Metabolic gene–deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes

By Stephen S. Fong, Bernhard O. Palsson. Nature Genetics, 2004

314 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fe51bbde261528df0b97bea94a7f81b395f4b747

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Citations in the corpus

Using Genome-Scale Models to Predict Biological Capabilities (2015, unknown): “A total of 78% of strains tested reached the metabolic model predicted optimal growth rate after adaptive laboratory evolution after 40 days of passage (Fong and Palsson, 2004).”
Systems biotechnology for strain improvement. (2005, unknown): “Palsson and colleagues demonstrated the usefulness of constraints-based flux analysis to predict the effects of genetic modifications on cellular metabolic characteristics [ 36 ].”
Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production (2009, methodology): “FBA has been used to successfully predict the metabolic phenotype of gene knockouts [1– 3], and the use of metabolic modeling in this case has the advantage of predicting nutrient-dependent phenotypes.”
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 (2007, unknown): “The Matlab functions to perform batch simulations and to plot the results are: model 1⁄4 readCbModel(‘Ec_iJR904_GlcMM’); model 1⁄4 changeRxnBounds(model, {‘EX_glc(e)’,‘EX_o2(e)’},y [ 10 18], ‘l’); substrateRxns 1⁄4 {‘EX_glc(e)’}; initConcentrations 1⁄4 10; initBiomass 1⁄4 .”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, background): “Constraint-based modeling with GEMs (Palsson, 2006) has allowed researchers to rapidly predict growth phenotypes in various genetic (Fong and Palsson, 2004) and environmental (Ibarra et al , 2002) conditions, explore different objectives of microbial metabolism (Schuetz et al , 2007; Ow et al ,…”
Applications of genome-scale metabolic reconstructions (2009, background): “Some studies have examined the phenomenon of shortterm adaptive evolution, and have shown that as a particular strain adapts to media it is grown in, its growth characteristics will converge toward the FBA-predicted optimal solution (Fong and Palsson, 2004; Fong et al, 2005).”
Network Medicine: A Network-based Approach to Human Disease (2011, background): “Given the relative accuracy of metabolic maps ( Box 1 ) and the ability to predict flux changes that are induced by drug-altered enzymatic activity in bacteria using flux balance analysi”
Modular epistasis in yeast metabolism (2005, methodology): “For the deletion of gene X , fitness was defined as W x = V ΔX growth / V wild–type growth (refs”
Network-based prediction of human tissue-specific metabolism (2008, background): “, and a strong correlation between gene expression and measure”
An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. (2006, background): “In the case of biomass maximization, this leads to, under certain situations , a highly desirable coupling of biomass formation to targeted biochemical production enabling the use of adaptive evolution for strain design (Ibarra et al., 2002; Fong and Palsson, 2004).”
Context-Specific Metabolic Networks Are Consistent with Experiments (2008, methodology): “The gene expression data used to construct the models consists of CEL files containing the data described in [17], normalized using GCRMA [14].”
Genome-scale models of microbial cells: evaluating the consequences of constraints (2004, background): “If a knockout strain is suboptimal,it can be evolved to achieve optimal functions 88 .”