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Chance and necessity in the evolution of minimal metabolic networks
Abstract
It is possible to infer aspects of an organism's lifestyle from its gene content. Can the reverse also be done? Here we consider this issue by modelling evolution of the reduced genomes of endosymbiotic bacteria. The diversity of gene content in these bacteria may reflect both variation in selective forces and contingency-dependent loss of alternative pathways. Using an in silico representation of the metabolic network of Escherichia coli, we examine the role of contingency by repeatedly simulating the successive loss of genes while controlling for the environment. The minimal networks that result are variable in both gene content and number. Partially different metabolisms can thus evolve owing to contingency alone. The simulation outcomes do preserve a core metabolism, however, which is over-represented in strict intracellular bacteria. Moreover, differences between minimal networks based on lifestyle are predictable: by simulating their respective environmental conditions, we can model evolution of the gene content in Buchnera aphidicola and Wigglesworthia glossinidia with over 80% accuracy. We conclude that, at least for the particular cases considered here, gene content of an organism can be predicted with knowledge of its distant ancestors and its current lifestyle.
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“Moreover, for eukaryotic host organisms such as yeasts, alternating asexual and sexual propagation during selection can be applied in order to decouple beneficial and unsound mutations and thus reduce potential deficits of laboratory evolution experiments [118].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [118]
Citations
Cites
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Adhi Harmoko, S., M.Komp, Jacquard, J. M., Zuse, K., & Eniac (2005) · Cited by 18,035
Community structure and metabolism through reconstruction of microbial genomes from the environment
Tyson, G. W., Chapman, J., Hugenholtz, P., Allen, E. E., Ram, R. J., Richardson, P. M., Solovyev, V. V., Rubin, E. M., Rokhsar, D. S., & Banfield, J. F. (2004) · Nature · doi:10.1038/nature02340 · Cited by 2,417
Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS
Shigenobu, S., Watanabe, H., Hattori, M., Sakaki, Y., & Ishikawa, H. (2000) · Nature · doi:10.1038/35024074 · Cited by 1,338
Genome-scale models of microbial cells: evaluating the consequences of constraints
Price, N. D., Reed, J. L., & Palsson, B. Ø. (2004) · Nature Reviews Microbiology · doi:10.1038/nrmicro1023 · Cited by 1,045
An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR)
Reed, J. L., Vo, T. D., Schilling, C. H., & Palsson, B. Ø. (2003) · Genome biology · doi:10.1186/gb-2003-4-9-r54 · Cited by 1,001
Global Transposon Mutagenesis and a Minimal Mycoplasma Genome
Hutchison, C. A., Peterson, S. N., Gill, S. R., Cline, R. T., White, O. R., Fraser, C. M., Smith, H. O., & Venter, J. C. (1999) · Science · doi:10.1126/science.286.5447.2165 · Cited by 925
A minimal gene set for cellular life derived by comparison of complete bacterial genomes
Mushegian, A. R., & Koonin, E. V. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.19.10268 · Cited by 916
Edwards, J. S., & Palsson, B. O. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5528 · Cited by 875
Calafiore, G. C., & Ghaoui, L. E. (2018) · Cambridge University Press eBooks · doi:10.1017/cbo9781107279667.013 · Cited by 822
Flagged first, then by citations
Research works printed but not linked: 1
- Nogge, G. Significance of symbionts for the maintenance of an optimal nutritional state for successful reproduction in haematophagous arthropods. Parasitology 82, 101–104 (1981)
Cited by
Sorted by citations
Items 101–200 of 243
Yang, L., Tan, J., O’Brien, E. J., Monk, J. M., Kim, D., Li, H. J., Charusanti, P., Ebrahim, A., Lloyd, C. J., Yurkovich, J. T., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1501384112 · Cited by 38
Survival of dried eukaryotes (anhydrobiotes) after exposure to very high temperatures
Mertens, J., Beladjal, L., ALCANTARA, A., FOUGNIES, L., Straeten, D. V. D., & Clegg, J. S. (2007) · Biological Journal of the Linnean Society · doi:10.1111/j.1095-8312.2007.00902.x · Cited by 39
Molecular chaperones: The modular evolution of cellular networks
Korcsmaros, T. D., Kovács, I. A., Szalay, M., & Csermely, P. (2007) · Journal of Biosciences · doi:10.1007/s12038-007-0043-y · Cited by 40
Self-citationLularevic, M., Racher, A. J., Jaques, C. M., & Kiparissides, A. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.27025 · Cited by 38
la Fuente, I. M. D., Cortés, J. M., Pelta, D. A., & Veguillas, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058284 · Cited by 36
Biomedical applications of cell- and tissue-specific metabolic network models
Fouladiha, H., & Marashi, S. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.02.014 · Cited by 37
Systems and Synthetic biology: tackling genetic networks and complex diseases
Cuccato, G., Gatta, G. D., & di Bernardo, D. (2009) · Heredity · doi:10.1038/hdy.2009.18 · Cited by 36
The role of cellular objectives and selective pressures in metabolic pathway evolution
Nam, H., Conrad, T. M., & Lewis, N. E. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2011.03.006 · Cited by 32
“Also, Escherichia coli central metabolism is structured as a minimal number of enzymatic steps between nutrients and biomass precursors [5 ], thereby achieving metabolic functionality with a minimal set of enzymes [6].”
Strategies of Genomic Integration Within Insect-Bacterial Mutualisms
Wernegreen, J. J. (2012) · Biological Bulletin · doi:10.1086/bblv223n1p112 · Cited by 33
Deep Questions in the Tree of Life
Keeling, P. J. (2007) · Science · doi:10.1126/science.1149593 · Cited by 33
Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss
McNally, C. P., & Borenstein, E. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0588-4 · Cited by 32
A genetic perspective on insect climate specialists
Hoffmann, A. A. (2010) · Australian Journal of Entomology · doi:10.1111/j.1440-6055.2010.00744.x · Cited by 31
Genotype networks, innovation, and robustness in sulfur metabolism
Rodrigues, J. F. M., & Wagner, A. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-39 · Cited by 30
The Genome-Scale Integrated Networks in Microorganisms
Hao, T., Wu, D., Zhao, L., Wang, Q., Wang, E., & Sun, J. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00296 · Cited by 30
Insect‐symbiont systems: From complex relationships to biotechnological applications
Chaves, S., Neto, M. C., & Tenreiro, R. P. (2009) · Biotechnology Journal · doi:10.1002/biot.200800237 · Cited by 30
Shuler, M. L., Foley, P., & Atlas, J. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-827-6_20 · Cited by 30
Liang, Y., Luo, H., Lin, Y., & Gao, F. (2024) · iMeta · doi:10.1002/imt2.157 · Cited by 29
Metabolic Networks of Sodalis glossinidius: A Systems Biology Approach to Reductive Evolution
Belda, E., Silva, F. J., Peretó, J. G., & Moyá, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030652 · Cited by 28
Hartleb, D., Jarre, F., & Lercher, M. J. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005036 · Cited by 26
Self-citationAgeing as a price of cooperation and complexityPreprint
Kiss, H., Mihalik, Á., Nánási, T., Őry, B., Spiró, Z., Sőti, C., & Csermely, P. (2008) · Nature Precedings · doi:10.1002/bies.200800224 · Cited by 36
Self-citationStress induces remodelling of yeast interaction and co-expression networks
Lehtinen, S., Marsellach, X., Codlin, S., Schmidt, A., Clément‐Ziza, M., Beyer, A., Bähler, J., Orengo, C. A., & Pancaldi, V. (2013) · Molecular BioSystems · doi:10.1039/c3mb25548d · Cited by 29
Burrell, A. M., Hawkins, A. K., & Pepper, A. E. (2012) · American Journal of Botany · doi:10.3732/ajb.1200382 · Cited by 26
Making the right connections: biological networks in the light of evolution
Cg, K., & Pinney, J. W. (2009) · BioEssays · doi:10.1002/bies.200900043 · Cited by 27
The Bacterial World Gets Smaller
Andersson, S. G. E. (2006) · Science · doi:10.1126/science.1133739 · Cited by 24
Tabe-Bordbar, S., & Marashi, S. (2013) · Biotechnology Letters · doi:10.1007/s10529-013-1328-x · Cited by 24
Ins and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?
Backer, P. D., Waele, D. D., & Speybroeck, L. V. (2009) · Acta Biotheoretica · doi:10.1007/s10441-009-9089-6 · Cited by 25
Coluzzi, C., Guillemet, M., Mazzamurro, F., Touchon, M., Godfroid, M., Achaz, G., Glaser, P., & Rocha, E. P. C. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad217 · Cited by 22
Experimental evolution of protein–protein interaction networks
Kaçar, B., & Gaucher, E. A. (2013) · Biochemical Journal · doi:10.1042/bj20130205 · Cited by 20
Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
Heckmann, D., Zielinski, D. C., & Palsson, B. Ø. (2018) · Nature Communications · doi:10.1038/s41467-018-07649-1 · Cited by 21
Inferring ancient metabolism using ancestral core metabolic models of enterobacteria
Baumler, D. J., Ma, B., Reed, J. L., & Perna, N. T. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-46 · Cited by 20
Vicente, C. S. L., Mondal, S. I., Akter, A., Ozawa, S., Kikuchi, T., & Hasegawa, K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200512 · Cited by 17
Scale-Free Networks: Characteristics of the Time-Variant Robustness and Vulnerability
Yu, A., Wang, N., & Wu, N. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3022169 · Cited by 19
Babaei, P., Ghasemi-Kahrizsangi, T., & Marashi, S. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/416289 · Cited by 18
The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems
Csermely, P. (2017) · BioEssays · doi:10.1002/bies.201700150 · Cited by 22
Self-citationStochasticity, determinism, and contingency shape genome evolution of endosymbiotic bacteria
Boyd, B. M., James, I., Johnson, K. P., Weiss, R. B., Bush, S. E., Clayton, D. H., & Dale, C. (2024) · Nature Communications · doi:10.1038/s41467-024-48784-2 · Cited by 19
The Number of Catalytic Elements Is Crucial for the Emergence of Metabolic Cores
la Fuente, I. M. D., Vadillo, F., Pérez-Pinilla, M.-B., López, A. V., & Veguillas, J. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007510 · Cited by 16
Hidden Connectivity in Networks with Vulnerable Classes of Nodes
Krause, S. M., Danziger, M. M., & Zlatić, V. (2016) · Physical Review X · doi:10.1103/physrevx.6.041022 · Cited by 21
Ravindran, S., Hajinajaf, N., Kundu, P., Comes, J., Nielsen, D. R., Varman, A. M., & Ghosh, A. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00379 · Cited by 14
An evaluation of minimal cellular functions to sustain a bacterial cell
Azuma, Y., & Ota, M. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-111 · Cited by 14
Chaiboonchoe, A., Ghamsari, L., Dohai, B., Ng, P., Khraiwesh, B., Jaiswal, A. K., Jijakli, K., Koussa, J., Nelson, D. R., Cai, H., et al. (2016) · Molecular BioSystems · doi:10.1039/c6mb00237d · Cited by 12
Ding, T., Case, K. A., Omolo, M. A., Reiland, H. A., Metz, Z. P., Diao, X., & Baumler, D. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149423 · Cited by 13
Beladjal, L., Mertens, J., & Clegg, J. S. (2008) · Journal of Thermal Biology · doi:10.1016/j.jtherbio.2007.11.003 · Cited by 13
In Silico Experiments in Scientific Papers on Molecular Biology
Moretti, S. (2011) · Science & Technology Studies · doi:10.23987/sts.55262 · Cited by 14
How to design multi-target drugs: Target search options in cellular networksPreprint
Korcsmaros, T. D., Szalay, M., Böde, C., Kovács, I. A., & Csermely, P. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0703010 · Cited by 13
Self-citationDelaye, L., Gil, R., Peretó, J. G., Latorre, A., & Moyá, A. (2010) · The Open Evolution Journal · doi:10.2174/1874404401004010012 · Cited by 12
Wernegreen, J. J. (2017) · Experimental Cell Research · doi:10.1016/j.yexcr.2017.04.028 · Cited by 12
Comparative genomics of metabolic networks of free-living and parasitic eukaryotes
Nerima, B., Nilsson, D., & Mäser, P. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-217 · Cited by 12
Research on small genomes: implications for synthetic biology
Klasson, L., & Andersson, S. G. E. (2010) · BioEssays · doi:10.1002/bies.200900165 · Cited by 12
Erosion of interaction networks in reduced and degraded genomes
Ochman, H., Liu, R., & Rocha, E. P. C. (2007) · Journal of Experimental Zoology Part B Molecular and Developmental Evolution · doi:10.1002/jez.b.21147 · Cited by 13
Efforts to Minimise the Bacterial Genome as a Free-Living Growing System
Aida, H., & Ying, B. (2023) · Biology · doi:10.3390/biology12091170 · Cited by 10
Reconstruction of Danio rerio Metabolic Model Accounting for Subcellular Compartmentalisation
Bekaert, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049903 · Cited by 11
New concepts and tools in constraint-based analysis of metabolic networks
Larhlimi, A. (2009) · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · doi:10.17169/refubium-11716 · Cited by 11
Genomic organization underlying deletional robustness in bacterial metabolic systems
Hosseini, S., & Wagner, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717243115 · Cited by 10
Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology
Papp, B., Szappanos, B., & Notebaart, R. A. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-173-4_27 · Cited by 12
Self-citationNOVEL INSIGHT INTO EVOLUTIONARY PROCESS FROM AVERAGE GENOME SIZE IN MARINE BACTERIOPLANKTONIC BIOTA
Ni, J. (2016) · Applied Ecology and Environmental Research · doi:10.15666/aeer/1402_065075 · Cited by 8
Barve, A., Hosseini, S., Martin, O., & Wagner, A. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-48 · Cited by 11
Hall, R. J., Thorpe, S., Thomas, G. H., & Wood, A. J. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000378 · Cited by 10
Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium
Ponce-de-León, M., Tamarit, D., Calle-Espinosa, J., Mori, M., Latorre, A., Montero, F., & Peretó, J. G. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02290 · Cited by 9
Avila-Jaime, B., Kawas, J. R., & García-Mazcorro, J. F. (2018) · Livestock Science · doi:10.1016/j.livsci.2018.04.017 · Cited by 8
Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora
Valadez‐Cano, C., Olivares‐Hernández, R., Reséndis-Antonio, O., DeLuna, A., & Delaye, L. (2017) · BMC Evolutionary Biology · doi:10.1186/s12862-017-0947-6 · Cited by 8
Abedpour, N., & Kollmann, M. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0232-5 · Cited by 7
Rewiring of Genetic Networks in Response to Modification of Genetic Background
Bajić, D., Moreno-Fenoll, C., & Poyatos, J. F. (2014) · Genome Biology and Evolution · doi:10.1093/gbe/evu255 · Cited by 7
Basics on network theory to analyze biological systems: a hands-on outlook
Amores, G. R., & Martínez‐Antonio, A. (2022) · Functional & Integrative Genomics · doi:10.1007/s10142-022-00907-y · Cited by 7
Jia, Y., Qin, C., Traw, M. B., Chen, X., He, Y., Jing, K., Yang, S., Wang, L., & Hurst, L. D. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010071 · Cited by 7
Self-citationSystems biology and food microbiology
Brul, S., & Westerhoff, H. V. (2007) · Elsevier eBooks · doi:10.1533/9781845692940.2.250 · Cited by 5
A Creationist Perspective of Beneficial Mutations in Bacteria
Anderson, K. L., & Purdom, G. (2008) · DigitalCommons-Cedarville (Cedarville University) · Cited by 5
Garza, D. R., von Meijenfeldt, F. A. B., van Dijk, B., Boleij, A., Huynen, M. A., & Dutilh, B. E. (2022) · BMC Ecology and Evolution · doi:10.1186/s12862-022-02052-3 · Cited by 4
Introduction to Systems Biology for Mathematical Programmers
Livingstone, K. D., Holder, A. G., & Almaas, E. (2008) · Engineering and management innovation series/Engineering and management innovation · doi:10.1201/9780849305696.pt2 · Cited by 4
Marashi, S., Kouhestani, H., & Mahdavi, M. (2013) · The Scientific World JOURNAL · doi:10.1155/2013/615697 · Cited by 4
Moyer, D. C., Pacheco, A. R., Bernstein, D. B., & Segrè, D. (2021) · Journal of Molecular Evolution · doi:10.1007/s00239-021-10018-0 · Cited by 4
Leaner and meaner genomes in Escherichia coli.
Ussery, D. W. (2006) · Genome biology · doi:10.1186/gb-2006-7-10-237 · Cited by 4
de Diego, A. M. G., Lorrio, S., Hernández-Guijo, J. M., Gandía, L., & de Diego, A. M. G. (2012) · Wiley eBooks · doi:10.1002/9781118185537.ch4 · Cited by 4
Tamasco, G. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.17.2022.tde-08112022-173558 · Cited by 4
Emergence of predictive capacity within microbial genetic networks
Tavazoie, S., Kung, S., & Tagkopoulos, I. (2008) · Cited by 3
Genome-Scale Reconstruction, Modeling, and Simulation of E. coli℉s Metabolic Network
Feist, A. M., Thiele, I., & Palsson, B. Ø. (2009) · Springer eBooks · doi:10.1007/978-1-4020-9394-4_9 · Cited by 3
Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks
Bardoscia, M., Matteo Marsili, A., & Samal, A. (2015) · Physical Review E · doi:10.1103/physreve.92.012809 · Cited by 3
Bahramali, G., Goliaei, B., Minuchehr, Z., & Marashi, S. (2016) · Amino Acids · doi:10.1007/s00726-016-2361-6 · Cited by 3
Hao, T., Wang, B., Zhao, L., Feng, X., & Sun, J. (2018) · IEEE Access · doi:10.1109/access.2018.2885005 · Cited by 3
Curating COBRA Models of Microbial Metabolism
Navid, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1585-0_14 · Cited by 3
Automated high-quality reconstruction of metabolic networks from high-throughput dataPreprint
Hartleb, D., Fritzemeier, C. J., & Lercher, M. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/282251 · Cited by 3
Self-citationStructuring complexity by mapping the possible in microbial ecosystems
Bajić, D., van Oort, M., Gabriëls, M., & Gojković, U. (2025) · Current Opinion in Microbiology · doi:10.1016/j.mib.2025.102658 · Cited by 2
Kataria, A., Bohrnsen, E., Schwarz, B. H., Drecktrah, D., Samuels, D. S., Carmody, A., Myers, L. M., & Groshong, A. M. (2025) · Communications Biology · doi:10.1038/s42003-025-09374-0 · Cited by 2
Repeatability of gene expression evolution in experimental environmental adaptation
Li, J., & Zhang, J. (2026) · Nature Communications · doi:10.1038/s41467-026-68838-x · Cited by 2
Minimisation of metabolic networks defines a new functional class of genes
Jansen, G., Qi, T., Latora, V., Amoutzias, G. D., Delneri, D., Oliver, S. G., & Nicosia, G. (2024) · Nature Communications · doi:10.1038/s41467-024-52816-2 · Cited by 2
Self-citationGenotype Networks and Evolutionary Innovations in Biological Systems
Wagner, A. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385944-0.00013-7 · Cited by 2
Sensory Transduction Network of E. coli
Galperin, M. Y. (2009) · Springer eBooks · doi:10.1007/978-1-4020-9394-4_8 · Cited by 3
1 Introduction to Functional Analysis in Yeast
Oliver, S. G. (2007) · Methods in microbiology · doi:10.1016/s0580-9517(06)36001-1 · Cited by 2
Self-citationBetts, M. J., & Russell, R. B. (2009) · Nature Structural & Molecular Biology · doi:10.1038/nsmb1109-1125 · Cited by 2
Purdom, G., & Anderson, K. L. (2008) · DigitalCommons-Cedarville (Cedarville University) · Cited by 2
Enzymes and Metabolic Pathways
Lynch, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780192847287.003.0019 · Cited by 2
Pinney, J. W. (2011) · doi:10.1002/9781119970606.ch23 · Cited by 2
The chemical basis of metabolic interdependence in microbial communitiesPreprint
Goyal, A., & Krishna, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484247 · Cited by 2
Modular gene interactions drive modular pan-genome evolution in bacteriaPreprint
Castro, J., & Brown, S. (2023) · bioRxiv · doi:10.1101/2022.11.15.516554 · Cited by 2
Human essential gene identification based on feature fusion and feature screening
Zhang, Z., Fan, Y., Cheng-bing, H., & Du, M. (2024) · IET Systems Biology · doi:10.1049/syb2.12105 · Cited by 1
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Konno, N., Maeno, S., & Endo, A. (2026) · Current Opinion in Microbiology · doi:10.1016/j.mib.2026.102815 · Cited by 1
Chi, B., Tao, S., & Liu, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139590 · Cited by 1
Multhoff, G., & Maio, A. D. (2006) · Cell stress & chaperones (Print) · doi:10.1379/csc-203.1 · Cited by 1
Metabolic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem
Ye, C., Xu, N., Chen, X., & Liu, L. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7528-0_10 · Cited by 1
Essentiality Landscape of Metabolic Networks
Kim, P., Kahng, B., Han, K., & Lee, D. S. (2019) · Journal of the Korean Physical Society · doi:10.3938/jkps.74.512 · Cited by 1
Maddamsetti, R. (2022) · Genome Biology and Evolution · doi:10.1093/gbe/evac114 · Cited by 2
Metadata
- Authors
- Pál, C., Papp, B., Lercher, M. J., Csermely, P., Oliver, S. G., & Hurst, L. D.
- Affiliations
- European Molecular Biology Laboratory (DE)
- University of Manchester (GB)
- Semmelweis University (HU)
- University of Bath (GB)
- Corresponding author
- Hurst, Laurence D.
- Venue
- Nature
- Published
- 2006-03
- Volume
- 440
- Issue
- 7084
- Pages
- 667-670
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2139553051
- Cited by
- 239
- References
- 31
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 4 · 2022: 14 · 2021: 2 · 2020: 6 · 2019: 4 · 2018: 12 · 2017: 12 · 2016: 10 · 2015: 10 · 2014: 10 · 2013: 15 · 2012: 18
- FWCI
- 9.78
- 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)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- Biotechnology and Biological Sciences Research Council (E19354)
- 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 › Agricultural and Biological Sciences › Insect Science › Insect symbiosis and bacterial influences
- Keywords
- gene loss, contingency, endosymbiotic bacteria, Buchnera aphidicola, Wigglesworthia glossinidia, Escherichia coli, genome reduction, metabolic network simulation, core metabolism, symbionts, non-essential genes
- MeSH terms
- Biological Evolution, Genes, Bacterial, Symbiosis, Genome, Bacterial, Computational Biology, Buchnera, Gene Transfer, Horizontal, Escherichia coli Proteins, Wigglesworthia, Escherichia coli K12
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 ›
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