The DOI matches its Crossref record; 22 of the 23 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Whereas small-scale cultivations and initial engineering steps are often performed in complex media in batch culture, during the scale-up of the process, cells are exposed to altered environmental conditions and environmental stress regimes [57,58].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [57]
Citations
Cites
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
Varma, A., & Palsson, B. Ø. (1994) · Applied and Environmental Microbiology · doi:10.1128/aem.60.10.3724-3731.1994 · Cited by 1,058
Self-citationSchuster, 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-citationEdwards, J. S., Ibarra, R. U., & Palsson, B. Ø. (2001) · Nature Biotechnology · doi:10.1038/84379 · Cited by 941
Self-citationEdwards, J. S., & Palsson, B. O. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5528 · Cited by 875
Self-citationE-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
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
Flagged first, then by citations
1 of the 23 references couldn’t be matched to a known paper.
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)
Cited by
Sorted by citations
Items 201–300 of 856
Benedict, M. N., Mundy, M. B., Henry, C. S., Chia, N., & Price, N. D. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003882 · Cited by 87
“GEMs have been utilized broadly [2,3,4,5] across all three domains of life [6] to accelerate research in such areas as network evolution [7,8,9], synthetic biology [10,11], and the discovery of novel drug targets [12].”
MethodYoung, J. D., Henne, K. L., Morgan, J. A., Konopka, A. E., & Ramkrishna, D. (2008) · Biotechnology and Bioengineering · doi:10.1002/bit.21780 · Cited by 84
Evolutionary engineering to enhance starter culture performance in food fermentations
Bachmann, H., Pronk, J. T., Kleerebezem, M., & Teusink, B. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.09.003 · Cited by 83
“As an optimization problem, it has the potential to predict evolutionary outcomes [18], where analysis of specific mutations identifies the molecular mechanisms leading to improved fitness.”
BackgroundFleming, R. M. T., Thiele, I., & Nasheuer, H. (2009) · Biophysical Chemistry · doi:10.1016/j.bpc.2009.08.007 · Cited by 84
“There have been many practical biological uses of constraint-based models, including study of bacterial evolution [3], analysis of network properties [4,5,6,7], study of phenotypic behavior [8,9], biological discovery [10,11,12], and metabolic engineering [13,14,15].”
BackgroundMachado, D., Herrgård, M. J., & Rocha, I. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005140 · Cited by 82
“The maximization of rate of biomass formation has shown agreement with experimental observations [16] and is often used.”
ResultStoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae
Jin, Y., & Jeffries, T. W. (2004) · Metabolic Engineering · doi:10.1016/j.ymben.2003.11.006 · Cited by 85
“As a similar case, Ibarra et al. (2002) showed that wild type E. coli grew sub-optimally on glycerol but its growth rate approached the optimal growth rate predicted in silico model after serial subcultures on glycerol.”
ResultHanemaaijer, M., Röling, W. F. M., Olivier, B. G., Khandelwal, R. A., Teusink, B., & Bruggeman, F. J. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00213 · Cited by 79
“…2006; Teusink et al., 2006; Oh et al., 2007 Simulations in correspondence with 13 C-tracer studies Sun et al., 2009 Non-optimal growth of strains Ibarra et al., 2002; Teusink et al., 2009 Correct gene knock-out strategies for higher product yields Bro et al., 2006; Pharkya and Maranas, 2006;…”
MethodTopological Signatures of Species Interactions in Metabolic Networks
Borenstein, E., & Feldman, M. W. (2009) · Journal of Computational Biology · doi:10.1089/cmb.2008.06tt · Cited by 82
“Formally, a directed edge from compound a to compound b indicates that compound a is a substrate in some reaction which produces compound b (i.e., for each given reaction, all the nodes that represent its substrates are connected by directed edges to all the nodes that represent its products).”
Abundant Indispensable Redundancies in Cellular Metabolic Networks
Wang, Z., & Zhang, J. (2009) · Genome Biology and Evolution · doi:10.1093/gbe/evp002 · Cited by 79
“Based on the stoichiometric associations among metabolites, the computational methods provide reliable quantitative predictions of metabolic function and Darwinian fitness under genetic and environmental perturbations (Edwards et al. 2001; Ibarra et al. 2002; Segre et al. 2002; Famili et al. 2003; Papp et al. 2004), t…”
BackgroundMethodA framework for evolutionary systems biology
Loewe, L. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-27 · Cited by 77
Homo-succinic acid production by metabolically engineered Mannheimia succiniciproducens
Lee, J. W., Yi, J., Kim, T. Y., Choi, S., Ahn, J. H., Song, H., Lee, M., & Lee, S. Y. (2016) · Metabolic Engineering · doi:10.1016/j.ymben.2016.10.004 · Cited by 78
Nullspace Approach to Determine the Elementary Modes of Chemical Reaction Systems
Wagner, C. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp034523f · Cited by 78
Fisher’s Geometrical Model Emerges as a Property of Complex Integrated Phenotypic Networks
Martin, G. (2014) · Genetics · doi:10.1534/genetics.113.160325 · Cited by 73
“…a wide variety of gene deletions (alone or in combination) in several model microbial species, mostly the bacterium Escherichia coli (Ibarra et al. 2002) and the yeast Saccharomyces Copyright © 2014 by the Genetics Society of America doi: 10.1534/genetics.113.160325 Manuscript received December 1,…”
BackgroundInfluentialiRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network
Imam, S., Yılmaz, Ş., Sohmen, U., Gorzalski, A. S., Reed, J. L., Noguera, D. R., & Donohue, T. J. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-116 · Cited by 78
“Nevertheless, the predicted growth rates are closer to experimental observations than results previously seen in some other organisms [45,46], suggesting R.”
ResultVallino, J. J. (2010) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2009.0272 · Cited by 79
“Between 0 and 1 there is an optimum 1 M that maximizes methanotroph productivity, which is selected for by evolution (Ibarra et al. 2002), but this value does not maximize entropy production because the methane that contributed to methanotroph biomass could have been oxidized.”
An analytical theory of balanced cellular growth
Dourado, H., & Lercher, M. J. (2020) · Nature Communications · doi:10.1038/s41467-020-14751-w · Cited by 82
Suboptimal resource allocation in changing environments constrains response and growth in bacteria
Balakrishnan, R., de Silva, R. T., Hwa, T., & Cremer, J. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110597 · Cited by 72
“…of hundreds of genes (Chang et al , 2002; Kao et al , 2005; Mostovenko et al , 2011), and implicating major regulators such as cAMP (Loomis & Maga-sanik, 1967; Ullmann & Monod, 1968; Inada et al , 1996; Kimata et al , 1997) and ppGpp (Traxler et al , 2006; Fern (cid:1) andez-Coll & Cashel, 2018).”
Basic concepts and principles of stoichiometric modeling of metabolic networks
Maarleveld, T. R., Khandelwal, R. A., Olivier, B. G., Teusink, B., & Bruggeman, F. J. (2013) · Biotechnology Journal · doi:10.1002/biot.201200291 · Cited by 75
“These applications to biology are the reasons for the existence of pathway analysis, and there are a number of success stories [34, 35].”
BackgroundOptimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks
Kelk, S. M., Olivier, B. G., Stougie, L., & Bruggeman, F. J. (2012) · Scientific Reports · doi:10.1038/srep00580 · Cited by 77
Evolutionary Remodeling of Bacterial Motility Checkpoint Control
Ni, B., Ghosh, B., Páldy, F. S., Colin, R., Heimerl, T., & Sourjik, V. (2017) · Cell Reports · doi:10.1016/j.celrep.2016.12.088 · Cited by 74
An integrative approach towards completing genome-scale metabolic networks
Christian, N., May, P., Kempa, S., Handorf, T., & Ebenhöh, O. (2009) · Molecular BioSystems · doi:10.1039/b915913b · Cited by 74
Harcombe, W. R., Delaney, N. F., Leiby, N., Klitgord, N., & Marx, C. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003091 · Cited by 75
Metabolic Network Model of a Human Oral Pathogen
Mazumdar, V., Snitkin, E. S., Amar, S., & Segrè, D. (2008) · Journal of Bacteriology · doi:10.1128/jb.01123-08 · Cited by 77
“reflect experimental data (22, 34, 79, 88), the FBA approach is increasingly used for various 23 AC CE PT ED”
MethodMori, M., Marinari, E., & Martino, A. D. (2019) · npj Systems Biology and Applications · doi:10.1038/s41540-019-0093-4 · Cited by 73
Cellular metabolomics ofEscherchia coli
Rabinowitz, J. D. (2007) · Expert Review of Proteomics · doi:10.1586/14789450.4.2.187 · Cited by 72
Genetic Redundancies and Their Evolutionary Maintenance
Zhang, J. (2012) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-3567-9_13 · Cited by 70
A genome-scale, constraint-based approach to systems biology of human metabolism
Mo, M. L., Jamshidi, N., & Palsson, B. Ø. (2007) · Molecular BioSystems · doi:10.1039/b705597h · Cited by 71
Self-citationBiochemical and statistical network models for systems biology
Price, N. D., & Shmulevich, I. (2007) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2007.07.009 · Cited by 69
“This constraint-based modeling procedure has been successfully utilized to study phenotypes in various model [15–18] and infectious [19,20] microorganisms.”
MethodWichuk, K., Brynjólfsson, S., & Fu, W. (2014) · Bioengineered · doi:10.4161/bioe.28720 · Cited by 70
“206 Bioengineered Volume 5 Issue 3 biomass yields.(15,16) Adaptive laboratory evolution (ALE) has been widely utilized as a tool for developing new biological and phenotypic functions and exploring strain improvement in synthetic biology for bacteria.”
Global epistasis on fitness landscapes
Díaz‐Colunga, J., Skwara, A., Gowda, K., Díaz‐Uriarte, R., Tikhonov, M., Bajić, D., & Sánchez, Á. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0053 · Cited by 71
“Models based on FBA have seen impressive success in predicting metabolic trait values to which adaptation would converge [26], and they have been shown to qualitatively capture the shape of metabolic fitness landscapes in model organisms such as Escherichia coli [27–30].”
Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism
Goldford, J. E., Hartman, H., Marsland, R., & Segrè, D. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-1018-8 · Cited by 70
Genome data mining of lactic acid bacteria: the impact of bioinformatics
Siezen, R. J., van Enckevort, F. H. J., Kleerebezem, M., & Teusink, B. (2004) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2004.02.002 · Cited by 68
“Such genome-scale metabolic network analyses can predict maximal yields [41], regulation sites [42 ], and optimal flux distributions [43 ].”
BackgroundGroßkopf, T., Consuegra, J., Gaffé, J., Willison, J. C., Lenski, R. E., Soyer, O. S., & Schneider, D. (2016) · BMC Evolutionary Biology · doi:10.1186/s12862-016-0733-x · Cited by 69
“Early applications of FBA ignored the essential role of tradeoffs in the computation of metabolic fluxes [28, 31, 32], but more recent applications have incorporated tradeoffs as constraints on total fluxes [18, 19, 33, 34] and thereby achieved better prediction of experimentally observed metabolic…”
BackgroundGottlieb, K., Albermann, C., & Sprenger, G. A. (2014) · Microbial Cell Factories · doi:10.1186/s12934-014-0096-1 · Cited by 67
“55 h [11,13].”
Metabolic shifts: a fitness perspective for microbial cell factories
Goel, A., Wortel, M. T., Molenaar, D., & Teusink, B. (2012) · Biotechnology Letters · doi:10.1007/s10529-012-1038-9 · Cited by 68
“coli evolves towards an in silico predicted ‘‘line of optimality’’ on glycerol, but, on glucose, the evolved cells increased their growth rate but moved away from the FBApredicted line of optimality by producing acetate (Ibarra et al. 2002).”
BackgroundHarmand, N., Gallet, R., Jabbour‐Zahab, R., Martin, G., & Lenormand, T. (2016) · Evolution · doi:10.1111/evo.13111 · Cited by 66
Viability and membrane potential analysis of Bacillus megaterium cells by impedance flow cytometry
David, F., Hebeisen, M., Schade, G., Franco‐Lara, E., & Berardino, M. D. (2011) · Biotechnology and Bioengineering · doi:10.1002/bit.23345 · Cited by 66
Greenblum, S. I., Chiu, H.-C., Levy, R., Carr, R., & Borenstein, E. (2013) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2013.04.001 · Cited by 66
“Although constraint-based methods have been shown to appropriately predict cellular metabolism [67,68] and”
Functional stoichiometric analysis of metabolic networks
Urbanczik, R., & Wagner, C. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti674 · Cited by 64
“A version of this metabolic model consisting of 99 reactions, with 11 external compounds (5 input, 5 output and 1 input/output), was analyzed in Klamt and Stelling (2002) and it was found that an excess of 5 · 10(5) elementary fluxes are needed for a complete set.”
MethodMetabolic complexity drives divergence in microbial communities
Silverstein, M. R., Bhatnagar, J. M., & Segrè, D. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02440-6 · Cited by 65
Network News: Innovations in 21st Century Systems Biology
Arkin, A. P., & Schaffer, D. V. (2011) · Cell · doi:10.1016/j.cell.2011.03.008 · Cited by 66
“Ibarra et al. (2002) created a constraints-based 848 Cell 144, March 18, 2011 ª2011 Elsevier whole-cell metabolic model for E. coli, in which stoichiometric, thermodynamic, and other constraints mathematically yielded a solution space of allowed metabolic network states (Ibarra et al., 2002).”
BackgroundPOPULATION SUBDIVISION AND ADAPTATION IN ASEXUAL POPULATIONS OF SACCHAROMYCES CEREVISIAE
Kryazhimskiy, S., Rice, D. P., & Desai, M. M. (2012) · Evolution · doi:10.1111/j.1558-5646.2011.01569.x · Cited by 65
From Annotated Genomes to Metabolic Flux Models and Kinetic Parameter Fitting
Segrè, D., Zucker, J., Katz, J., Lin, X. N., D’haeseleer, P., Rindone, W. P., Kharchenko, P. V., Nguyen, D., Wright, M. A., & Church, G. M. (2003) · OMICS A Journal of Integrative Biology · doi:10.1089/153623103322452413 · Cited by 65
Functional and Metabolic Effects of Adaptive Glycerol Kinase (GLPK) Mutants in Escherichia coli
Applebee, M. K., Joyce, A. R., Conrad, T. M., Pettigrew, D. W., & Palsson, B. Ø. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.195305 · Cited by 64
Self-citationZanghellini, J., Natter, K., Jungreuthmayer, C., Thalhammer, A., Kurat, C. F., Gogg‐Fassolter, G., Kohlwein, S. D., & von Grünberg, H. (2008) · FEBS Journal · doi:10.1111/j.1742-4658.2008.06681.x · Cited by 63
“This successive calibration sets our approach apart from conventional FBA and DFBA implementations [9–13].”
MethodResultProspects for the Future Using Genomics and Proteomics in Clinical Microbiology
Fournier, P., & Raoult, D. (2010) · Annual Review of Microbiology · doi:10.1146/annurev-micro-090110-102922 · Cited by 62
The Systems Biology of Glycosylation
Murrell, M. P., Yarema, K. J., & Levchenko, A. (2004) · ChemBioChem · doi:10.1002/cbic.200400143 · Cited by 63
OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling
Shen, F., Sun, R., Yao, J., Li, J., Liu, Q., Price, N. D., Liu, C., & Wang, Z. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006835 · Cited by 60
Systems biology of lactic acid bacteria: a critical review
Teusink, B., Bachmann, H., & Molenaar, D. (2011) · Microbial Cell Factories · doi:10.1186/1475-2859-10-s1-s11 · Cited by 62
“[74] where it was shown that during serial transfer E.”
BackgroundA Practical Guide to Genome-Scale Metabolic Models and Their Analysis
dos Santos, F. B., Boele, J., & Teusink, B. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00024-4 · Cited by 64
Achieving Diversity in the Face of Constraints: Lessons from Metabolism
Milo, R., & Last, R. L. (2012) · Science · doi:10.1126/science.1217665 · Cited by 64
Dynamic analysis of optimality in myocardial energy metabolism under normal and ischemic conditions
Luo, R., Liao, S., Tao, G., Li, Y., Zeng, S., Li, Y., & Luo, Q. (2006) · Molecular Systems Biology · doi:10.1038/msb4100071 · Cited by 65
“Able to model an optimal systemic state based on stoichiometry by integrating a restricted set of parameters in a system, it has been widely used to rebuild and analyze behaviors of semicharacterized metabolic networks (Edwards and Palsson, 2000; Covert et al, 2001; Ibarra et al, 2002).”
MethodInfluentialThe origins of mutational robustness
Fares, M. A. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.04.008 · Cited by 64
Predicting evolution from genomics: experimental evolution of bacteriophage T7
Bull, J. J., & Molineux, I. J. (2008) · Heredity · doi:10.1038/sj.hdy.6801087 · Cited by 60
“Programs of this sort have already been initiated for simple phenotypes (for example, those involving single genes) and in studies of bacterial metabolism, where a knowledge of molecular pathways paired with metabolic flux theory has yielded unique insights into evolution in response to the nutrients available to the…”
BackgroundMathematical modelling of microbes: metabolism, gene expression and growth
de Jong, H., Casagranda, S., Giordano, N., Eugenio Cinquemani, B., Ropers, D., Geiselmann, J., & Gouzé, J. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0502 · Cited by 65
Integrated analysis of metabolic phenotypes in Saccharomyces cerevisiae
Duarte, N. C., Palsson, B. Ø., & Fu, P. (2004) · BMC Genomics · doi:10.1186/1471-2164-5-63 · Cited by 63
Self-citationA critical view of metabolic network adaptations
Papp, B., Teusink, B., & Notebaart, R. A. (2008) · HFSP journal · doi:10.2976/1.3020599 · Cited by 59
“In a number of studies the flux state of evolved strains was altered to reflect the optimal flux distribution for producing biomass in a predefined environment (Ibarra et al., 2002).”
BackgroundTop-down Phenomics of Arabidopsis thaliana
Tian, C., Chikayama, E., Tsuboi, Y., Kuromori, T., Shinozaki, K., Kikuchi, J., & Hirayama, T. (2007) · Journal of Biological Chemistry · doi:10.1074/jbc.m700549200 · Cited by 60
Metabolic networks evolve towards states of maximum entropy production
Unrean, P., & Srienc, F. (2011) · Metabolic Engineering · doi:10.1016/j.ymben.2011.08.003 · Cited by 60
“A cell with optimal performance is expected to emerge from an evolutionary process in which fittest cells are selected by the given cell environment (Ibarra et al., 2002; Fong et al., 2003; Teusink et al., 2009).”
BackgroundGenotype networks in metabolic reaction spaces
Samal, A., Rodrigues, J. F. M., Jost, J., Martin, O., & Wagner, A. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-30 · Cited by 56
“In practice, regulatory constraints can often be overcome quickly in laboratory evolution experiments [20,21] and are thus temporary rather than fundamental obstacles to cell growth.”
Limitations of entropy maximization in ecology
Haegeman, B., & Loreau, M. (2008) · Oikos · doi:10.1111/j.1600-0706.2008.16539.x · Cited by 59
Statistical mechanics for metabolic networks during steady state growth
Martino, D. D., Andersson, A. M. C., Bergmiller, T., Guet, C. C., & Tkačik, G. (2018) · Nature Communications · doi:10.1038/s41467-018-05417-9 · Cited by 59
Simulations reveal challenges to artificial community selection and possible strategies for success
Xie, L., Yuan, A. E., & Shou, W. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000295 · Cited by 61
“655 For example, several-fold improvement in nutrient affinity and ~20% increase in maximal growth rate 656 have been observed in experimental evolution [32, 30].”
BackgroundA genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database
Weaver, D. S., Keseler, I. M., Mackie, A. M., Paulsen, I. T., & Karp, P. D. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-79 · Cited by 57
“Successive generations of evolution under constant growth conditions might bring the experimental result closer to theory, as described in Ibarra et al. [28].”
MethodSuperessential reactions in metabolic networks
Barve, A., Rodrigues, J. F. M., & Wagner, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1113065109 · Cited by 55
“Mismatches between FBA predictions and experimental data can often be attributed to enzyme misregulation, wherein regulatory constraints prevent enzymes from being expressed at optimal levels (24 – 26).”
BackgroundAdaptation to Optimal Cell Growth through Self-Organized Criticality
Furusawa, C., & Kaneko, K. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.208103 · Cited by 57
Fitness Effects of Mutations in Bacteria
Gordo, I., Perfeito, L., & Sousa, A. (2011) · Microbial Physiology · doi:10.1159/000332747 · Cited by 57
Poker, G., Zarai, Y., Michael Margaliot, A., & Tuller, T. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0713 · Cited by 50
“coli [26], and optimal protein expression levels from the lac operon [14].”
BackgroundIntegrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism
Fleming, R. M. T., Thiele, I., Provan, G. M., & Nasheuer, H. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.02.044 · Cited by 56
Design and Application of Genome-Scale Reconstructed Metabolic Models
Rocha, I., Förster, J., & Nielsen, J. (2008) · Methods in molecular biology · doi:10.1007/978-1-59745-321-9_29 · Cited by 58
“However, it has been demonstrated (38) that under growth selection pressure, the bacteria E.”
BackgroundDevelopment of network-based pathway definitions: the need to analyze real metabolic networks
Palsson, B. Ø., Price, N. D., & Papin, J. A. (2003) · Trends in biotechnology · doi:10.1016/s0167-7799(03)00080-5 · Cited by 56
Self-citationMaharjan, R. P., Seeto, S., & Ferenci, T. (2006) · Journal of Bacteriology · doi:10.1128/jb.01414-06 · Cited by 53
“that computational approaches can explain biological systems (6, 20).”
BackgroundRules for biological regulation based on error minimization
Shinar, G., Dekel, E., Tlusty, T., & Alon, U. S. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506610103 · Cited by 54
Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth
Steuer, R., Knoop, H., & Machné, R. (2012) · Journal of Experimental Botany · doi:10.1093/jxb/ers018 · Cited by 53
“…metabolic network of prokaryotic organisms can usually be assigned a well-defined functionality, namely the synthesis of cellular building blocks to support cellular growth and the provision of ATP and other ‘currency metabolites’ for cellular maintenance (Ibarra et al., 2002; Lewis et al., 2010).”
BackgroundChen, Y., McConnell, B. O., Dhara, V. G., Naik, H. M., Li, C., Antoniewicz, M. R., & Betenbaugh, M. J. (2019) · npj Systems Biology and Applications · doi:10.1038/s41540-019-0103-6 · Cited by 53
Price, N. D., Thiele, I., & Palsson, B. Ø. (2006) · Biophysical Journal · doi:10.1529/biophysj.105.072645 · Cited by 52
“Linear programming has been used for such purposes as predicting the lethality of knockouts (2), optimal growth rates (3), ranges of achievable fluxes (4), and even the endpoint of in vitro evolution (5).”
Self-citationSnitkin, E. S., Dudley, A. M., Janse, D. M., Wong, K., Church, G. M., & Segrè, D. (2008) · Genome biology · doi:10.1186/gb-2008-9-9-r140 · Cited by 55
“Previous studies have indeed found that E. coli grows suboptimally in glycerol, and that the FBA-predicted optimum is achieved only upon several generations of in vitro evolution [ 4 ].”
BackgroundMethodResultInfluentialAuriol, C., Bestel‐Corre, G., Claude, J., Soucaille, P., & Meynial‐Salles, I. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1010431108 · Cited by 51
“Experiments of adaptive evolution have been conducted in the laboratory to understand the behavior of Escherichia coli in response to different environmental or metabolic constraints: (i) wide serial subculturing for over 20 years (more than 40,000 generations) on glucose-limited mineral medium (1), (ii) growth for mo…”
BackgroundWahl, A., Sidorenko, Y., Dauner, M., Genzel, Y., & Reichl, U. (2008) · Biotechnology and Bioengineering · doi:10.1002/bit.21873 · Cited by 51
“Ibarra et al. (2002) predicted the growth behavior of E. coli in glycerol medium and showed that after several generations the system adjusted to the a priori calculated flux distribution.”
Violations of robustness trade‐offs
Kitano, H. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.40 · Cited by 51
“For example, a population of E. coli evolved in the laboratory may quickly arrive at one of the projected positions on the efficient frontier and move along the trajectory of maximum biomass production through evolution (Ibarra et al, 2002) (Trajectory A in Figure 4B).”
BackgroundMethodSong, H., Jaques Reifman, A., & Wallqvist, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112524 · Cited by 52
“coli at balanced growth conditions are close to the theoretical maximum predicted by FBA [36].”
BackgroundMicrofluidic single‐cell analysis links boundary environments and individual microbial phenotypes
Dusny, C., & Schmid, A. (2014) · Environmental Microbiology · doi:10.1111/1462-2920.12667 · Cited by 53
“This implies comprehensive regulation and functional adaptation of the microbial cell on all hierarchical levels, ranging from genome to metabolome (Ibarra et al., 2002; Balaban et al., 2004; Cai et al., 2006).”
BackgroundImproving the sunlight-to-biomass conversion efficiency in microalgal biofactories
Wobbe, L., & Remacle, C. (2014) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2014.08.021 · Cited by 53
An ancient Chinese wisdom for metabolic engineering: Yin-Yang
Wu, S. G., He, L., Wang, Q., & Tang, Y. (2015) · Microbial Cell Factories · doi:10.1186/s12934-015-0219-3 · Cited by 52
“Lastly, the electron transport chain for ATP generation and nutrient transporters may compete for membrane and intracellular spaces so that the capacity of the microbial powerhouse cannot be easily upgraded [13,14].”
BackgroundLewis, N. E., Cho, B., Knight, E. M., & Palsson, B. Ø. (2009) · Journal of Bacteriology · doi:10.1128/jb.00034-09 · Cited by 48
“It has been shown previously that wild-type E. coli does not grow optimally when immediately grown under an alternative growth condition; however, strains grow faster and more efficiently when grown at mid-log phase for extended periods of time (45).”
BackgroundInfluentialSelf-citationSequential Activation of Metabolic Pathways: a Dynamic Optimization Approach
Oyarzún, D. A., Ingalls, B., Middleton, R. H., & Kalamatianos, D. (2009) · Bulletin of Mathematical Biology · doi:10.1007/s11538-009-9427-5 · Cited by 52
“FBA has provided useful predictions of metabolic responses in a number of different organisms under diverse conditions, e.g., Ibarra et al. (2002).”
BackgroundOn the complete determination of biological systems
Selinger, D. W., Wright, M. A., & Church, G. M. (2003) · Trends in biotechnology · doi:10.1016/s0167-7799(03)00113-6 · Cited by 49
“Models of this type have even been shown to be predictive of the biological evolution of metabolic fluxes [ 16 ].”
BackgroundSystems Biology Is Taking OffPreprint
Ehrenberg, M., Elf, J., Aurell, E., Sandberg, R., & Tegnér, J. (2003) · Genome Research · doi:10.1101/gr.1763203 · Cited by 54
“Interestingly, the growth rate of bacterial cells is accounted for in such in silico models and can be maximized by simple linear optimization techniques (Ibarra et al. 2002) and then compared with the growth rate of real bacteria.”
Xu, X., Williams, T. C., Divne, C., Pretorius, I. S., & Paulsen, I. T. (2019) · Biotechnology for Biofuels · doi:10.1186/s13068-019-1427-6 · Cited by 48
Recent advances and challenges in the bioconversion of acetate to value-added chemicals
Mutyala, S., & Kim, J. R. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128064 · Cited by 46
Kacmar, J., Zamamiri, A. M., Carlson, R. P., Abu‐Absi, N. R., & Srienc, F. (2004) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2004.01.003 · Cited by 46
Malatinszky, D., Steuer, R., & Jones, P. R. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.01487 · Cited by 47
Enumerating constrained elementary flux vectors of metabolic networks
Urbanczik, R. (2007) · IET Systems Biology · doi:10.1049/iet-syb:20060073 · Cited by 46
Adaptive evolution by optimizing expression levels in different environments
Babu, M. M., & Aravind, L. (2005) · Trends in Microbiology · doi:10.1016/j.tim.2005.11.005 · Cited by 47
Kim, K., Hou, C. Y., Choe, D., Kang, M., Cho, S., Sung, B. H., Lee, D., Lee, S., Kang, T. J., & Cho, B. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.11.004 · Cited by 48
Irreversibility in biophysical and biochemical engineering
Lucia, U. (2012) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2012.07.018 · Cited by 46
Estimating the size of the solution space of metabolic networks
Braunstein, A., Mulet, R., & Pagnani, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-240 · Cited by 45
“For example, it is well known that under evolutionary pressure, prokaryotes cells like E-Coli behave optimizing their growth performance [5].”
Tzamali, E., Poirazi, P., Tollis, I. G., & Reczko, M. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-167 · Cited by 51
“The most commonly applied objective is the maximization of growth rate reflected in biomass production (Equation 1), which has proved to successfully describe unicellular organisms [34].”
BackgroundJohn, A. J., Samuel, M. S., Govarthanan, M., & Ethiraj, S. (2022) · Environmental Research · doi:10.1016/j.envres.2022.114018 · Cited by 45
Bilevel optimization techniques in computational strain design
Chowdhury, A., Zomorrodi, A. R., & Maranas, C. D. (2014) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2014.06.007 · Cited by 43
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
