The DOI matches its Crossref record; 135 of the 135 references it cites were found in a registry or an index.
The functional basis of adaptive evolution in chemostats
Open accessAbstract
Two of the central problems in biology are determining the molecular basis of adaptive evolution and understanding how cells regulate their growth. The chemostat is a device for culturing cells that provides great utility in tackling both of these problems: it enables precise control of the selective pressure under which organisms evolve and it facilitates experimental control of cell growth rate. The aim of this review is to synthesize results from studies of the functional basis of adaptive evolution in long-term chemostat selections using Escherichia coli and Saccharomyces cerevisiae. We describe the principle of the chemostat, provide a summary of studies of experimental evolution in chemostats, and use these studies to assess our current understanding of selection in the chemostat. Functional studies of adaptive evolution in chemostats provide a unique means of interrogating the genetic networks that control cell growth, which complements functional genomic approaches and quantitative trait loci (QTL) mapping in natural populations. An integrated approach to the study of adaptive evolution that accounts for both molecular function and evolutionary processes is critical to advancing our understanding of evolution. By renewing efforts to integrate these two research programs, experimental evolution in chemostats is ideally suited to extending the functional synthesis to the study of genetic networks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C. T., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq537 · Cited by 4,835
THE GROWTH OF BACTERIAL CULTURES
Monod, J. (1949) · Annual Review of Microbiology · doi:10.1146/annurev.mi.03.100149.002103 · Cited by 4,643
Diet and the evolution of human amylase gene copy number variation
Perry, G. H., Dominy, N. J., Claw, K. G., Lee, A. S., Fiegler, H., Redon, R., Werner, J., Villanea, F. A., Mountain, J. L., Misra, R., et al. (2007) · Nature Genetics · doi:10.1038/ng2123 · Cited by 1,496
Schaechter, M., MaalOe, O., & Kjeldgaard, N. O. (1958) · Journal of General Microbiology · doi:10.1099/00221287-19-3-592 · Cited by 1,435
Evolution Experiments with Microorganisms: The Dynamics and Genetic Bases of Adaptation
Elena, S. F., & Lenski, R. E. (2003) · Nature Reviews Genetics · doi:10.1038/nrg1088 · Cited by 1,386
Potrykus, K., & Cashel, M. (2008) · Annual Review of Microbiology · doi:10.1146/annurev.micro.62.081307.162903 · Cited by 1,170
Smith, H. L., & Waltman, P. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511530043 · Cited by 990
Effects of Aneuploidy on Cellular Physiology and Cell Division in Haploid Yeast
Torres, E. M., Sokolsky, T., Tucker, C. M., Chan, L. Y., Boselli, M., Dunham, M. J., & Amon, A. (2007) · Science · doi:10.1126/science.1142210 · Cited by 935
Target of Rapamycin (TOR) in Nutrient Signaling and Growth Control
Loewith, R., & Hall, M. N. (2011) · Genetics · doi:10.1534/genetics.111.133363 · Cited by 927
van Opijnen, T., Bodi, K. L., & Camilli, A. (2009) · Nature Methods · doi:10.1038/nmeth.1377 · Cited by 926
Research works printed but not linked: 2
- Bell, 2008, Selection
- Lippman, 2010, Chapter 16 - Systems Biology and TOR: Past, Present, and Future
Cited by
Sorted by citations
Conversion of Escherichia coli to Generate All Biomass Carbon from CO2
Gleizer, S., Ben-Nissan, R., Bar‐On, Y. M., Antonovsky, N., Noor, Ε., Zohar, Y., Jona, G., Krieger, E., Shamshoum, M., Bar‐Even, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.009 · Cited by 528
Adaptive laboratory evolution principles and applications in industrial biotechnology
Mavrommati, M., Daskalaki, A., Papanikolaou, S., & Aggelis, G. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107795 · Cited by 272
Microbial Growth under Limiting Conditions-Future Perspectives
Grau, J. M. G., & Aranda, B. (2023) · Microorganisms · doi:10.3390/microorganisms11071641 · Cited by 215
Butyric acid: Applications and recent advances in its bioproduction
Jiang, L., Fu, H., Yang, H. K., Xu, W., Wang, J., & Yang, S. (2018) · Biotechnology Advances · doi:10.1016/j.biotechadv.2018.09.005 · Cited by 187
Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution
den Bergh, B. V., Swings, T., Fauvart, M., & Michiels, J. J. (2018) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00008-18 · Cited by 200
Implications of microbial adaptation for the assessment of environmental persistence of chemicals
Poursat, B. A. J., van Spanning, R. J. M., de Voogt, P., & Parsons, J. R. (2019) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2019.1607687 · Cited by 132
The enduring utility of continuous culturing in experimental evolution
Gresham, D., & Dunham, M. J. (2014) · Genomics · doi:10.1016/j.ygeno.2014.09.015 · Cited by 129
Self-citationO’Brien, E. J., Utrilla, J., & Palsson, B. Ø. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004998 · Cited by 115
He, H., Höper, R., Dodenhöft, M., Marlière, P., & Bar‐Even, A. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.03.002 · Cited by 115
A Model for Designing Adaptive Laboratory Evolution Experiments
LaCroix, R. A., Palsson, B. Ø., & Feist, A. M. (2017) · Applied and Environmental Microbiology · doi:10.1128/aem.03115-16 · Cited by 103
Experimental Evolution Reveals Favored Adaptive Routes to Cell Aggregation in Yeast
Hope, E. A., Amorosi, C. J., Miller, A. W., Dang, K., Heil, C. S., & Dunham, M. J. (2017) · Genetics · doi:10.1534/genetics.116.198895 · Cited by 90
Automated in vivo enzyme engineering accelerates biocatalyst optimization
Orsi, E., von Borzyskowski, L. S., Noack, S., Nikel, P. I., & Lindner, S. N. (2024) · Nature Communications · doi:10.1038/s41467-024-46574-4 · Cited by 95
Lee, S., Lee, N., Oh, H., & Ahn, C. (2020) · Chemosphere · doi:10.1016/j.chemosphere.2020.127934 · Cited by 75
Evolutionary engineering of industrial microorganisms-strategies and applications
Zhu, Z., Zhang, J., Ji, X., Fang, Z., Wu, Z., Chen, J., & Du, G. (2018) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-018-8937-1 · Cited by 67
Abdul‐Rahman, F., Tranchina, D., & Gresham, D. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab173 · Cited by 66
Self-citationManriquez, B., Muller, D. A. E., & Prigent‐Combaret, C. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.619122 · Cited by 62
Nutrient-Limited Operational Strategies for the Microbial Production of Biochemicals
Rajpurohit, H., & Eiteman, M. A. (2022) · Microorganisms · doi:10.3390/microorganisms10112226 · Cited by 67
Evolutionary engineering of Corynebacterium glutamicum
Stella, R. G., Wiechert, J., Noack, S., & Frunzke, J. (2019) · Biotechnology Journal · doi:10.1002/biot.201800444 · Cited by 55
Engineered citrate synthase alters Acetate Accumulation in Escherichia coli
Tovilla‐Coutiño, D. B., Momany, C., & Eiteman, M. A. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.06.006 · Cited by 56
Kim, S., Kim, Y.-S., Suh, D. H., Lee, C. H., Yoo, S. M., Lee, S. Y., & Yoon, S. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-74606-8 · Cited by 53
Ratib, N. R., Seidl, F., Ehrenreich, I. M., & Finkel, S. E. (2021) · mBio · doi:10.1128/mbio.03337-20 · Cited by 45
Public goods and metabolic strategies
Bachmann, H., Bruggeman, F. J., Molenaar, D., dos Santos, F. B., & Teusink, B. (2016) · Current Opinion in Microbiology · doi:10.1016/j.mib.2016.03.007 · Cited by 50
Evolutionary pressures on microbial metabolic strategies in the chemostat
Wortel, M. T., Bosdriesz, E., Teusink, B., & Bruggeman, F. J. (2016) · Scientific Reports · doi:10.1038/srep29503 · Cited by 47
Advanced continuous cultivation methods for systems microbiology
Adamberg, K., Valgepea, K., & Vilu, R. (2015) · Microbiology · doi:10.1099/mic.0.000146 · Cited by 44
Lourenço, M., Ramiro, R. S., Güleresi, D., Barroso-Batista, J., Xavier, K. B., Gordo, I., & Sousa, A. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006420 · Cited by 44
Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes
Shepelin, D., Hansen, A. S. L., Lennen, R. M., Luo, H., & Herrgård, M. J. (2018) · Genes · doi:10.3390/genes9050249 · Cited by 49
Technological tools and strategies for culturing human gut microbiota in engineered in vitro models
Sardelli, L., Perottoni, S., Tunesi, M., Boeri, L., Fusco, F., Petrini, P., Albani, D., & Giordano, C. (2021) · Biotechnology and Bioengineering · doi:10.1002/bit.27816 · Cited by 43
Dijk, J. V., Flerlage, H., Beijer, S., Slootweg, J. C., & van Wezel, A. P. (2022) · Chemosphere · doi:10.1016/j.chemosphere.2022.134050 · Cited by 42
Anaya, N. M., Faghihzadeh, F., Ganji, N., Bothun, G. D., & Oyanedel-Craver, V. A. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.03.039 · Cited by 41
Peltier, É., Friedrich, A., Schacherer, J., & Marullo, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00683 · Cited by 38
Scarsini, M., Thiriet-Rupert, S., Veidl, B., Mondeguer, F., Hu, H., Marchand, J., & Schoefs, B. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2021.760516 · Cited by 37
Trace Metal Requirements and Interactions in Symbiodinium kawagutii
Rodriguez, I. B., & Ho, T. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00142 · Cited by 37
Evolthon: A community endeavor to evolve lab evolution
Strauss, S. K., Schirman, D., Jona, G., Brooks, A. N., Kunjapur, A. M., Ba, A. N. N., Flint, A., Solt, A., Mershin, A., Dixit, A. C., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000182 · Cited by 35
Models for Gut-Mediated Horizontal Gene Transfer by Bacterial Plasmid Conjugation
Ott, L. C., & Mellata, M. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.891548 · Cited by 32
Accelerated adaptive laboratory evolution: A tool for evolutionary biotechnology
Declerck, L., Bouchon, F., Demeester, W., Guidi, C., & Mey, M. D. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108741 · Cited by 32
Price, C. E., dos Santos, F. B., Hesseling, A., Uusitalo, J. J., Bachmann, H., Benavente, V., Goel, A., Berkhout, J., Bruggeman, F. J., Marrink, . J., et al. (2019) · BMC Evolutionary Biology · doi:10.1186/s12862-018-1331-x · Cited by 29
Min, K., Lehmeier, C., Ballantyne, F., & Billings, S. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.02083 · Cited by 28
İlker, E., & Hinczewski, M. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.238101 · Cited by 26
Pulkkinen, K., Pekkala, N., Ashrafi, R., Hämäläinen, D. M., Nkembeng, A. N., Lipponen, A., Hiltunen, T., Valkonen, J. K., & Taskinen, J. (2018) · FEMS Microbiology Ecology · doi:10.1093/femsec/fiy060 · Cited by 25
An incoherent feedforward loop facilitates adaptive tuning of gene expression
Hong, J., Brandt, N., Abdul‐Rahman, F., Yang, A. W. H., Hughes, T., & Gresham, D. (2018) · eLife · doi:10.7554/elife.32323 · Cited by 24
Self-citationBarraclough, T. G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218692 · Cited by 19
Poursat, B. A. J., van Spanning, R. J. M., Braster, M., Helmus, R., de Voogt, P., & Parsons, J. R. (2019) · Chemosphere · doi:10.1016/j.chemosphere.2019.125102 · Cited by 19
Adaptation of Escherichia coli to long-term batch culture in various rich media
Westphal, L. L., Lau, J., Negro, Z., Moreno, I. Y., Ismail, W. M., Lee, H., Tang, H., Finkel, S. E., & Kram, K. E. (2018) · Research in Microbiology · doi:10.1016/j.resmic.2018.01.003 · Cited by 18
Construction of mini‐chemostats for high‐throughput strain characterization
Bergenholm, D., Liu, G., Hansson, D., & Nielsen, J. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.26931 · Cited by 19
Hays, M., Schwartz, K., Schmidtke, D. T., Aggeli, D., & Sherlock, G. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010747 · Cited by 19
A multi-omics study to boost continuous bolaform sophorolipid production
Dierickx, S., Maes, K., Roelants, S. L. K. W., Pomian, B., Meulebroek, L. V., Maeseneire, S. L. D., Vanhaecke, L., & Soetaert, W. (2021) · New Biotechnology · doi:10.1016/j.nbt.2021.11.002 · Cited by 17
A High-Resolution View of Adaptive Event Dynamics in a Plasmid
Han, M., Arbeithuber, B., Cremona, M. A., DeGiorgio, M., & Nekrutenko, A. (2019) · Genome Biology and Evolution · doi:10.1093/gbe/evz197 · Cited by 17
Evolutionary rescue of phosphomannomutase deficiency in yeast models of human disease
Vignogna, R. C., Allocca, M., Monticelli, M., Norris, J. W., Steet, R., Perlstein, E. O., Andreotti, G., & Lang, G. I. (2022) · eLife · doi:10.7554/elife.79346 · Cited by 15
Allen, J., Torres‐Acosta, M. A., Mohan, N., Lye, G. J., & Ward, J. M. (2022) · Microbial Cell Factories · doi:10.1186/s12934-022-01958-3 · Cited by 15
Spealman, P., De, T., Chuong, J. N., & Gresham, D. (2023) · Journal of Molecular Evolution · doi:10.1007/s00239-023-10102-7 · Cited by 16
Self-citationWright, N. R., Jessop-Fabre, M. M., Sánchez, B. J., Wulff, T., Workman, C. T., Rønnest, N. P., & Sonnenschein, N. (2022) · Applied and Environmental Microbiology · doi:10.1128/aem.02307-21 · Cited by 14
Subpopulations in Strains of Staphylococcus aureus Provide Antibiotic Tolerance
Mashayamombe, M., Carda‐Diéguez, M., Mira, Á., Fitridge, R., Zilm, P. S., & Kidd, S. P. (2023) · Antibiotics · doi:10.3390/antibiotics12020406 · Cited by 13
In search of the pH limit of growth in halo‐alkaliphilic cyanobacteria
Yi, L., Solanki, R., & Strous, M. (2024) · Environmental Microbiology Reports · doi:10.1111/1758-2229.13323 · Cited by 12
Continuous Cultivation of Yarrowia lipolytica: Potential, Challenges, and Case Studies
Kamzolova, S. V. (2024) · Fermentation · doi:10.3390/fermentation10120619 · Cited by 12
Tarquini, S. (2017) · Bulletin of Volcanology · doi:10.1007/s00445-017-1145-5 · Cited by 12
Prakash, O., Waghmare, U., Chauhan, A., & Patil, Y. B. (2025) · Applied and Environmental Microbiology · doi:10.1128/aem.01728-24 · Cited by 11
Population collapse and adaptive rescue during long‐term chemostat fermentation
Rai, N., Huynh, L. N., Kim, M., & Tagkopoulos, I. (2018) · Biotechnology and Bioengineering · doi:10.1002/bit.26898 · Cited by 12
How Many Biochemistries Are Available To Build a Cell?
Acevedo‐Rocha, C. G., & Schulze‐Makuch, D. (2015) · ChemBioChem · doi:10.1002/cbic.201500379 · Cited by 10
Wright, N. R., Rønnest, N. P., & Thykær, J. (2015) · Biotechnology Progress · doi:10.1002/btpr.2184 · Cited by 10
Tan, J. C., Hu, Q., & Scrutton, N. S. (2024) · Microbial Cell Factories · doi:10.1186/s12934-024-02548-1 · Cited by 9
Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats
Wright, N. R., Rønnest, N. P., & Sonnenschein, N. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.579841 · Cited by 8
Kessi‐Pérez, E. I., Ponce, B., Li, J., Molinet, J., Baeza, C., Figueroa, D., Bastías, C., Gaete, M., Liti, G., Díaz‐Barrera, A., et al. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01204 · Cited by 9
Corrêa, T., Godoy, M. G., Bazylinski, D. A., & Abreu, F. D. Á. (2022) · Marine Drugs · doi:10.3390/md20110724 · Cited by 8
The stressostat: A novel approach in adaptive laboratory evolution to improve end-product resistance
Hartono, S., Meijerink, M. F. A., Abee, T., Smid, E. J., & van Mastrigt, O. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.10.010 · Cited by 7
Cheng, Y., Huang, W., Lo, S.-C., Huang, E., Chiang, E. I., Huang, C., & Yang, Y. (2023) · Microorganisms · doi:10.3390/microorganisms11020253 · Cited by 8
A synthetic biology approach for the design of genetic algorithms with bacterial agents
Gargantilla-Becerra, Á., Gutiérrez, M., & Lahoz-Beltrá, R. (2021) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2021.1879072 · Cited by 7
Continuous Bioprocess Development: Methods for Control and Characterization of the Biological System
Neubauer, P. B., & Bournazou, M. N. C. (2017) · Wiley eBooks · doi:10.1002/9783527699902.ch1 · Cited by 5
Synthetic yeast chromosome XI design enables extrachromosomal circular DNA formation on demandPreprint
Blount, B. A., Lu, X., Driessen, M. R. M., Jovicevic, D., Sanchez, M. I., Ciurkot, K., Zhao, Y., Lauer, S. L., McKiernan, R. M., Gowers, G.-O. F., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.15.500197 · Cited by 5
Hansson, E. M., Childs, D. Z., & Beckerman, A. P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0272052 · Cited by 4
Kulkarni, R. S. (2022) · Medical Hypotheses · doi:10.1016/j.mehy.2022.110999 · Cited by 4
Single-cell heterogeneity in ribosome content and the consequences for the growth lawsPreprint
Brettner, L. M., & Geiler‐Samerotte, K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.19.590370 · Cited by 4
Schwartz, K., Kinnersley, M. A., Lindsey, C. R., Sherlock, G. J., & Rosenzweig, F. (2025) · BMC Biology · doi:10.1186/s12915-025-02331-7 · Cited by 4
Suppressing evolution in genetically engineered systems through repeated supplementation
Layman, N. C., Tuschhoff, B. M., Basinski, A. J., Remien, C. H., Bull, J. J., & Nuismer, S. L. (2020) · Evolutionary Applications · doi:10.1111/eva.13119 · Cited by 2
Metabolic trade-offs in response to increased acetate metabolism in Escherichia coliPreprint
Pentz, J. T., Koehler, S. I., Middlebrook, E. A., Mak, K. Y., Hovde, B. T., & Hanschen, E. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.20.624476 · Cited by 3
Segmental copy number amplifications are more stable than aneuploidies in the absence of selection
De, T., Nun, N. B., Spealman, P., Suresh, I., Avecilla, G., Abdul‐Rahman, F., Ram, Y., & Gresham, D. (2026) · Molecular Biology and Evolution · doi:10.1093/molbev/msag095 · Cited by 2
Self-citationFitness tracking for adapting populations
Gresham, D. (2015) · Nature · doi:10.1038/nature14207 · Cited by 2
Self-citationTonoyan, L., Guihéneuf, F., Friel, R., & O’Flaherty, V. (2020) · BIO-PROTOCOL · doi:10.21769/bioprotoc.3813 · Cited by 2
Klim, J., Zielenkiewicz, U., Skoneczny, M., Skoneczna, A., Kurlandzka, A., & Kaczanowski, S. (2021) · BMC Ecology and Evolution · doi:10.1186/s12862-021-01830-9 · Cited by 2
Savageau, M. A. (2023) · Journal of Molecular Evolution · doi:10.1007/s00239-023-10127-y · Cited by 2
Genetic conflicts in budding yeast: The 2μ plasmid as a model selfish element
Hays, M. (2024) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2024.04.002 · Cited by 2
Pleiotropy allows recovery of phenotypic plasticity in constant environmentsPreprint
Kingma, E. M., Diepeveen, E. T., de la Cruz, L. I., & Laan, L. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.02.123208 · Cited by 2
Adaptive Laboratory Evolution of Pichia pastoris by Serial Batch Cultivation
Moritz, C., Gassler, T., Altvater, M., & Mattanovich, D. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4779-0_26 · Cited by 1
Price, C. E., dos Santos, F. B., Hesseling, A., Uusitalo, J. J., Bachmann, H., Benavente, V., Goel, A., Berkhout, J., Bruggeman, F. J., Marrink, . J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/147926 · Cited by 1
A high resolution view of adaptive eventsPreprint
Han, M., Arbeithuber, B., Cremona, M. A., DeGiorgio, M., & Nekrutenko, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/429175 · Cited by 1
Cai, Y., & Geritz, S. A. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.13.189662 · Cited by 1
Molecular Systems Predict Equilibrium Distributions of Phenotype Diversity Available for SelectionPreprint
Valderrama-Gómez, M. Á., & Savageau, M. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.27.446045 · Cited by 1
Adaptive strategies under prolonged starvation and role of slow growth in bacterial fitnessPreprint
Nandy, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.14.472581 · Cited by 1
Hays, M., Schwartz, K., Schmidtke, D. T., Aggeli, D., & Sherlock, G. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.11.491585 · Cited by 1
Hansson, E. M., Childs, D. Z., & Beckerman, A. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.26.582134 · Cited by 1
Study of the evolutionary dynamics of plant beneficial bacteria
Manriquez, B. (2021) · doi:10.70675/26d2e9c3z1040z43ebz9981z3f3240e5f687 · Cited by 1
Microbial Experimental Evolution
Dykhuizen, D. E. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-800049-6.00232-8
Factors that enhance the ability of Pseudomonas aeruginosa to resist the action of antibiotics
Matrood, W. A. (2016) · Liverpool John Moores University · doi:10.24377/ljmu.t.00004727
Adaptive evolution of transcription factor binding affinitiesPreprint
Hong, J., Brandt, N., Yang, A. W. H., Hughes, T., & Gresham, D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/118455
Self-citationStudy of Cells in the Steady‐State Growth Space
Erm, S., Abner, K., Seiman, A., Adamberg, K., & Vilu, R. (2017) · Wiley eBooks · doi:10.1002/9783527699902.ch9
Thierie, J. (2018) · Journal of Computational Biology and Bioinformatics Research · doi:10.5897/jcbbr2018.0117
Adaptation of Escherichia coli to the mouse gut
Batista, J. M. B. (2016) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
A synthetic biology approach for the design of genetic algorithms with bacterial agentsPreprint
Gargantilla-Becerra, Á., Gutiérrez, M., & Lahoz-Beltrá, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07540
Wright, N. R., Wulff, T., Workman, C. T., Rønnest, N. P., & Sonnenschein, N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.18.427050
Understanding and engineering the factors that control fatty acid biosynthesis in escherichia coli
Gallagher, D. T. (2018) · Spiral (Imperial College London) · doi:10.25560/86387
Metadata
- Authors
- Gresham, D., & Hong, J.
- Affiliations
- New York University (US)
- Corresponding author
- Gresham, David
- Venue
- FEMS Microbiology Reviews
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-09
- Volume
- 39
- Issue
- 1
- Pages
- n/a-n/a
- ISSN
- 0168-6445 (ISSN-L)
- PMC
- PMC4391987
- OpenAlex
- W2038785521
- Cited by
- 122
- References
- 135
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 11 · 2023: 9 · 2022: 13 · 2021: 23 · 2020: 12 · 2019: 11 · 2018: 13 · 2017: 8 · 2016: 10 · 2015: 5 · 2014: 1
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- FEMS Microbiology Reviews (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Office of Extramural Research, National Institutes of Health (GM107466)
- National Science Foundation (MCB-1244219)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- adaptive evolution, chemostat, experimental evolution, cell growth regulation, gene networks, Escherichia coli, Saccharomyces cerevisiae, selection, growth rate, long-term selection, functional genomics
- MeSH terms
- Adaptation, Physiological, Escherichia coli, Biological Evolution, Saccharomyces cerevisiae, Bioreactors
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