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Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth
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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From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [57]
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“Furthermore, as metabolic networks function within the context of biochemical environments and interact with these environments by taking up or secreting various organic and inorganic compounds, previous studies have also addressed the effect that these environmental interactions have on the metabolic process, as mani…”
BackgroundNonoptimal microbial response to antibiotics underlies suppressive drug interactions
Bollenbach, T., Quan, S., Chait, R., & Kishony, R. (2009) · Cell · doi:10.1016/j.cell.2009.10.025 · Cited by 227
“It has been argued that many aspects of bacterial physiology have evolved to be 'optimal' -namely to maximize growth rate in a given condition (Dekel and Alon, 2005; Ibarra et al., 2002; Liebermeister et al., 2004).”
BackgroundJensen, P. A., & Papin, J. A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq702 · Cited by 222
Advances in combating fungal diseases: vaccines on the threshold
Cutler, J. E., Deepe, G. S., & Klein, B. S. (2006) · Nature Reviews Microbiology · doi:10.1038/nrmicro1537 · Cited by 223
Soupène, E., van Heeswijk, W. C., PLUMBRIDGE, J. A., Stewart, V. J., Bertenthal, D., Lee, H., Gyaneshwar, P., Paliy, O., Charernnoppakul, P., & Kustu, S. G. (2003) · Journal of Bacteriology · doi:10.1128/jb.185.18.5611-5626.2003 · Cited by 216
“ilvG Growth inhibited by valine (31) rfb-50 Sensitive to phage P1 rph-1 Partial pyrimidine requirement fnr-267 Defects in anaerobic respirations eut Unable to grow on ethanolamine as the carbon or nitrogen source ? Slow growth on galactose ? Slow growth on glycerol (24) ? Poor motility (32)”
BackgroundA metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)
Dukovski, I., Bajić, D., Chacón, J. M., Quintin, M., Vila, J. C. C., Sulheim, S., Pacheco, A. R., Bernstein, D. B., Riehl, W. J., Korolev, K. S., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00593-3 · Cited by 234
Expanding the metabolic engineering toolbox: more options to engineer cells
Tyo, K. E. J., Alper, H. S., & Stephanopoulos, G. N. (2007) · Trends in biotechnology · doi:10.1016/j.tibtech.2007.01.003 · Cited by 215
“Although these models are being developed for simpler organisms, such as bacteria and yeast [ 33 ,34], they can be expanded to higher cellular systems [35] .A nE. coli metabolic model has recently been successful in predicting a triple gene knockout for improving lycopene production that would have been intractable to…”
MethodGrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions
Kumar, V. S., & Maranas, C. D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000308 · Cited by 212
“, presence or absence) of single and/ or multiple knockout mutants for a variety of substrates against experimental data [5–7].”
BackgroundGenome-based design of a cell-free culture medium for Tropheryma whipplei
Renesto, P., Crapoulet, N., Ogata, H., Scola, B. L., Vestris, G., Claverie, J., & Raoult, D. (2003) · The Lancet · doi:10.1016/s0140-6736(03)14071-8 · Cited by 203
Reed, J. L., & Palsson, B. Ø. (2004) · Genome Research · doi:10.1101/gr.2546004 · Cited by 211
Self-citationIntegration of gene expression data into genome-scale metabolic models
Åkesson, M., Förster, J., & Nielsen, J. (2004) · Metabolic Engineering · doi:10.1016/j.ymben.2003.12.002 · Cited by 204
Plasticity of the cis-Regulatory Input Function of a Gene
Mayo, A., Setty, Y., Shavit, S., Zaslaver, A., & Alon, U. S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040045 · Cited by 200
“This question may also be further studied experimentally [22,23,25,54,55] by evolving bacteria in defined environments that favor different input functions.”
BackgroundThe genome sequence of the capnophilic rumen bacterium Mannheimia succiniciproducens
Hong, S. H., Kim, J. S., Lee, S. Y., In, Y. H., Choi, S. S., Rih, J.-K., Kim, C., Jeong, H., Hur, C. G., & Kim, J. J. (2004) · Nature Biotechnology · doi:10.1038/nbt1010 · Cited by 204
Impact of gene expression noise on organismal fitness and the efficacy of natural selection
Wang, Z., & Zhang, J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1100059108 · Cited by 194
Metagrowth: a new resource for the building of metabolic hypotheses in microbiology
Ogata, H. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki042 · Cited by 197
Fong, S. S., Nanchen, A., Palsson, B. Ø., & Sauer, U. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m510016200 · Cited by 189
Self-citationHeckmann, D., Schulze, S., Denton, A. K., Gowik, U., Westhoff, P., Weber, A. P. M., & Lercher, M. J. (2013) · Cell · doi:10.1016/j.cell.2013.04.058 · Cited by 182
Is maximization of molar yield in metabolic networks favoured by evolution?
Schuster, S., Pfeiffer, T., & Fell, D. A. (2007) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2007.12.008 · Cited by 184
“…systems of increasing complexity and coined the term ''flux balance analysis'' (Varma and Palsson, 1993a, b, 1994; Edwards and Palsson, 1999; Edwards et al., 2001, 2002; Ibarra et al., 2002; Fong and ARTICLE IN PRESS www.elsevier.com/locate/yjtbi 0022-5193/$ -see front matter r 2007 Elsevier Ltd.”
BackgroundBioLogic gates enable logical transcription control in mammalian cells
Kramer, B. P., Fischer, C., & Fussenegger, M. (2004) · Biotechnology and Bioengineering · doi:10.1002/bit.20142 · Cited by 185
“For optimal molecular interventions in complex regulatory networks orchestrating the cellular transcriptome in space and time (Ibarra et al., 2002; Aebersold and Mann, 2003), we have constructed a variety of switchboard-type transgene control units.”
MethodFormulating genome‐scale kinetic models in the post‐genome era
Jamshidi, N., & Palsson, B. Ø. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.8 · Cited by 180
“It has thus been used productively for the analysis of distal causation (Ibarra et al , 2002; Fong and Palsson, 2004; Pal et al , 2006; Harrison et al , 2007).”
MethodSelf-citationFu, W., Gudmundsson, O., Feist, A. M., Herjolfsson, G., Brynjolfsson, S., & Palsson, B. Ø. (2012) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2012.07.004 · Cited by 170
“…ALE indicate that C. vulgaris is capable of adapting to different conditions, as has been observed with other organisms (Applebee et al., 2008; Babu and Aravind, 2006; Ibarra et al., 2002; Teusink et al., 2009), and manages to do so by altering its ratio of chlorophyll a to chlorophyll b.”
Self-citationConstrained Allocation Flux Balance Analysis
Mori, M., Hwa, T., Martin, O., Martino, A. D., & Marinari, E. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004913 · Cited by 173
Use of genome-scale microbial models for metabolic engineering
Patil, K. R., Åkesson, M., & Nielsen, J. (2003) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2003.11.003 · Cited by 175
Saa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166
“These methods have generated fundamental biological insights into the operation of metabolic networks (Ibarra et al., 2002; Schuetz et al., 2012) as well as great advances in biotechnological applications (Lee and Kim, 2015; Yim et al., 2011).”
MethodMathematical models in microbial systems biology
Stelling, J. (2004) · Current Opinion in Microbiology · doi:10.1016/j.mib.2004.08.004 · Cited by 164
Campodonico, M. A., Andrews, B. A., Asenjo, J. A., Palsson, B. Ø., & Feist, A. M. (2014) · Metabolic Engineering · doi:10.1016/j.ymben.2014.07.009 · Cited by 162
“…together with COBRA methods and organism-specific models has proven successful for the calculation of wild type E. coli optimal growth rates (Ibarra et al., 2002), native E. coli metabolite production through knock-outs (Fong et al., 2005), and for non-native E. coli metabolite production…”
MethodSelf-citation
Metadata
- 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
- Published
- 2002-11
- Volume
- 420
- Issue
- 6912
- Pages
- 186-189
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- 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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