Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

By Rafael U. Ibarra, Jeremy S. Edwards, Bernhard O. Palsson. Nature, 2002

875 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4f1f8c6c4c6b85604d1efbb767250cbde599b7ea

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Citations in the corpus

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, background): “Although the validity of this assumption has been experimentally confirmed under some conditions [51], there are cases (such as overflow metabolism) where this assumption is not valid.”
Flux coupling analysis of genome-scale metabolic network reconstructions. (2004, background): “…secretion under increasingly anaerobic conditions (Varma et al. 1993); (3) quantitatively predicting cellular growth rates (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…”
Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli (2003, methodology): “It has also been found that some carbon sources do no initially support optimal behavior, although limited adaptive evolution data do suggest that over time E. coli adjusts its metabolic ﬂuxes to ﬁnd the optimal solution (26; S. S. Fong and B. O. Palsson, unpublished data).”
Microbial Propionic Acid Production (2017, background): “Energy maximisation is consistent with the evolutionary drive to maximise growth, which can approximate the behavior of microorganisms [35].”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, background): “…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 , 2009) to examine the driving…”
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).”