Cited by 857Microbial Metabolic Engineering and BioproductionEvolution and Genetic DynamicsGene Regulatory Network Analysis

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

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

DOI 10.1038/nature01149

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Abstract

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.

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The complete genome sequence of Escherichia coli K-12.
Blattner, F., Plunkett, G., Bloch, C., Perna, N., Burland, V., Riley, M., Collado-Vides, J., Glasner, J. D., Rode, C., Mayhew, G., et al. (1997) · Science · doi:10.1126/science.277.5331.1453 · Cited by 7,996
Understanding the control of metabolism
(1997) · Choice Reviews Online · doi:10.5860/choice.34-6252 · Cited by 1,230
Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110
Varma, A., & Palsson, B. Ø. (1994) · Applied and Environmental Microbiology · doi:10.1128/aem.60.10.3724-3731.1994 · Cited by 1,058
Self-citation
A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks
Schuster, S., Fell, D. A., & Dandekar, T. (2000) · Nature Biotechnology · doi:10.1038/73786 · Cited by 947
Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use
Varma, A., & Palsson, B. Ø. (1994) · Nature Biotechnology · doi:10.1038/nbt1094-994 · Cited by 945
Self-citation
In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data
Edwards, J. S., Ibarra, R. U., & Palsson, B. Ø. (2001) · Nature Biotechnology · doi:10.1038/84379 · Cited by 941
Self-citation
The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
Edwards, J. S., & Palsson, B. O. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5528 · Cited by 875
Self-citation
E-CELL: software environment for whole-cell simulation
Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T. S., Matsuzaki, Y., Miyoshi, F., Saito, K., Tanida, S., Yugi, K., Venter, J. C., et al. (1999) · Bioinformatics · doi:10.1093/bioinformatics/15.1.72 · Cited by 764
The EcoCyc Database
Karp, P. D. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.56 · Cited by 453
WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction
Overbeek, R. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.123 · Cited by 386

Show all 23 references

Research works printed but not linked: 1

Edwards, J. S., Ramakrishna, R., Schilling, C. H. & Palsson, B. O. Metabolic Engineering (eds Lee, S. Y. and Papoutsakis, E. T.) (Marcel Dekker, New York, 1999)

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Kovács, K. (2013) · SZTE Repository of Dissertations (University of Szeged) · doi:10.14232/phd.1668
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Notebaart, R. A. (2009) · Radboud Repository (Radboud University)
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Caviedes, G. P. (2013) · doi:10.7916/d80c534r
“Flux balance analysis (FBA), in particular, has been used to accurately predict gene and nutrient essentiality [2, 76, 77], microbial growth rates [78], metabolic flux distributions [79, 80], and evolutionary adaptations to environmental and genetic perturbations [81-83].”
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“coli under few minimal media have been shown to have good agreement with experimentally measured values [55,56].”
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Shimizu, H., Shinfuku, Y., Sono, M., Furusawa, C., & Hirasawa, T. (2008) · International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS) · doi:10.4108/icst.bionetics2008.4704
“It has been shown that the metabolic profiles calculated by maximization of biomass production can well describe those obtained experimentally in a number of organisms and environmental conditions, suggesting capability of organisms to maximize their growth rate by adaptation and evolution [11, 12].”
Background
Adaptive evolution: E. coli's learning curve
Louis, J. C. (2003) · BIOSILICO · doi:10.1016/s1478-5382(03)02287-x
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Furusawa, C., Kaneko, K., & Shimizu, H. (2008) · International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS) · doi:10.4108/icst.bionetics2008.4722
“This study provides a possible explanation for the establishment of the optimal growth rate in metabolic reaction networks, proposed by Palsson and colleagues[2, 7, 4].”
Background
Methods for analysis of evolutive adaptation of E. coli
Feuer, R., Ederer, M., Trachtmann, N., Sauter, T., Gilles, E. D., Sprenger, G. A., & Sawodny, O. (2007) · SICE Annual Conference (SICE) · doi:10.1109/sice.2007.4421194
“coli strains show an evolution to the optimal achievable efficiency [3], [4].”
Background
Genome-Scale Models and the Genetic Basis for E. coli Adaptation
Applebee, M. K., & Palsson, B. Ø. (2009) · Springer eBooks · doi:10.1007/978-1-4020-9394-4_12
Self-citation
Oral health report.
Bartold, P. M. (2001) · PubMed
Flux balance analysis to model microbial metabolism for electricity generation
Mao, L. (2013) · Lincoln University Research Archive (Lincoln University)
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Hietpas, R. T. (2013) · eScholarship@UMassChan · doi:10.13028/m2kk6j
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DOPICO, J. T. (2014) · doi:10.4995/thesis/10251/39351
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Background
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Yang, H. (2010) · doi:10.7282/t35m65vd
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Background
Metabolische Oszillationen in Hefe
Fakultät, L. (2014)
THÈSE / UNIVERSITÉ DE RENNES 1 sous le sceau de l'Université Européenne de Bretagne pour le grade de DOCTEUR DE L'UNIVERSITÉ DE RENNES 1 Mention : Informatique
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Thompson, R. A. (2016)
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Monte, E., Rosa‐Garrido, M., Vondriska, T. M., & Wang, J. (2016) · Comprehensive physiology · doi:10.1002/cphy.c160003
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Mori, M. (2013)
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Background
Systems Analysis of the Cardiac Myocyte Hypertrophy Signaling Network
Ryall, K. A. (2014) · doi:10.18130/v30r65

Authors: Ibarra, R. U., Edwards, J. S., & Palsson, B. O.
Affiliations: University of California San Diego (US)
University of Delaware (US)
ORCID: Edwards, Jeremy S.
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2002-11
Volume: 420
Issue: 6912
Pages: 186-189
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature01149
OpenAlex: W2025663053
Cited by: 857
References: 23
Citations by year: 2026: 11 · 2025: 11 · 2024: 22 · 2023: 21 · 2022: 17 · 2021: 27 · 2020: 23 · 2019: 29 · 2018: 34 · 2017: 42 · 2016: 44 · 2015: 34 · 2014: 46 · 2013: 41 · 2012: 51
FWCI: 12.89
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Science Foundation
U.S. Department of Energy
Istituto Superiore di Sanità
National Institutes of Health
Biological and Environmental Research
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords: Escherichia coli K-12, adaptive evolution, optimal growth rate, glycerol metabolism, constraint-based metabolic modeling, in silico prediction, metabolic network evolution, carbon source utilization
MeSH terms: Adaptation, Physiological, Carbon, Computer Simulation, Escherichia coli, Biological Evolution, Glucose, Glycerol, Kinetics, Malates, Models, Biological, Phenotype, Selection, Genetic, Genome, Bacterial, Computational Biology, Acetic Acid, Succinic Acid, Genomics

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

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