Reconstructing organisms in silico: genome-scale models and their emerging applications
Open accessAbstract
Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.
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Combinatorial multiplexing experiments remain prohibitively expensive and technically challenging to scale.
“Furthermore, combinatorial multiplexing experiments remain prohibitively expensive and technically challenging to scale (7, 8).”
From A systematic comparison of single-cell perturbation response prediction models (2026) · cites this paper as [7]
The Emerging applications of GEMs section discusses multi-strain analysis and bottlenecks in constructing computational models, not the cost or scalability of combinatorial multiplexing experiments. Searches for multiplexing and for the expense and technical difficulty of combinatorial experiments found no passage addressing the claim. (LLM, conf=0.74)
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Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
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 · see citing works
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903 · see citing works
Self-citationCaspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395 · see citing works
A protocol for generating a high-quality genome-scale metabolic reconstruction
Thiele, I., & Palsson, B. Ø. (2010) · Nature Protocols · doi:10.1038/nprot.2009.203 · Cited by 1,789 · see citing works
Self-citationKBase: The United States Department of Energy Systems Biology Knowledgebase
(2018) · doi:10.1038/nbt.4163 · Cited by 1,624 · see citing works
COBRApy: COnstraints-Based Reconstruction and Analysis for Python
Ebrahim, A., Lerman, J. A., Palsson, B. O., & Hyduke, D. R. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-74 · Cited by 1,378 · see citing works
Self-citationFeist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Vassily Hatzimanikatis, A., & Palsson, B. Ø. (2007) · Molecular Systems Biology · doi:10.1038/msb4100155 · Cited by 1,367 · see citing works
Self-citationA Whole-Cell Computational Model Predicts Phenotype from Genotype
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., & Covert, M. W. (2012) · Cell · doi:10.1016/j.cell.2012.05.044 · Cited by 1,300 · cited 2× · see citing works
“Whole-cell models of the human pathogen Mycoplasma genitalium 115 , Saccharomyces cerevisiae 116 and E. coli 117 have been developed.”
BackgroundMethodHolt, K. E., Wertheim, H. F. L., Zadoks, R. N., Baker, S. G., Whitehouse, C. A., Dance, D. A. B., Jenney, A., Connor, T. R., Hsu, L. Y., Severin, J. A., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1501049112 · Cited by 1,276 · see citing works
Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003) · Biotechnology and Bioengineering · doi:10.1002/bit.10803 · Cited by 1,262 · see citing works
Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0
Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., Haraldsdóttir, H. S., Wachowiak, J., Keating, S., Vlasov, V., et al. (2019) · Nature Protocols · doi:10.1038/s41596-018-0098-2 · Cited by 1,253 · see citing works
Self-citationMetabolic engineering of Escherichia coli for direct production of 1,4-butanediol
Yim, H. H., Haselbeck, R. J., Niu, W., Pujol-Baxley, C., Burgard, A. P., Boldt, J., Khandurina, J., Trawick, J. D., Osterhout, R. E., Stephen, R., et al. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.580 · Cited by 1,119 · see citing works
High-throughput generation, optimization and analysis of genome-scale metabolic models
Henry, C. S., DeJongh, M., Best, A. A., Frybarger, P. M., Linsay, B., & Stevens, R. L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1672 · Cited by 1,090 · see citing works
Milo, R., & Phillips, R. (2015) · Garland Science eBooks · doi:10.1201/9780429258770 · Cited by 1,034 · see citing works
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 1,024 · see citing works
Self-citationA community-driven global reconstruction of human metabolism
Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2488 · Cited by 1,019 · see citing works
Self-citationA comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011
Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., & Palsson, B. Ø. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.65 · Cited by 1,006 · see citing works
Self-citationAn 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 · see citing works
Self-citationDynamic flux balance analysis of diauxic growth in Escherichia coli
Mahadevan, R., Edwards, J. S., & Doyle, F. J. (2002) · Biophysical Journal · doi:10.1016/s0006-3495(02)73903-9 · Cited by 945 · see citing works
Metabolic dependencies drive species co-occurrence in diverse microbial communities
Zelezniak, A., Andrejev, S., Ponomarova, O., Mende, D. R., Bork, P., & Patil, K. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421834112 · Cited by 930 · see citing works
The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities
Davis, J. J., Wattam, A. R., Aziz, R. K., Brettin, T. S., Butler, R. M., Butler, R., Chlenski, P., Conrad, N. R., Dickerman, A. W., Dietrich, E. M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz943 · Cited by 880 · see citing works
Edwards, J. S., & Palsson, B. O. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5528 · Cited by 875 · see citing works
Self-citationApplications of genome‐scale metabolic reconstructions
Oberhardt, M., Palsson, B. Ø., & Papin, J. A. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.77 · Cited by 820 · see citing works
Self-citationConstraint-based models predict metabolic and associated cellular functions
Bordbar, A., Monk, J. M., King, Z. A., & Palsson, B. Ø. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3643 · Cited by 818 · see citing works
Self-citationIntegrating high-throughput and computational data elucidates bacterial networks
Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J., & Palsson, B. O. (2004) · Nature · doi:10.1038/nature02456 · Cited by 792 · see citing works
Self-citationConstraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods
Lewis, N. E., Nagarajan, H., & Palsson, B. O. (2012) · Nature Reviews Microbiology · doi:10.1038/nrmicro2737 · Cited by 779 · see citing works
Self-citationCurrent status and applications of genome-scale metabolic models
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2019) · Genome biology · doi:10.1186/s13059-019-1730-3 · Cited by 765 · see citing works
Recon3D enables a three-dimensional view of gene variation in human metabolism
Brunk, E., Sahoo, S., Zielinski, D. C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Preciat, G., Aurich, M. K., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4072 · Cited by 755 · see citing works
Self-citationFast automated reconstruction of genome-scale metabolic models for microbial species and communities
Machado, D., Andrejev, S., Tramontano, M., & Patil, K. R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky537 · Cited by 724 · see citing works
Using Genome-scale Models to Predict Biological Capabilities
O’Brien, E. J., Monk, J. M., & Palsson, B. Ø. (2015) · Cell · doi:10.1016/j.cell.2015.05.019 · Cited by 709 · see citing works
Self-citationThe biomass objective function
Feist, A. M., & Palsson, B. Ø. (2010) · Current Opinion in Microbiology · doi:10.1016/j.mib.2010.03.003 · Cited by 608 · see citing works
Self-citationMetabolic modeling of a mutualistic microbial community
Stolyar, S. M., Dien, S. V., Hillesland, K. L., Pinel, N., Lie, T. J., Leigh, J. A., & Stahl, D. A. (2007) · Molecular Systems Biology · doi:10.1038/msb4100131 · Cited by 553 · see citing works
The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli
Feist, A. M., & Palsson, B. Ø. (2008) · Nature Biotechnology · doi:10.1038/nbt1401 · Cited by 545 · cited 1× · see citing works
“GEMs have been successfully implemented for a wide range of applications 10–17 , including understanding microorganisms 16–22 , metabolic engineering 23–28 , drug development 29 , prediction of enzyme functions 30 , understanding microbial community interactions 31–40 and human disease 41,42 .”
MethodSelf-citationiML1515, a knowledgebase that computes Escherichia coli traits
Monk, J. M., Lloyd, C. J., Brunk, E., Mih, N., Sastry, A. V., King, Z. A., Takeuchi, R., Nomura, W., Zhang, Z., Mori, H., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3956 · Cited by 542 · see citing works
Self-citationComparison of 61 Sequenced Escherichia coli Genomes
Lukjančenko, O., Wassenaar, T. M., & Ussery, D. W. (2010) · Microbial Ecology · doi:10.1007/s00248-010-9717-3 · Cited by 535 · see citing works
Mardinoğlu, A., Ågren, R., Kampf, C., Asplund, A., Uhlén, M., & Nielsen, J. (2014) · Nature Communications · doi:10.1038/ncomms4083 · Cited by 520 · see citing works
Harcombe, W. R., Riehl, W. J., Dukovski, I., Granger, B. R., Betts, A., Lang, A., Bonilla, G. M., Kar, A., Leiby, N., Mehta, P., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.03.070 · Cited by 518 · see citing works
MEMOTE for standardized genome-scale metabolic model testing
Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F. T., Ataman, M., Babaei, P., Bartell, J. A., Blank, L. M., Chauhan, S., Correia, K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0446-y · Cited by 509 · see citing works
Self-citationGenome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471 · see citing works
Self-citationPredicting selective drug targets in cancer through metabolic networks
Folger, O., Jerby‐Arnon, L., Frezza, C., Gottlieb, E., Ruppin, E., & Shlomi, T. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.35 · Cited by 469 · see citing works
Sánchez, B. J., Zhang, C., Nilsson, A., Lahtvee, P., Kerkhoven, E. J., & Nielsen, J. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167411 · Cited by 466 · see citing works
Systems Properties of the Haemophilus influenzaeRd Metabolic Genotype
Edwards, J. S., & Palsson, B. Ø. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.25.17410 · Cited by 434 · see citing works
Self-citationÅgren, R., Liu, L., Shoaie, S., Vongsangnak, W., Nookaew, I., & Nielsen, J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002980 · Cited by 418 · see citing works
Levy, R., & Borenstein, E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1300926110 · Cited by 402 · see citing works
Zomorrodi, A. R., & Maranas, C. D. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002363 · Cited by 397 · see citing works
Noronha, A., Modamio, J., Jarosz, Y., Guerard, E., Sompairac, N., Preciat, G., Daníelsdóttir, A. D., Krecké, M., Merten, D., Haraldsdóttir, H. S., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky992 · Cited by 383 · see citing works
Wang, H., Marcišauskas, S., Sánchez, B. J., Domenzain, I., Hermansson, D., Agren, R., Nielsen, J., & Kerkhoven, E. J. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006541 · Cited by 381 · see citing works
The bacterial pangenome as a new tool for analysing pathogenic bacteria
Rouli, L., Merhej, V., Fournier, P., & Raoult, D. (2015) · New Microbes and New Infections · doi:10.1016/j.nmni.2015.06.005 · Cited by 348 · see citing works
Xu, P., Ranganathan, S., Fowler, Z. L., Maranas, C. D., & Koffas, M. A. G. (2011) · Metabolic Engineering · doi:10.1016/j.ymben.2011.06.008 · Cited by 346 · see citing works
Lu, H., Li, F., Sánchez, B. J., Zhu, Z., Li, G., Domenzain, I., Marcišauskas, S., Anton, P. M., Lappa, D., Lieven, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11581-3 · Cited by 344 · see citing works
Bailey, J. E. (1998) · Biotechnology Progress · doi:10.1021/bp9701269 · Cited by 333 · see citing works
Pramanik, J., & Keasling, J. D. (1997) · Biotechnology and Bioengineering · doi:10.1002/(sici)1097-0290(19971120)56:4 · Cited by 329 · see citing works
Zhuang, K. H., Izallalen, M., Mouser, P., Richter, H., Risso, C., Mahadevan, R., & Lovley, D. R. (2010) · The ISME Journal · doi:10.1038/ismej.2010.117 · Cited by 321 · see citing works
Basic and applied uses of genome‐scale metabolic network reconstructions of Escherichia coli
McCloskey, D., Palsson, B. Ø., & Feist, A. M. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.18 · Cited by 320 · see citing works
Self-citationWhat Makes a Bacterial Species Pathogenic?:Comparative Genomic Analysis of the Genus Leptospira
Fouts, D. E., Matthias, M. A., Adhikarla, H., Adler, B., Santos, L. A., Berg, D. E., Bulach, D., Buschiazzo, A., Chang, Y.-F., Galloway, R. L., et al. (2016) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0004403 · Cited by 311 · see citing works
Self-citationMonk, J. M., Charusanti, P., Aziz, R. K., Lerman, J. A., Premyodhin, N., Orth, J. D., Feist, A. M., & Palsson, B. Ø. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1307797110 · Cited by 286 · see citing works
Self-citationRecon 2.2: from reconstruction to model of human metabolism
Swainston, N., Smallbone, K., Hefzi, H., Dobson, P. D., Brewer, J., Hanscho, M., Zielinski, D. C., Ang, K. S., Gardiner, N. J., Gutierrez, J. M., et al. (2016) · Metabolomics · doi:10.1007/s11306-016-1051-4 · Cited by 285 · see citing works
Systems approach to refining genome annotation
Reed, J. L., Patel, T., Chen, K. H., Joyce, A. R., Applebee, M. K., Herring, C. D., Bui, O., Knight, E. M., Fong, S. S., & Palsson, B. Ø. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0603364103 · Cited by 283 · see citing works
Self-citationMetabolic Capabilities of Escherichia coli: I. Synthesis of Biosynthetic Precursors and Cofactors
Varma, A., & Palsson, B. Ø. (1993) · Journal of Theoretical Biology · doi:10.1006/jtbi.1993.1202 · Cited by 282 · see citing works
Self-citationIn silico method for modelling metabolism and gene product expression at genome scale
Lerman, J. A., Hyduke, D. R., Latif, H., Portnoy, V. A., Lewis, N. E., Orth, J. D., Schrimpe-Rutledge, A. C., Smith, R. D., Adkins, J. N., Zengler, K., et al. (2012) · Nature Communications · doi:10.1038/ncomms1928 · Cited by 266 · see citing works
Self-citationBosi, E., Monk, J. M., Aziz, R. K., Fondi, M., Nizet, V., & Palsson, B. Ø. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1523199113 · Cited by 266 · see citing works
Self-citationA Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism
Hefzi, H., Ang, K. S., Hanscho, M., Bordbar, A., Ruckerbauer, D. E., Lakshmanan, M., Orellana, C. A., Baycin‐Hizal, D., Huang, Y., Ley, D., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.10.020 · Cited by 253 · see citing works
Self-citationBiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree
Norsigian, C. J., Pusarla, N., McConn, J. L., Yurkovich, J. T., Dräger, A., Palsson, B. Ø., & King, Z. A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1054 · Cited by 250 · see citing works
Self-citationMintz-Oron, S., Meir, S., Malitsky, S., Ruppin, E., Aharoni, A., & Shlomi, T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1100358109 · Cited by 244 · see citing works
Metabolic Capabilities of Escherichia coli II. Optimal Growth Patterns
Varma, A., & Palsson, B. Ø. (1993) · Journal of Theoretical Biology · doi:10.1006/jtbi.1993.1203 · Cited by 233 · see citing works
Self-citationA systematic assessment of current genome-scale metabolic reconstruction tools
Mendoza, S. N., Olivier, B. G., Molenaar, D., & Teusink, B. (2019) · Genome biology · doi:10.1186/s13059-019-1769-1 · Cited by 230 · see citing works
Heckmann, D., Lloyd, C. J., Mih, N., Ha, Y., Zielinski, D. C., Haiman, Z. B., Desouki, A. A., Lercher, M. J., & Palsson, B. O. (2018) · Nature Communications · doi:10.1038/s41467-018-07652-6 · Cited by 218 · see citing works
Self-citationSystems Biology: Constraint-based Reconstruction and Analysis
Palsson, B. Ø. (2015) · doi:10.1017/cbo9781139854610 · Cited by 210 · see citing works
Self-citationA Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models
Opdam, S., Richelle, A., Kellman, B. P., Li, S., Zielinski, D. C., & Lewis, N. E. (2017) · Cell Systems · doi:10.1016/j.cels.2017.01.010 · Cited by 209 · see citing works
Genome-Scale Thermodynamic Analysis of Escherichia coli Metabolism
Henry, C. S., Jankowski, M. D., Broadbelt, L. J., & Vassily Hatzimanikatis, A. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.071720 · Cited by 208 · see citing works
GrowMatch: 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 207 · see citing works
Integration 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 203 · see citing works
Archer, C. T., Kim, J. F., Jeong, H., Park, J. H., Vickers, C. E., Lee, S. Y., & Nielsen, L. K. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-9 · Cited by 202 · see citing works
Optimizing genome-scale network reconstructions
Monk, J. M., Nogales, J., & Palsson, B. Ø. (2014) · Nature Biotechnology · doi:10.1038/nbt.2870 · Cited by 200 · see citing works
Self-citationNogales, J., Mueller, J., Guðmundsson, S., Canalejo, F. J., Duque, E., Monk, J. M., Feist, A. M., Ramos, J. L., Niu, W., & Palsson, B. Ø. (2019) · Environmental Microbiology · doi:10.1111/1462-2920.14843 · Cited by 191 · see citing works
Self-citationModelling approaches for studying the microbiome
Kumar, M., Ji, B., Zengler, K., & Nielsen, J. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0491-9 · Cited by 190 · see citing works
Modeling Hybridoma Cell Metabolism Using a Generic Genome-Scale Metabolic Model of Mus musculus
Sheikh, K., Förster, J., & Nielsen, L. K. (2005) · Biotechnology Progress · doi:10.1021/bp0498138 · Cited by 185 · see citing works
Thiele, I., Jamshidi, N., Fleming, R. M. T., & Palsson, B. Ø. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000312 · Cited by 182 · see citing works
Self-citationCOBRAme: A computational framework for genome-scale models of metabolism and gene expression
Lloyd, C. J., Ebrahim, A., Yang, L., King, Z. A., Catoiu, E., O’Brien, E. J., Liu, J. K., & Palsson, B. O. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006302 · Cited by 167 · see citing works
Self-citationMoretti, S., Martin, O., Van Du Tran, T., Bridge, A., Morgat, A., & Pagni, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1117 · Cited by 162 · see citing works
Integrative genome‐scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery
Kim, H. U., Kim, S. Y., Jeong, H., Kim, T. Y., Kim, J. J., Choy, H. E., Yi, K. Y., Rhee, J. H., & Lee, S. Y. (2011) · Molecular Systems Biology · doi:10.1038/msb.2010.115 · Cited by 160 · see citing works
Systematizing the generation of missing metabolic knowledge
Orth, J. D., & Palsson, B. Ø. (2010) · Biotechnology and Bioengineering · doi:10.1002/bit.22844 · Cited by 155 · see citing works
Self-citationSimultaneous cross-evaluation of heterogeneous <i>E. coli</i> datasets via mechanistic simulation
Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I., et al. (2020) · Science · doi:10.1126/science.aav3751 · Cited by 151 · see citing works
Predicting synthetic rescues in metabolic networks
Motter, A. E., Gulbahce, N., Almaas, E., & Barabási, A.-Ĺ. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.1 · Cited by 142 · see citing works
MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases
Kumar, A., Suthers, P. F., & Maranas, C. D. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-6 · Cited by 141 · see citing works
Mahadevan, R., Palsson, B. Ø., & Lovley, D. R. (2010) · Nature Reviews Microbiology · doi:10.1038/nrmicro2456 · Cited by 140 · see citing works
Self-citationThe y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function
Ghatak, S., King, Z. A., Sastry, A. V., & Palsson, B. Ø. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz030 · Cited by 140 · see citing works
Self-citationSystems medicine and metabolic modelling
Mardinoğlu, A., & Nielsen, J. (2011) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2011.02493.x · Cited by 137 · see citing works
Seif, Y., Kavvas, E. S., Lachance, J., Yurkovich, J. T., Nuccio, S., Fang, X. D., Catoiu, E., Raffatellu, M., Palsson, B. Ø., & Monk, J. M. (2018) · Nature Communications · doi:10.1038/s41467-018-06112-5 · Cited by 136 · see citing works
Self-citationCheung, C. Y. M., Williams, T. C. R., Poolman, M. G., Fell, D. A., Ratcliffe, R. G., & Sweetlove, L. J. (2013) · The Plant Journal · doi:10.1111/tpj.12252 · Cited by 134 · see citing works
Zomorrodi, A. R., & Segrè, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01407-5 · Cited by 130 · see citing works
Modeling metabolism of the human gut microbiome
Magnúsdóttir, S., & Thiele, I. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.12.005 · Cited by 127 · see citing works
Application of the redox potential for controling a sulfide oxidizing bioreactor
Janssen, A. J. H., Meijer, S., Bontsema, J., & Lettinga, G. (1998) · Biotechnology and Bioengineering · doi:10.1002/(sici)1097-0290(19981020)60:2 · Cited by 123 · see citing works
Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium
Montagud, A., Navarro, E., de Córdoba, P. F., Urchueguía, J. F., & Patil, K. R. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-156 · Cited by 121 · see citing works
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Yang, L., Mih, N., Anand, A., Park, J. H., Tan, J., Yurkovich, J. T., Monk, J. M., Lloyd, C. J., Sandberg, T. E., Seo, S. W., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905039116 · Cited by 121 · see citing works
Self-citationO’Brien, E. J., Utrilla, J., & Palsson, B. Ø. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004998 · Cited by 113 · see citing works
Self-citationSalvy, P., & Vassily Hatzimanikatis, A. (2020) · Nature Communications · doi:10.1038/s41467-019-13818-7 · Cited by 100 · see citing works
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Guzmán, G. I., Utrilla, J., Nurk, S., Brunk, E., Monk, J. M., Ebrahim, A., Palsson, B. Ø., & Feist, A. M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414218112 · Cited by 92 · see citing works
Self-citationLiu, J. K., O’Brien, E. J., Lerman, J. A., Zengler, K., Palsson, B. Ø., & Feist, A. M. (2014) · BMC Systems Biology · doi:10.1186/s12918-014-0110-6 · Cited by 92 · see citing works
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Chen, K., Gao, Y., Mih, N., O’Brien, E. J., Yang, L., & Palsson, B. Ø. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705524114 · Cited by 90 · see citing works
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Norsigian, C. J., Fang, X. D., Seif, Y., Monk, J. M., & Palsson, B. Ø. (2019) · Nature Protocols · doi:10.1038/s41596-019-0254-3 · Cited by 90 · see citing works
Self-citationBioModels: expanding horizons to include more modelling approaches and formats
Glont, M., Nguyen, T. V. N., Graesslin, M., Hälke, R., Ali, R., Schramm, J., Wimalaratne, S. M., Kothamachu, V. B., Rodriguez, N., Swat, M. J., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1023 · Cited by 89 · see citing works
Zúñiga, C., Li, C., Yu, G., Al‐Bassam, M. M., Li, T., Jiang, L., Zaramela, L. S., Guarnieri, M. T., Betenbaugh, M. J., & Zengler, K. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0567-6 · Cited by 88 · see citing works
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Nilsson, A., Nielsen, J., & Palsson, B. O. (2017) · Cell Systems · doi:10.1016/j.cels.2017.11.013 · Cited by 87 · see citing works
Self-citationBenedict, M. N., Gonnerman, M. C., Metcalf, W. W., & Price, N. D. (2011) · Journal of Bacteriology · doi:10.1128/jb.06040-11 · Cited by 84 · see citing works
A biochemically-interpretable machine learning classifier for microbial GWAS
Kavvas, E. S., Yang, L., Monk, J. M., Heckmann, D., & Palsson, B. Ø. (2020) · Nature Communications · doi:10.1038/s41467-020-16310-9 · Cited by 82 · see citing works
Self-citationMcAnulty, M. J., Yen, J. Y., Freedman, B. G., & Senger, R. S. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-42 · Cited by 79 · see citing works
Wang, Q., Chen, X., Yang, Y., & Zhao, X. (2006) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-006-0535-y · Cited by 78 · see citing works
Heckmann, D., Campeau, A., Lloyd, C. J., Phaneuf, P. V., Hefner, Y., Carrillo-Terrazas, M., Feist, A. M., Gonzalez, D. J., & Palsson, B. Ø. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001562117 · Cited by 78 · see citing works
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Carrera, J., Estrela, R., Luo, J., Rai, N., Τσουκαλάς, Α., & Tagkopoulos, I. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145108 · Cited by 76 · see citing works
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 74 · see citing works
Plant genome-scale metabolic reconstruction and modelling
Dal’Molin, C. G. D. O., & Nielsen, L. K. (2012) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2012.08.007 · Cited by 71 · see citing works
Coupling S-adenosylmethionine–dependent methylation to growth: Design and uses
Luo, H., Hansen, A. S. L., Yang, L., Schneider, K. P., Kristensen, M., Christensen, U., Christensen, H. B., Du, B., Özdemir, E., Feist, A. M., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.2007050 · Cited by 71 · see citing works
Self-citationNavid, A., & Almaas, E. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-150 · Cited by 68 · see citing works
Du, B., Yang, L., Lloyd, C. J., Fang, X. D., & Palsson, B. Ø. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007525 · Cited by 63 · see citing works
Self-citationMedlock, G. L., & Papin, J. A. (2020) · Cell Systems · doi:10.1016/j.cels.2019.11.006 · Cited by 63 · see citing works
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Seiler, N., & Atanassov, C. L. (1994) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-7156-3_4 · Cited by 62 · see citing works
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Hastings, J., Mains, A., Virk, B., Rodríguez, N., Murdoch, S., Pearce, J., Bergmann, S., Novère, N. G. L., & Casanueva, O. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00002 · Cited by 62 · see citing works
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Brunk, E., Mih, N., Monk, J. M., Zhang, Z., O’Brien, E. J., Bliven, S. E., Chen, K., Chang, R. L., Bourne, P. E., & Palsson, B. Ø. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0271-6 · Cited by 59 · see citing works
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Liu, J. K., Lloyd, C. J., Al‐Bassam, M. M., Ebrahim, A., Kim, J.-N., Olson, C. A., Aksenov, A. A., Dorrestein, P. C., & Zengler, K. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006848 · Cited by 59 · see citing works
Self-citationFang, X. D., Monk, J. M., Nurk, S., Akseshina, M., Zhu, Q., Gemmell, C., Gianetto-Hill, C., Leung, N., Szubin, R., Sanders, J. G., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02559 · Cited by 57 · see citing works
Self-citationMetabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans
Kumar, V. S., Ferry, J. G., & Maranas, C. D. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-28 · Cited by 52 · see citing works
Fang, X. D., Monk, J. M., Mih, N., Du, B., Sastry, A. V., Kavvas, E. S., Seif, Y., Smarr, L. L., & Palsson, B. O. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0587-5 · Cited by 50 · see citing works
Self-citationSalvy, P., & Vassily Hatzimanikatis, A. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013836118 · Cited by 50 · see citing works
Norsigian, C. J., Attia, H., Szubin, R., Yassin, A. S., Palsson, B. Ø., Aziz, R. K., & Monk, J. M. (2019) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2019.00161 · Cited by 49 · see citing works
Self-citationssbio: a Python framework for structural systems biology
Mih, N., Brunk, E., Chen, K., Catoiu, E., Sastry, A. V., Kavvas, E. S., Monk, J. M., Zhang, Z., & Palsson, B. Ø. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty077 · Cited by 43 · see citing works
Self-citationNorsigian, C. J., Kavvas, E. S., Seif, Y., Palsson, B. Ø., & Monk, J. M. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00121 · Cited by 42 · see citing works
Self-citationComputational Modeling of the Human Microbiome
Chowdhury, S., & Fong, S. S. (2020) · Microorganisms · doi:10.3390/microorganisms8020197 · Cited by 42 · see citing works
Imielinski, M. B., Belta, C. A., Rubin, H. N., & Halász, Á. (2006) · Biophysical Journal · doi:10.1529/biophysj.105.069278 · Cited by 40 · see citing works
Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole‐cell model WM_S288C
Ye, C., Xu, N., Gao, C., Liu, G., Xu, J., Zhang, W., Chen, X., Nielsen, J., & Liu, L. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27298 · Cited by 40 · see citing works
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 · see citing works
Self-citationGuan, N., Du, B., Li, J., Shin, H., Chen, R. R., Du, G., Chen, J., & Liu, L. (2017) · Biotechnology and Bioengineering · doi:10.1002/bit.26478 · Cited by 37 · see citing works
Phylogenomic Analysis Reveals the Evolutionary Route of Resistant Genes in Staphylococcus aureus
John, J., George, S., Nori, S. R. C., & Sathi, S. N. (2019) · Genome Biology and Evolution · doi:10.1093/gbe/evz213 · Cited by 37 · see citing works
Peterson, J. R., Thor, S., Kohler, L., Kohler, P. R. A., Metcalf, W. W., & Luthey-Schulten, Z. A. (2016) · BMC Genomics · doi:10.1186/s12864-016-3219-8 · Cited by 36 · see citing works
Analysis of genetic variation and potential applications in genome-scale metabolic modeling
Cardoso, J. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00013 · Cited by 34 · see citing works
Yang, Z.-K., Luo, H., Zhang, Y., Wang, B., & Gao, F. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty938 · Cited by 34 · see citing works
MEMOSys 2.0: an update of the bioinformatics database for genome-scale models and genomic data
Pabinger, S., Snajder, R., Hardiman, T., Willi, M., Dander, A., & Trajanoski, Z. (2014) · Database · doi:10.1093/database/bau004 · Cited by 25 · see citing works
Expanding the uses of genome‐scale models with protein structures
Mih, N., & Palsson, B. Ø. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188601 · Cited by 12 · see citing works
Self-citationLloyd, C. J., Monk, J. M., Yang, L., Ebrahim, A., & Palsson, B. Ø. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007817 · Cited by 11 · see citing works
Self-citationCautha, S. C., Gowen, C. M., Lussier, F.-X., Gold, N. D., Martin, V. J. J., & Mahadevan, R. (2013) · IFAC Proceedings Volumes · doi:10.3182/20131216-3-in-2044.00066 · Cited by 7 · see citing works
Salvy, P., & Hatzimanikatis, V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.15.204420 · Cited by 7 · see citing works
Lloyd, C. J., Monk, J. M., Yang, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.23.003236 · Cited by 3 · see citing works
Self-citation
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Research works printed but not linked: 2
- [130]Mayr E This is Biology: The Science of the Living World (Harvard University Press, 1998).
- [131]Palsson B Systems Biology (Cambridge University Press, 2015).
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Sorted by citations
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393 · see citing works
Ecological modelling approaches for predicting emergent properties in microbial communities
van den Berg, N. I., Machado, D., Santos, S., Rocha, I., Chacón, J. M., Harcombe, W. R., Mitri, S., & Patil, K. R. (2022) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01746-7 · Cited by 253 · see citing works
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Cho, J. S., Kim, G. B., Eun, H., Moon, C. W., & Lee, S. Y. (2022) · JACS Au · doi:10.1021/jacsau.2c00344 · Cited by 240 · see citing works
Enterococci enhance Clostridioides difficile pathogenesis
Smith, A. B., Jenior, M. L., Keenan, O., Hart, J. L., Specker, J. T., Abbas, A. A., Rangel, P. C., Di, C., Green, J., Bustin, K. A., et al. (2022) · Nature · doi:10.1038/s41586-022-05438-x · Cited by 208 · see citing works
Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data
Passi, A., Tibocha‐Bonilla, J. D., Kumar, M., Tec-Campos, D., Zengler, K., & Zúñiga, C. (2021) · Metabolites · doi:10.3390/metabo12010014 · Cited by 169 · see citing works
A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications
Antoniewicz, M. R. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.11.002 · Cited by 156 · see citing works
Machine learning-enabled retrobiosynthesis of molecules
Yu, T., Boob, A. G., Volk, M., Liu, X., Cui, H., & Zhao, H. (2023) · Nature Catalysis · doi:10.1038/s41929-022-00909-w · Cited by 120 · see citing works
Community standards and future opportunities for synthetic communities in plant–microbiota research
Northen, T. R., Kleiner, M., Torres, M., Kovács, Á. T., Nicolaisen, M. H., Krzyżanowska, D. M., Sharma, S., Lund, G., Jelsbak, L., Baars, O., et al. (2024) · Nature Microbiology · doi:10.1038/s41564-024-01833-4 · Cited by 125 · see citing works
Chen, Y., Gustafsson, J., Rangel, A. E. T., Anton, M., Domenzain, I., Kittikunapong, C., Li, F., Yuan, L., Nielsen, J., & Kerkhoven, E. J. (2024) · Nature Protocols · doi:10.1038/s41596-023-00931-7 · Cited by 117 · see citing works
Automating the design-build-test-learn cycle towards next-generation bacterial cell factories
Gurdo, N., Volke, D. C., McCloskey, D., & Nikel, P. I. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.01.002 · Cited by 117 · see citing works
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Jo, C., Zhang, J., Tam, J. M., Church, G., Khalil, A. S., Segrè, D., & Tang, T.-C. (2023) · Materials Today Bio · doi:10.1016/j.mtbio.2023.100560 · Cited by 89 · see citing works
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Ishchuk, O. P., Domenzain, I., Sánchez, B. J., Muñiz-Paredes, F. R., Martínez, J. L., Nielsen, J., & Petranović, D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2108245119 · Cited by 97 · see citing works
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Machine learning for metabolic pathway optimization: A review
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Kozaeva, E., Volkova, S., Matos, M. R. A., Mezzina, M. P., Wulff, T., Volke, D. C., Nielsen, L. K., & Nikel, P. I. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.07.014 · Cited by 76 · see citing works
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Lim, H. G., Rychel, K., Sastry, A. V., Bentley, G. J., Mueller, J., Schindel, H. S., Larsen, P. E., Laible, P. D., Guss, A. M., Niu, W., et al. (2022) · Metabolic Engineering · doi:10.1016/j.ymben.2022.04.004 · Cited by 66 · see citing works
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Lyu, X., Nuhu, M., Candry, P., Wolfanger, J., Betenbaugh, M., Saldivar, A., Zuniga, C., Wang, Y., & Shrestha, S. (2024) · Journal of Industrial Microbiology and Biotechnology · doi:10.1093/jimb/kuae025 · Cited by 53 · see citing works
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Dawood, A. S., Algharib, S. A., Zhao, G., Zhu, T., Qi, M., Delai, K., Hao, Z., Marawan, M. A., Shirani, I., & Guo, A. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.855731 · Cited by 48 · see citing works
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Mathematical modeling of proteome constraints within metabolism
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Karlsen, S. T., Rau, M. H., Sánchez, B. J., Jensen, K. L., & Zeidan, A. A. (2023) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuad030 · Cited by 40 · see citing works
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Fawaz, A., Ferraresi, A., & Isidoro, C. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13111590 · Cited by 43 · see citing works
Enhancing Microbiome Research through Genome-Scale Metabolic Modeling
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Zampieri, G., Efthimiou, G., & Angione, C. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.01.008 · Cited by 35 · see citing works
Wang, D., Meng, Y., Huang, L., Zhang, X.-X., Luo, X., & Meng, F. (2024) · Water Research · doi:10.1016/j.watres.2024.122356 · Cited by 36 · see citing works
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The complex world of kefir: Structural insights and symbiotic relationships
Cheng, T., Zhang, T., Zhang, P., He, X., Sadiq, F. A., Li, J., Sang, Y., & Gao, J. (2024) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.13364 · Cited by 35 · see citing works
Esvap, E., & Ülgen, K. Ö. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00140 · Cited by 33 · see citing works
Monteiro-Silva, F., Queirós, C., Pereira, M. S. D. L., Pinho, T. M., Barroso, T., Magalhães, S., Boaventura‐Cunha, J., dos Santos, F. N., Cunha, M., & Martins, R. C. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109220 · Cited by 35 · see citing works
Path to improving the life cycle and quality of genome-scale models of metabolism
Seif, Y., & Palsson, B. Ø. (2021) · Cell Systems · doi:10.1016/j.cels.2021.06.005 · Cited by 26 · see citing works
Self-citationConstruction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges
Bi, X., Liu, Y., Li, J., Du, G., Lv, X., & Liu, L. (2022) · Biomolecules · doi:10.3390/biom12050721 · Cited by 31 · see citing works
Environment-specific virocell metabolic reprogramming
Howard‐Varona, C., Lindback, M., Fudyma, J., Krongauz, A., Solonenko, N., Zayed, A. A., Andreopoulos, W., Olson, H. M., Kim, Y., Kyle, J. E., et al. (2024) · The ISME Journal · doi:10.1093/ismejo/wrae055 · Cited by 31 · see citing works
Deciphering and designing microbial communities by genome-scale metabolic modelling
Wu, S., Qu, Z., Chen, D., Wu, H., Caiyin, Q., & Qiao, J. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.055 · Cited by 31 · see citing works
Quantifying the varying harvest of fermentation products from the human gut microbiota
Arnoldini, M., Sharma, R., Moresi, C., Chure, G., Chabbey, J., Slack, E. M. C., & Cremer, J. (2025) · Cell · doi:10.1016/j.cell.2025.07.005 · Cited by 36 · see citing works
Weiss, B., Souza, A. C. O., Constancio, M. T. L., Alvarenga, D. O., Pylro, V. S., Alves, L. M. C., & Varani, A. M. (2021) · Microorganisms · doi:10.3390/microorganisms9050995 · Cited by 31 · see citing works
Enhancing Microbiome Research through Genome-Scale Metabolic Modeling
Ankrah, N. Y. D., Bernstein, D. B., Biggs, M. B., Carey, M. A., Engevik, M. A., García-Jiménez, B., Lakshmanan, M., Pacheco, A. R., Sulheim, S., & Medlock, G. L. (2021) · NORA - Norwegian Open Research Archives · doi:10.3929/ethz-b-000523735 · Cited by 31 · see citing works
Ethanol-lactate transition of Lachancea thermotolerans is linked to nitrogen metabolism
Battjes, J., Melkonian, C., Mendoza, S. N., Haver, A., Al-Nakeeb, K., Koza, A., Schrübbers, L. C., Wagner, M. J., Zeidan, A. A., Douwe Molenaar, B., et al. (2022) · Food Microbiology · doi:10.1016/j.fm.2022.104167 · Cited by 31 · see citing works
Chung, C. H., & Chandrasekaran, S. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac132 · Cited by 30 · see citing works
Recent advances in model-assisted metabolic engineering
Guðmundsson, S., & Nogales, J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.100392 · Cited by 30 · see citing works
Use and limitations of genome-scale metabolic models in food microbiology
Somerville, V., Grigaitis, P., Battjes, J., Moro, F., & Teusink, B. (2021) · Current Opinion in Food Science · doi:10.1016/j.cofs.2021.12.010 · Cited by 30 · see citing works
Microbes with higher metabolic independence are enriched in human gut microbiomes under stressPreprint
Veseli, I., Chen, Y. T., Schechter, M. S., Vanni, C., Fogarty, E. C., Watson, A. R., Jabrì, B., Blekhman, R., Willis, A. D., Yu, M., et al. (2023) · eLife · doi:10.7554/elife.89862 · Cited by 32 · see citing works
Liu, Y., Xue, B., Liu, H., Wang, S., & Su, H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108348 · Cited by 28 · see citing works
A Systematic Comparison of Single-Cell Perturbation Response Prediction Models
Li, L., You, Y., Fu, Y., Liao, W., Fan, X., Lu, S., Cao, Y., Li, B., Ren, W., Kong, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.23.630036 · Cited by 25 · see citing works
“Furthermore, combinatorial multiplexing experiments remain prohibitively expensive and technically challenging to scale ( 7, 8 ).”
Microalgae growth-promoting bacteria for cultivation strategies: Recent updates and progress
., W. P.-A., Sattayawat, P., Inwongwan, S., Cheirsilp, B., Liewtrakula, N., Maneechote, W., Rangseekaew, P., Ahmad, F., Mehmood, M. A., Gao, F., et al. (2024) · Microbiological Research · doi:10.1016/j.micres.2024.127813 · Cited by 26 · see citing works
High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032
Feierabend, M., Renz, A., Zelle, E., Nöh, K., Wiechert, W., & Dräger, A. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.750206 · Cited by 27 · see citing works
Acevedo, A., Torres, F., Kiwi, M., Baeza‐Lehnert, F., Barros, L. F., Lee‐Liu, D., & González-Billault, C. E. (2023) · Aging · doi:10.18632/aging.204663 · Cited by 25 · see citing works
Schroeder, W. L., Suthers, P. F., Willis, T. C., Mooney, E. J., & Maranas, C. D. (2024) · Metabolites · doi:10.3390/metabo14070365 · Cited by 25 · see citing works
Sasikumar, R., Saranya, S. V., Lincy, L. L., Thamanna, L., & Chellapandi, P. (2024) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2024.109978 · Cited by 25 · see citing works
Hof, M. V. ‘., Mohite, O. S., Monk, J. M., Weber, T., Palsson, B. Ø., & Sommer, M. O. A. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05108-9 · Cited by 23 · see citing works
Self-citationBlázquez, B., León, D. S., Rojas, A., Tortajada, M., & Nogales, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24087091 · Cited by 20 · see citing works
Microbes with higher metabolic independence are enriched in human gut microbiomes under stress
Veseli, I., Chen, Y. T., Schechter, M. S., Vanni, C., Fogarty, E. C., Watson, A. R., Jabrì, B., Blekhman, R., Willis, A. D., Yu, M., et al. (2025) · eLife · doi:10.1101/2023.05.10.540289 · Cited by 26 · see citing works
Metabolic modeling of synthetic microbial communities for bioremediation
Wang, L., Wang, X., Wu, H., Wang, H., Wang, Y., & Lü, Z. (2023) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2023.2212569 · Cited by 22 · see citing works
Gong, G., Wu, B., Liu, L., Li, J., & He, M. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108432 · Cited by 22 · see citing works
The bright frontiers of microbial metabolic optogenetics
Wegner, S. A., Barocio-Galindo, R. M., & Avalos, J. L. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2022.102207 · Cited by 22 · see citing works
Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories
Sen, P. (2022) · Biotechnology and Genetic Engineering Reviews · doi:10.1080/02648725.2022.2152631 · Cited by 26 · see citing works
Fakih, I., Got, J., Robles-Rodríguez, C. E., Siegel, A., Forano, É., & Muñoz‐Tamayo, R. (2023) · mSystems · doi:10.1128/msystems.01027-22 · Cited by 22 · see citing works
Hari, A., Doddapaneni, T. R. K. C., & Kikas, T. (2024) · Environmental Research · doi:10.1016/j.envres.2024.118665 · Cited by 21 · see citing works
Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations
Choi, Y.-M., Choi, D., Lee, Y. Q., Koduru, L., Lewis, N. E., Lakshmanan, M., & Lee, D. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.07.025 · Cited by 20 · see citing works
Yang, X., Yang, T., Zhang, Z., Zhang, Y., Mei, X., Gao, Y., Wang, N., Jiang, G., Xu, Y., Shen, Q., et al. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02908-z · Cited by 20 · see citing works
Exploring the Molecular Terrain: A Survey of Analytical Methods for Biological Network Analysis
Nguyen, T.-T., Dao, T.-K., Pham, D.-T., & Duong, T.-H. (2024) · Symmetry · doi:10.3390/sym16040462 · Cited by 20 · see citing works
Sabach, O., Buhnik‐Rosenblau, K., Kesten, I., Freilich, S., Freilich, S., & Kashi, Y. (2023) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2023.110402 · Cited by 19 · see citing works
Unifying multi-sample network inference from prior knowledge and omics data with CORNETO
Rodríguez-Mier, P., Garrido‐Rodríguez, M., Gábor, A., & Sáez-Rodríguez, J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01069-9 · Cited by 19 · see citing works
Systems-Level Modeling for CRISPR-Based Metabolic Engineering
Cardiff, R. A. L., Carothers, J. M., Zalatan, J. G., & Sauro, H. M. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00053 · Cited by 21 · see citing works
Applications of targeted proteomics in metabolic engineering: advances and opportunities
Yunus, I. S., & Lee, T. S. (2022) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102709 · Cited by 18 · see citing works
Biofoundries: Bridging Automation and Biomanufacturing in Synthetic Biology
Lee, D., Kim, H., Sung, B. H., Cho, B., & Lee, S. (2023) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-023-0226-x · Cited by 18 · see citing works
On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers
Vázquez-Jiménez, A., de León, U. A.-P., Matadamas-Guzman, M. L., Muciño-Olmos, E. A., Martínez-López, Y. E., Escobedo-Tapia, T., & Reséndis-Antonio, O. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.705646 · Cited by 18 · see citing works
Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation
Dahal, S., Renz, A., Dräger, A., & Yang, L. (2023) · Communications Biology · doi:10.1038/s42003-023-04540-8 · Cited by 18 · see citing works
Systems metabolic engineering of Corynebacterium glutamicum for efficient l-tryptophan production
Dong, Y., & Chen, Z. (2025) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2025.02.002 · Cited by 17 · see citing works
Genetic and metabolic engineering of Methanococcus spp
Li, J., Akinyemi, T. S., Shao, N., Chen, C., Dong, X., Liu, Y., & Whitman, W. B. (2022) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2022.11.002 · Cited by 17 · see citing works
Yao, H., Dahal, S., & Yang, L. (2023) · Microbial Cell Factories · doi:10.1186/s12934-022-02004-y · Cited by 17 · see citing works
Leveraging genome-scale metabolic models to understand aerobic methanotrophs
Wutkowska, M., Tláskal, V., Bordel, S., Stein, L. Y., Nweze, J. A., & Daebeler, A. (2024) · The ISME Journal · doi:10.1093/ismejo/wrae102 · Cited by 17 · see citing works
Devika, N. T., Jangam, A. K., Katneni, V. K., Patil, P. K., Nathamuni, S. P., & Shekhar, M. S. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.752477 · Cited by 17 · see citing works
Advances in biorenewables-resource-waste systems and modelling
Guo, M., Wu, C., Chapman, S. P., Yu, X., Vinestock, T., Hastings, A., Smith, P., & Shah, N. (2023) · Carbon Capture Science & Technology · doi:10.1016/j.ccst.2023.100142 · Cited by 16 · see citing works
de Almeida, E. L. M., Kerkhoven, E. J., & da Silveira, W. B. (2024) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-024-00009-5 · Cited by 16 · see citing works
Goh, K. J., Stubenrauch, C. J., & Lithgow, T. J. (2024) · EMBO Reports · doi:10.1038/s44319-024-00111-y · Cited by 16 · see citing works
Characterization of effects of genetic variants via genome-scale metabolic modelling
Tong, H., Küken, A., Razaghi‐Moghadam, Z., & Nikoloski, Z. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03844-4 · Cited by 16 · see citing works
A structural property for reduction of biochemical networks
Küken, A., Wendering, P., Langary, D., & Nikoloski, Z. (2021) · Scientific Reports · doi:10.1038/s41598-021-96835-1 · Cited by 15 · see citing works
Characterising functional redundancy in microbiome communities via relative entropy
Fässler, D., Heinken, A., & Hertel, J. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.012 · Cited by 16 · see citing works
Liebal, U. W., Ullmann, L., Lieven, C., Kohl, P., Wibberg, D., Zambanini, T., & Blank, L. M. (2022) · Journal of Fungi · doi:10.3390/jof8050524 · Cited by 15 · see citing works
Salusjärvi, L., Ojala, L. S., Gopalacharyulu, P. V., Lienemann, M., Jouhten, P., Pitkänen, J., & Toivari, M. H. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.989481 · Cited by 15 · see citing works
Ghiotto, G., Detman, A., Chojnacka, A., Orellana, E., Bernardini, N., Fraulini, S., Treu, L., Sikora, A., & Campanaro, S. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.151390 · Cited by 15 · see citing works
Gawthrop, P. J., Pan, M., & Crampin, E. J. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0478 · Cited by 15 · see citing works
- Authors
- Fang, X., Lloyd, C. J., & Palsson, B. O.
- Affiliations
- University of California San Diego (US)
- Venue
- Nature Reviews Microbiology
- Published
- 2020-09-21
- Volume
- 18
- Issue
- 12
- Pages
- 731-743
- ISSN
- 1740-1526 (ISSN-L)
- 1740-1534
- PMC
- PMC7981288
- OpenAlex
- W3087744872
- Cited by
- 292
- References
- 142
- Citations by year
- 2026: 34 · 2025: 49 · 2024: 66 · 2023: 60 · 2022: 35 · 2021: 47 · 2020: 1
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Microbiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Funders
- National Institute of General Medical Sciences (R01GM057089)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- genome-scale metabolic models, Escherichia coli, metabolic network reconstruction, pangenome analysis, proteome allocation, phenotype prediction
- MeSH terms
- Firmicutes, Bacillota, Whole Genome Sequencing, Thermotoga, Cyanobacteria, Computer Simulation, Escherichia coli, Models, Genetic, Phenotype, Stress, Physiological, Genome, Bacterial, Proteobacteria, Genomics, Actinobacteria, Gene Regulatory Networks, Metabolic Networks and Pathways
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