In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

By Jeremy S. Edwards, Rafael U. Ibarra, Bernhard O. Palsson. Nature Biotechnology, 2001

1,011 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/221f1d2617d3a0fd33754f07c8f9d4f3a339f5cf

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In Molecular Biology

Citations in the corpus

Evolutionary programming as a platform for in silico metabolic engineering (2005, neutral): “Since several microbial metabolic networks have evolved towards operation of optimal growth rate [15-18], the use optimization of growth rate is an often applied objective function in FBA.”
Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli (2003, methodology): “Growth on M9 minimal medium with acetate, malate, or succinate as the primary carbon source agreed with the computational hypothesis (15, 26); however, glycerol supported only suboptimal growth of E. coli .”
Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic Networks (2001, background): “coli could be correctly predicted when compared to data given in literature [20,21].”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “Using GEMs, FBA has since predicted growth rates 42 , pathway usage 43,44 and the effect of gene expression noise on fitness 45 .”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “Once ﬁtted, and assuming these parameters remain constant across environments, the model can be used to predict growth rates on different media (Edwards et al ., 2001).”
Genome-Scale Metabolic Model of Helicobacter pylori 26695 (2002, methodology): “validated against experimental data (19, 20), here the approach is used primarily”
Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production (2009, methodology): “FBA has also been used to predict the time courses of growth, substrate uptake, and metabolite production by both Escherichia coli and Mycobacterium tuberculosis using a pseudo-steady-state dynamic modeling approach [4–6].”
Shifts in growth strategies reflect tradeoffs in cellular economics (2009, background): “The latter two phenomena are also observed in fast growing tumour cells and cell lines.”
Energy balance for analysis of complex metabolic networks. (2002, background): “As recently implemented with significant success (Bonarius et al., 1997; Edwards et al., 2001; Ramakrishna et al., 2001; Schilling et al., 2001), biological networks are assumed to optimize a certain biologically meaningful objective function, which is a linear combination of the fluxes,”
Metabolite profiling: from diagnostics to systems biology (2004, background): “Both theoretical and experimental disciplines have seen the emergence of systems-based approaches to biology in the past few year”
Constraint-based models predict metabolic and associated cellular functions (2014, background): “Simple constraints include fixing cellular input and output ranges on the basis of uptake and secretion of metabolites 56 , as well as carrying out genetic knockouts by setting the associated bounds of the reactions to zero 24 .”
An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) (2003, methodology): “An objective function with a combination of the metabolic precursors, energy and redox potential required for the production of biomass has proven useful in predicting in vivo cellular behavior [9,10,25,26].”
Dynamic flux balance analysis of diauxic growth in Escherichia coli. (2002, methodology): “The parameters used for the DFBA were the maximal oxygen and the glucose uptake rates (Varma and Palsson, 1994b), the mass transfer coefficient (Edwards et al., 2001), the substrate saturation constant (Wong et al.”