overproduction of a desired metabolite.”
- An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. (2006, methodology): “In this paper, we describe the modeling and algorithmic changes required to extend OptKnock (Burgard et al., 2003) to allow for up- and/or downregulation in addition to gene knock-outs to meet a bioproduction goal.”
- Flux balance analysis in the era of metabolomics (2006, background): “The bi-level formulation for inverse optimization problems was extended further to identify gene deletion strategies for the overproduction of a desired metabolite (OptKnock) [38].”
- Systems biotechnology for strain improvement. (2005, neutral): “For example, on the basis of constraints-based flux analysis, gene knockout targets of recombinant E. coli can be identified by means of linear programming [26], mixed-integer optimization [28], quadratic programming [29] and bilevel optimization [ 30 ].”
- Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “Optimization methods based on genetic (Patil et al ., 2005) or linear programming (Burgard et al ., 2003) algorithms were introduced to circumvent this issue.”