Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth
Abstract
Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
During scale-up of microbial processes, cells are exposed to altered environmental conditions and environmental stress regimes.
“Whereas small-scale cultivations and initial engineering steps are often performed in complex media in batch culture, during the scale-up of the process, cells are exposed to altered environmental conditions and environmental stress regimes [57,58].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [57]
Cites
The complete genome sequence of Escherichia coli K-12.
Blattner, F., Plunkett, G., Bloch, C., Perna, N., Burland, V., Riley, M., Collado-Vides, J., Glasner, J. D., Rode, C., Mayhew, G., et al. (1997) · Science · doi:10.1126/science.277.5331.1453 · Cited by 7,996 · see citing works
Understanding the control of metabolism
(1997) · Choice Reviews Online · doi:10.5860/choice.34-6252 · Cited by 1,230 · see citing works
Varma, A., & Palsson, B. Ø. (1994) · Applied and Environmental Microbiology · doi:10.1128/aem.60.10.3724-3731.1994 · Cited by 1,058 · see citing works
Self-citationMetabolic Flux Balancing: Basic Concepts, Scientific and Practical Use
Varma, A., & Palsson, B. Ø. (1994) · Nature Biotechnology · doi:10.1038/nbt1094-994 · Cited by 959 · see citing works
Self-citationSchuster, S., Fell, D. A., & Dandekar, T. (2000) · Nature Biotechnology · doi:10.1038/73786 · Cited by 947 · see citing works
Edwards, J. S., Ibarra, R. U., & Palsson, B. Ø. (2001) · Nature Biotechnology · doi:10.1038/84379 · Cited by 941 · see citing works
Self-citationEdwards, J. S., & Palsson, B. O. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5528 · Cited by 875 · see citing works
Self-citationE-CELL: software environment for whole-cell simulation
Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T. S., Matsuzaki, Y., Miyoshi, F., Saito, K., Tanida, S., Yugi, K., Venter, J. C., et al. (1999) · Bioinformatics · doi:10.1093/bioinformatics/15.1.72 · Cited by 764 · see citing works
Karp, P. D. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.56 · Cited by 451 · see citing works
WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction
Overbeek, R. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.123 · Cited by 395 · see citing works
Flux analysis of underdetermined metabolic networks: the quest for the missing constraints
Bonarius, H. P. J., Schmid, G., & TRAMPER, J. (1997) · Trends in biotechnology · doi:10.1016/s0167-7799(97)01067-6 · Cited by 332 · see citing works
Genome-Scale Metabolic Model of Helicobacter pylori 26695
Schilling, C. H., Covert, M. W., Famili, I., Church, G. M., Edwards, J. S., & Palsson, B. Ø. (2002) · Journal of Bacteriology · doi:10.1128/jb.184.16.4582-4593.2002 · Cited by 326 · see citing works
Self-citationMetabolic modeling of microbial strains in silico
Covert, M. W., Schilling, C. H., Famili, I., Edwards, J. S., Goryanin, I., Selkov, E., & Palsson, B. Ø. (2001) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(00)01754-0 · Cited by 312 · see citing works
Self-citationThe challenges of in silico biology
Palsson, B. Ø. (2000) · Nature Biotechnology · doi:10.1038/81125 · Cited by 306 · see citing works
Self-citationModeling and simulation: tools for metabolic engineering
Wiechert, W. (2002) · Journal of Biotechnology · doi:10.1016/s0168-1656(01)00418-7 · Cited by 206 · see citing works
Mathematical modelling of metabolism
Gombert, A. K., & NIELSEN, J. K. (2000) · Current Opinion in Biotechnology · doi:10.1016/s0958-1669(00)00079-3 · Cited by 188 · see citing works
Characterizing the metabolic phenotype: A phenotype phase plane analysis
Edwards, J. S., Ramakrishna, R., & Palsson, B. Ø. (2001) · Biotechnology and Bioengineering · doi:10.1002/bit.10047 · Cited by 173 · see citing works
Self-citationA reconstruction of the metabolism of Methanococcus jannaschii from sequence data
Selkov, E., Maltsev, N., Olsen, G. J., Overbeek, R., & Whitman, W. B. (1997) · Gene · doi:10.1016/s0378-1119(97)00307-7 · Cited by 117 · see citing works
Multiple mechanisms controlling carbon metabolism in bacteria
Saier, M. H. (1998) · Biotechnology and Bioengineering · doi:10.1002/(sici)1097-0290(19980420)58:2/3 · Cited by 97 · see citing works
Borth, N., Zeyda, M., & Katinger, H. W. D. (2000) · Biotechnology and Bioengineering · doi:10.1002/1097-0290(2000)71:4 · Cited by 86 · see citing works
Self-citationWeikert, C., Sauer, U., & Bailey, J. E. (1997) · Microbiology · doi:10.1099/00221287-143-5-1567 · Cited by 63 · see citing works
The Department of Energy Microbial Cell Project: A 180° Paradigm Shift for Biology
Drell, D. W. (2002) · OMICS A Journal of Integrative Biology · doi:10.1089/15362310252780799 · Cited by 12 · see citing works
Flagged first, then by citations
1 of the 23 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903 · see citing works
“, which allow the flux for each reaction to be calculated, have recently significantly improved our ability to make quantifiable predictions on the relative importance of various reactions, giving rise to experimentally testable hypothese”
BackgroundAbsolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli
Bennett, B. D., Kimball, E. H., Gao, M., Osterhout, R., Van Dien, S. J., & Rabinowitz, J. D. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.186 · Cited by 1,830 · see citing works
“These include glutamate and αketoglutarate (23) competing for the active site of aspartate aminotransferase (with Km of 0.”
BackgroundGlobal reconstruction of the human metabolic network based on genomic and bibliomic data
Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610772104 · Cited by 1,374 · see citing works
Self-citationEvolution 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 · see citing works
Engineering Cellular Metabolism
Nielsen, J., & Keasling, J. D. (2016) · Cell · doi:10.1016/j.cell.2016.02.004 · Cited by 1,400 · see citing works
Feist, 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
“…in the context of generating maximal amounts of biomass precursors (i.e., simulated optimal growth) from available media substrates using FBA can generate results that are consistent with experimental data (Edwards et al, 2001; Covert and Palsson, 2002; Ibarra et al, 2002; Covert et al, 2004).”
ResultSelf-citationBurgard, A. P., Pharkya, P., & Maranas, C. D. (2003) · Biotechnology and Bioengineering · doi:10.1002/bit.10803 · Cited by 1,262 · see citing works
“Interestingly, recent work suggests that even when FBA predictions under the biomass maximization assumption seem to fail, metabolic networks can be evolved, for certain cases, towards maximum growth (i.e., biomass yield) through adaptive evolution (Ibarra et al., 2002).”
BackgroundThe effects of alternate optimal solutions in constraint-based genome-scale metabolic models
Mahadevan, R., & Schilling, C. H. (2003) · Metabolic Engineering · doi:10.1016/j.ymben.2003.09.002 · Cited by 1,203 · 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
“…30,31 ; second,the determination of likely physiological states by choosing the objective function that represents probable physiological functions 32,33 such as biomass or ATP production; and third, the design of strains 34–36 to satisfy an engineering goal such as the improved production of a…”
BackgroundInfluentialSelf-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
“An objective function with a combination of the metabolic precursors, energy and redox potential required for the production of biomass has proven useful in predicting in vivo cellular behavior [9,10,25,26].”
BackgroundMethodSelf-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
“Studies have shown that, by optimizing the assumed cellular objectives of growth 27 and energy use 36,37 , one can predict metabolic fluxes in microorganisms.”
BackgroundSelf-citationAdaptive laboratory evolution—Principles and applications for biotechnology
Dragosits, M., & Mattanovich, D. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-64 · Cited by 827 · see citing works
“Subsequently, it was shown that suboptimal growth on glycerol can approach its theoretical maximum during laboratory evolution [65].”
BackgroundKawecki, T. J., Lenski, R. E., Ebert, D., Hollis, B., Olivieri, I., & Whitlock, M. C. (2012) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2012.06.001 · Cited by 807 · see citing works
Optimality and evolutionary tuning of the expression level of a protein
Dekel, E., & Alon, U. (2005) · Nature · doi:10.1038/nature03842 · Cited by 766 · see citing works
Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models
Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Adkins, J., Schramm, G., Purvine, S. O., López‐Ferrer, D., et al. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.47 · Cited by 757 · see citing works
“In addition, the measured growth rates of evolved strains were shown to be consistent with most growth rate predictions from an in silico genome-scale metabolic model (GEM) of Escherichia coli (Ibarra et al , 2002; Fong and Palsson, 2004).”
InfluentialSelf-citationAdvances in flux balance analysis
Kauffman, K. J., Prakash, P., & Edwards, J. S. (2003) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2003.08.001 · Cited by 744 · see citing works
Self-citationShifts in growth strategies reflect tradeoffs in cellular economics
Molenaar, D., van Berlo, R., de Ridder, D., & Teusink, B. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.82 · Cited by 653 · see citing works
“The latter two phenomena are also observed in fast growing tumour cells and cell lines.”
BackgroundInfluentialUsing 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-citationGlobal organization of metabolic fluxes in the bacterium Escherichia coli
Almaas, E., Kovács, B., Vicsek, T., Oltvai, Z. N., & Barabási, A.-Ĺ. (2004) · Nature · doi:10.1038/nature02289 · Cited by 710 · see citing works
Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli
Schuetz, R. J., Kuepfer, L., & Sauer, U. (2007) · Molecular Systems Biology · doi:10.1038/msb4100162 · Cited by 684 · see citing works
“Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli”
Cleaning up with genomics: applying molecular biology to bioremediation
Lovley, D. R. (2003) · Nature Reviews Microbiology · doi:10.1038/nrmicro731 · Cited by 616 · see citing works
“Rather, it is assumed that the cell optimizes the network of cellular reactions to synthesize all of the cellular components that are needed for growth and survival to achieve a particular goal — often defined as the maximum growth rat”
BackgroundLee, S. K., Chou, H., Ham, T. S., Lee, T. S., & Keasling, J. D. (2008) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2008.10.014 · Cited by 616 · see citing works
The 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-citationContext-Specific Metabolic Networks Are Consistent with Experiments
Becker, S. A., & Palsson, B. Ø. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000082 · Cited by 594 · see citing works
“However, after selective pressure is applied to maximize growth, the actual growth has been shown to improve to match the prediction, typically after a month or two of serial passage of cells [16].”
BackgroundSelf-citationBajad, S. U., Lu, W., Kimball, E., Yuan, J., Peterson, C. N., & Rabinowitz, J. D. (2006) · Journal of Chromatography A · doi:10.1016/j.chroma.2006.05.019 · Cited by 593 · see citing works
Sandberg, T. E., Salazar, M. J., Weng, L. L., Palsson, B. Ø., & Feist, A. M. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2019.08.004 · Cited by 558 · see citing works
Self-citationThe evolution of molecular biology into systems biology
Westerhoff, H. V., & Palsson, B. Ø. (2004) · Nature Biotechnology · doi:10.1038/nbt1020 · Cited by 566 · see citing works
Self-citationThe 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 · see citing works
Self-citationMetabolomics and systems biology: making sense of the soup
KELL, D. (2004) · Current Opinion in Microbiology · doi:10.1016/j.mib.2004.04.012 · Cited by 529 · see citing works
Just-in-time transcription program in metabolic pathways
Zaslaver, A., Mayo, A., Rosenberg, R., Bashkin, P., Sberro, H., Tsalyuk, M., Surette, M. G., & Alon, U. S. (2004) · Nature Genetics · doi:10.1038/ng1348 · Cited by 471 · see citing works
“coli is carried out by well-characterized enzymatic pathway”
BackgroundRegulatory on/off minimization of metabolic flux changes after genetic perturbations
Shlomi, T., Berkman, O., & Ruppin, E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0406346102 · Cited by 458 · see citing works
Connecting extracellular metabolomic measurements to intracellular flux states in yeast
Mo, M. L., Palsson, B. Ø., & Herrgård, M. J. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-37 · Cited by 452 · see citing works
“These methods have been extensively used to study genome-scale metabolic networks and have successfully predicted, for example, optimal metabolic states, gene deletion lethality, and adaptive evolutionary endpoints [14-16].”
Self-citationEvolutionary programming as a platform for in silico metabolic engineering
Patil, K. R., Rocha, I., Förster, J., & Nielsen, J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-308 · Cited by 455 · see citing works
Multidimensional Optimality of Microbial Metabolism
Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M., & Sauer, U. (2012) · Science · doi:10.1126/science.1216882 · Cited by 420 · see citing works
Flux balance analysis of biological systems: applications and challenges
Raman, K., & Chandra, N. R. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp011 · Cited by 430 · see citing works
“of biomass [19], although under some conditions, the”
BackgroundEnergy Constraints on the Evolution of Gene Expression
Wagner, A. (2005) · Molecular Biology and Evolution · doi:10.1093/molbev/msi126 · Cited by 419 · see citing works
“This is vividly illustrated by recent work that analyzes how the input into a metabolic reaction network and the output produced by the network can affect cell growth (Ibarra, Edwards, and Palsson 2002; Segre, Vitkup, and Church 2002).”
BackgroundThe Systemic Imprint of Growth and Its Uses in Ecological (Meta)Genomics
Vieira‐Silva, S., & Rocha, E. P. C. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000808 · Cited by 408 · see citing works
Machado, D., & Herrgård, M. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003580 · Cited by 403 · see citing works
“Although the validity of this assumption has been experimentally confirmed under some conditions [51], there are cases (such as overflow metabolism) where this assumption is not valid.”
BackgroundBeg, Q. K., Vazquez, A., Ernst, J., de Menezes, M. A., Bar-Joseph, Z., Barabási, A.-L., & Oltvai, Z. N. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609845104 · Cited by 368 · see citing works
Genome-scale microbial in silico models: the constraints-based approach
Price, N. D., Papin, J. A., Schilling, C. H., & Palsson, B. Ø. (2003) · Trends in biotechnology · doi:10.1016/s0167-7799(03)00030-1 · Cited by 401 · see citing works
Self-citationGraph theory and networks in Biology
Mason, O., & Verwoerd, M. (2007) · IET Systems Biology · doi:10.1049/iet-syb:20060038 · Cited by 399 · see citing works
“Further results of this nature have been reported in [129].”
OptFlux: an open-source software platform for in silico metabolic engineering
Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., Pinto, J. P., Nielsen, J., Patil, K. R., Ferreira, E. C., & Rocha, M. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-45 · Cited by 388 · see citing works
“…flux over the reaction that represents biomass formation is the one being maximized, since this has proven to be a good representation of the natural behaviour of microorganisms in many circum-stances [31], but it is possible to perform simulations by maximizing or minimizing any flux of the model.”
Selection and optimization of microbial hosts for biofuels production
Fischer, C. R., Klein‐Marcuschamer, D., & Stephanopoulos, G. N. (2008) · Metabolic Engineering · doi:10.1016/j.ymben.2008.06.009 · Cited by 384 · see citing works
Herring, C. D., Raghunathan, A., Honisch, C., Patel, T., Applebee, M. K., Joyce, A. R., Albert, T. J., Blattner, F. R., van den Boom, D., Cantor, C. R., et al. (2006) · Nature Genetics · doi:10.1038/ng1906 · Cited by 381 · see citing works
“Growth of the sequenced strain MG1655 has been observed to differ from computational predictions based on flux balance analysi”
BackgroundSelf-citationFlux Coupling Analysis of Genome-Scale Metabolic Network ReconstructionsPreprint
Burgard, A. P., Nikolaev, E. V., Schilling, C. H., & Maranas, C. D. (2004) · Genome Research · doi:10.1101/gr.1926504 · Cited by 383 · see citing works
“…secretion under increasingly anaerobic conditions (Varma et al. 1993); (3) quantitatively predicting cellular growth rates (Edwards et al. 2001; Ibarra et al. 2002); (4) assessing the performance limits of metabolic networks in response to gene additions or deletions (Bur-gard and Maranas…”
BackgroundMetabolic engineering of algae for fourth generation biofuels production
Lu, J. J., Sheahan, C., & Fu, P. (2011) · Energy & Environmental Science · doi:10.1039/c0ee00593b · Cited by 371 · see citing works
Famili, I., Förster, J., Nielsen, J., & Palsson, B. O. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2235812100 · Cited by 353 · see citing works
Self-citationIn silico design and adaptive evolution of Escherichia coli for production of lactic acid
Fong, S. S., Burgard, A. P., Herring, C. D., Knight, E. M., Blattner, F. R., Maranas, C. D., & Palsson, B. Ø. (2005) · Biotechnology and Bioengineering · doi:10.1002/bit.20542 · Cited by 363 · see citing works
“…Escherichia coli, this systems-level approach has been shown to be reasonably accurate in its ability to describe the endpoint growth phenotypes after adaptive evolution for both wild-type (Fong et al., 2003; Ibarra et al., 2002) and single gene deletion strains (Fong and Palsson, 2004) of E. coli.”
BackgroundMethodSelf-citationTowards multidimensional genome annotation
Reed, J. L., Famili, I., Thiele, I., & Palsson, B. Ø. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1769 · Cited by 362 · see citing works
Self-citationPharkya, P., & Maranas, C. D. (2005) · Metabolic Engineering · doi:10.1016/j.ymben.2005.08.003 · Cited by 329 · see citing works
“In the case of biomass maximization, this leads to, under certain situations , a highly desirable coupling of biomass formation to targeted biochemical production enabling the use of adaptive evolution for strain design (Ibarra et al., 2002; Fong and Palsson, 2004).”
BackgroundGenome-scale models of bacterial metabolism: reconstruction and applications
Durot, M., Bourguignon, P.-Y., & Schächter, V. (2008) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2008.00146.x · Cited by 328 · see citing works
“First, the assumption of optimal substrate utilization can be questionable for growth predictions on environments that are not commonly encountered by the organism (Ibarra et al ., 2002; Schuster et al ., 2008).”
BackgroundZhuang, 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
“Genome-scale flux balance analysis models of pure cultures, such as Escherichia coli (Ibarra et al., 2002; Reed and Palsson, 2003; Reed et al., 2003; Feist et al., 2007; Feist and Palsson, 2008) and Geobacter species (Mahadevan et al.”
BackgroundBasic 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
“…GEMs (Palsson, 2006) has allowed researchers to rapidly predict growth phenotypes in various genetic (Fong and Palsson, 2004) and environmental (Ibarra et al , 2002) conditions, explore different objectives of microbial metabolism (Schuetz et al , 2007; Ow et al , 2009) to examine the driving…”
BackgroundSelf-citationFischer, E., & Sauer, U. (2005) · Nature Genetics · doi:10.1038/ng1555 · Cited by 318 · see citing works
“, although adaptation can drive network operation toward this objectiv”
BackgroundThirteen Years of Building Constraint-Based In Silico Models of Escherichia coli
Reed, J. L., & Palsson, B. Ø. (2003) · Journal of Bacteriology · doi:10.1128/jb.185.9.2692-2699.2003 · Cited by 302 · see citing works
“It has also been found that some carbon sources do no initially support optimal behavior, although limited adaptive evolution data do suggest that over time E. coli adjusts its metabolic fluxes to find the optimal solution (26; S. S. Fong and B. O. Palsson, unpublished data).”
BackgroundMethodInfluentialSelf-citationMicrobial Propionic Acid Production
González-García, R. A., McCubbin, T., Navone, L., Stowers, C. C., Nielsen, L. K., & Marcellin, E. (2017) · Fermentation · doi:10.3390/fermentation3020021 · Cited by 302 · see citing works
“Energy maximisation is consistent with the evolutionary drive to maximise growth, which can approximate the behavior of microorganisms [35].”
BackgroundNetwork Context and Selection in the Evolution to Enzyme Specificity
Nam, H., Lewis, N. E., Lerman, J. A., Lee, D.-H., Chang, R. L., Kim, D., & Palsson, B. O. (2012) · Science · doi:10.1126/science.1216861 · Cited by 277 · see citing works
Self-citationFong, S. S., & Palsson, B. Ø. (2004) · Nature Genetics · doi:10.1038/ng1432 · Cited by 297 · see citing works
Self-citationDistributed robustness versus redundancy as causes of mutational robustness
Wagner, A. (2005) · BioEssays · doi:10.1002/bies.20170 · Cited by 290 · see citing works
“However, within 40 days or 700 generations of evolution in the laboratory, the strain increases growth substantially, and thegrowth rateapproaches that predictedby flux balance analysis as maximal.((43))”
BackgroundTeusink, B., Wiersma, A., Douwe Molenaar, B., Francke, C., de Vos, W. M., Siezen, R. J., & Smid, E. J. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m606263200 · Cited by 283 · see citing works
Microbial laboratory evolution in the era of genome‐scale science
Conrad, T. M., Lewis, N. E., & Palsson, B. Ø. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.42 · Cited by 279 · see citing works
“When E. coli growth is not consistent with the LO, populations migrate toward the LO through adaptive evolution (Ibarra et al, 2002; Figure 2C).”
BackgroundSelf-citationMinimal cut sets in biochemical reaction networks
Klamt, S., & Gilles, E. D. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg395 · Cited by 270 · see citing works
“Systems biology (Kitano, 2002) aims at investigating cellular networks by combining experiments and mathematical modeling and computer simulations.”
BackgroundOrth, J. D., Fleming, R. M. T., & Palsson, B. Ø. (2010) · EcoSal Plus · doi:10.1128/ecosalplus.10.2.1 · Cited by 275 · see citing works
“Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations (18, 19), prediction of growth phenotypes after adaptive evolution (20), and prediction of essential genes (21).”
BackgroundSelf-citationFong, S. S., Joyce, A. R., & Palsson, B. Ø. (2005) · Genome Research · doi:10.1101/gr.3832305 · Cited by 265 · see citing works
“Seven parallel evolution experiments (indexed as Lac2, Lac3, LacA, LacB, LacC, LacD, and LacE) were conducted on L-lactate-supplemented M9 minimal medium at 30°C for 60 days (>1000 generations) and seven parallel evolution experiments (Gly1, Gly2, GlyA, GlyB, GlyC, GlyD, and GlyE) were conducted on glycerol-supplement…”
BackgroundMethodInfluentialSelf-citationImproved vanillin production in baker's yeast through in silico design
Brochado, A. R., Matos, C., Møller, B. L., Hansen, J., Mortensen, U. H., & Patil, K. R. (2010) · Microbial Cell Factories · doi:10.1186/1475-2859-9-84 · Cited by 268 · see citing works
“FBA has been successfully applied to predict gene essentiality [21,22], end point of adaptive evolution experiments [23] and optimal metabolic states under given environmental conditions [19].”
MethodPrinciples of transcriptional control in the metabolic network of Saccharomyces cerevisiae
Ihmels, J., Levy, R., & Barkai, N. (2003) · Nature Biotechnology · doi:10.1038/nbt918 · Cited by 265 · see citing works
“Moreover, the flux is readily optimized to maximize metabolic efficiency under different condition”
BackgroundLaCroix, R. A., Sandberg, T. E., O'Brien, E. J., Utrilla, J., Ebrahim, A., Guzmán, G. I., Szubin, R., Palsson, B. Ø., & Feist, A. M. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.02246-14 · Cited by 254 · see citing works
Self-citationA genome-scale metabolic reconstruction of Pseudomonas putida KT2440: i JN746 as a cell factory
Nogales, J., Palsson, B. Ø., & Thiele, I. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-79 · Cited by 261 · see citing works
“missing gene annotations [45]); ii) prediction of the outcome of adaptive evolution [46-48]; iii) design of engineered production strains [49]; and iv) the understanding of topological features of metabolic networks [50-53].”
Self-citationKinetic flux profiling for quantitation of cellular metabolic fluxes
Yuan, J., Bennett, B. D., & Rabinowitz, J. D. (2008) · Nature Protocols · doi:10.1038/nprot.2008.131 · Cited by 256 · see citing works
A 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
“In bacterial systems, the use of a biomass objective function (Feist and Palsson, 2010) has come to dominate, as experimental work shows cells evolving toward optimal states as predicted by maximization of biomass production (Edwards et al., 2001; Ibarra et al., 2002; Lewis et al., 2010).”
Self-citationTyson, G. W., Lo, I., Baker, B. J., Allen, E. E., Hugenholtz, P., & Banfield, J. F. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.10.6319-6324.2005 · Cited by 256 · see citing works
“To date, genome sequences have only been used to optimize growth conditions of cultivated organisms such as Escherichia coli (13, 16).”
Nielsen, J. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044757 · Cited by 252 · see citing works
“coli could be evolved to increase their specific growth rate on glycerol, reaching a value consistent with the model prediction (56).”
BackgroundResultBecker, S. A., & Palsson, B. Ø. (2005) · BMC Microbiology · doi:10.1186/1471-2180-5-8 · Cited by 251 · see citing works
Self-citationModel-driven evaluation of the production potential for growth-coupled products of Escherichia coli
Feist, A. M., Zielinski, D. C., Orth, J. D., Schellenberger, J., Herrgard, M. J., & Palsson, B. Ø. (2010) · Metabolic Engineering · doi:10.1016/j.ymben.2009.10.003 · Cited by 238 · see citing works
Self-citationLarge-scale reconstruction and phylogenetic analysis of metabolic environments
Borenstein, E., Kupiec, M., Feldman, M. W., & Ruppin, E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806162105 · Cited by 238 · see citing works
“Furthermore, as metabolic networks function within the context of biochemical environments and interact with these environments by taking up or secreting various organic and inorganic compounds, previous studies have also addressed the effect that these environmental interactions have on the metabolic process, as mani…”
BackgroundNonoptimal microbial response to antibiotics underlies suppressive drug interactions
Bollenbach, T., Quan, S., Chait, R., & Kishony, R. (2009) · Cell · doi:10.1016/j.cell.2009.10.025 · Cited by 227 · see citing works
“It has been argued that many aspects of bacterial physiology have evolved to be 'optimal' -namely to maximize growth rate in a given condition (Dekel and Alon, 2005; Ibarra et al., 2002; Liebermeister et al., 2004).”
BackgroundJensen, P. A., & Papin, J. A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq702 · Cited by 226 · see citing works
Advances in combating fungal diseases: vaccines on the threshold
Cutler, J. E., Deepe, G. S., & Klein, B. S. (2006) · Nature Reviews Microbiology · doi:10.1038/nrmicro1537 · Cited by 223 · see citing works
Soupène, E., van Heeswijk, W. C., PLUMBRIDGE, J. A., Stewart, V. J., Bertenthal, D., Lee, H., Gyaneshwar, P., Paliy, O., Charernnoppakul, P., & Kustu, S. G. (2003) · Journal of Bacteriology · doi:10.1128/jb.185.18.5611-5626.2003 · Cited by 216 · see citing works
“ilvG Growth inhibited by valine (31) rfb-50 Sensitive to phage P1 rph-1 Partial pyrimidine requirement fnr-267 Defects in anaerobic respirations eut Unable to grow on ethanolamine as the carbon or nitrogen source ? Slow growth on galactose ? Slow growth on glycerol (24) ? Poor motility (32)”
BackgroundA metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)
Dukovski, I., Bajić, D., Chacón, J. M., Quintin, M., Vila, J. C. C., Sulheim, S., Pacheco, A. R., Bernstein, D. B., Riehl, W. J., Korolev, K. S., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00593-3 · Cited by 222 · see citing works
Expanding the metabolic engineering toolbox: more options to engineer cells
Tyo, K. E. J., Alper, H. S., & Stephanopoulos, G. N. (2007) · Trends in biotechnology · doi:10.1016/j.tibtech.2007.01.003 · Cited by 215 · see citing works
“Although these models are being developed for simpler organisms, such as bacteria and yeast [ 33 ,34], they can be expanded to higher cellular systems [35] .A nE. coli metabolic model has recently been successful in predicting a triple gene knockout for improving lycopene production that would have been intractable to…”
MethodGrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions
Kumar, V. S., & Maranas, C. D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000308 · Cited by 207 · see citing works
“, presence or absence) of single and/ or multiple knockout mutants for a variety of substrates against experimental data [5–7].”
BackgroundGenome-based design of a cell-free culture medium for Tropheryma whipplei
Renesto, P., Crapoulet, N., Ogata, H., Scola, B. L., Vestris, G., Claverie, J., & Raoult, D. (2003) · The Lancet · doi:10.1016/s0140-6736(03)14071-8 · Cited by 203 · see citing works
Reed, J. L., & Palsson, B. Ø. (2004) · Genome Research · doi:10.1101/gr.2546004 · Cited by 204 · see citing works
Self-citationIntegration of gene expression data into genome-scale metabolic models
Åkesson, M., Förster, J., & Nielsen, J. (2004) · Metabolic Engineering · doi:10.1016/j.ymben.2003.12.002 · Cited by 203 · see citing works
Plasticity of the cis-Regulatory Input Function of a Gene
Mayo, A., Setty, Y., Shavit, S., Zaslaver, A., & Alon, U. S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040045 · Cited by 196 · see citing works
“This question may also be further studied experimentally [22,23,25,54,55] by evolving bacteria in defined environments that favor different input functions.”
BackgroundThe genome sequence of the capnophilic rumen bacterium Mannheimia succiniciproducens
Hong, S. H., Kim, J. S., Lee, S. Y., In, Y. H., Choi, S. S., Rih, J.-K., Kim, C., Jeong, H., Hur, C. G., & Kim, J. J. (2004) · Nature Biotechnology · doi:10.1038/nbt1010 · Cited by 199 · see citing works
Impact of gene expression noise on organismal fitness and the efficacy of natural selection
Wang, Z., & Zhang, J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1100059108 · Cited by 194 · see citing works
Metagrowth: a new resource for the building of metabolic hypotheses in microbiology
Ogata, H. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki042 · Cited by 197 · see citing works
Fong, S. S., Nanchen, A., Palsson, B. Ø., & Sauer, U. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m510016200 · Cited by 189 · see citing works
Self-citationHeckmann, D., Schulze, S., Denton, A. K., Gowik, U., Westhoff, P., Weber, A. P. M., & Lercher, M. J. (2013) · Cell · doi:10.1016/j.cell.2013.04.058 · Cited by 186 · see citing works
Is maximization of molar yield in metabolic networks favoured by evolution?
Schuster, S., Pfeiffer, T., & Fell, D. A. (2007) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2007.12.008 · Cited by 184 · see citing works
“…systems of increasing complexity and coined the term ''flux balance analysis'' (Varma and Palsson, 1993a, b, 1994; Edwards and Palsson, 1999; Edwards et al., 2001, 2002; Ibarra et al., 2002; Fong and ARTICLE IN PRESS www.elsevier.com/locate/yjtbi 0022-5193/$ -see front matter r 2007 Elsevier Ltd.”
BackgroundBioLogic gates enable logical transcription control in mammalian cells
Kramer, B. P., Fischer, C., & Fussenegger, M. (2004) · Biotechnology and Bioengineering · doi:10.1002/bit.20142 · Cited by 184 · see citing works
“For optimal molecular interventions in complex regulatory networks orchestrating the cellular transcriptome in space and time (Ibarra et al., 2002; Aebersold and Mann, 2003), we have constructed a variety of switchboard-type transgene control units.”
MethodFormulating genome‐scale kinetic models in the post‐genome era
Jamshidi, N., & Palsson, B. Ø. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.8 · Cited by 180 · see citing works
“It has thus been used productively for the analysis of distal causation (Ibarra et al , 2002; Fong and Palsson, 2004; Pal et al , 2006; Harrison et al , 2007).”
MethodSelf-citationFu, W., Gudmundsson, O., Feist, A. M., Herjolfsson, G., Brynjolfsson, S., & Palsson, B. Ø. (2012) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2012.07.004 · Cited by 170 · see citing works
“…ALE indicate that C. vulgaris is capable of adapting to different conditions, as has been observed with other organisms (Applebee et al., 2008; Babu and Aravind, 2006; Ibarra et al., 2002; Teusink et al., 2009), and manages to do so by altering its ratio of chlorophyll a to chlorophyll b.”
Self-citationConstrained Allocation Flux Balance Analysis
Mori, M., Terence Hwa, B., Martin, O., Martino, A. D., & Marinari, E. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004913 · Cited by 169 · see citing works
Use of genome-scale microbial models for metabolic engineering
Patil, K. R., Åkesson, M., & Nielsen, J. (2003) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2003.11.003 · Cited by 169 · see citing works
Saa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166 · see citing works
“These methods have generated fundamental biological insights into the operation of metabolic networks (Ibarra et al., 2002; Schuetz et al., 2012) as well as great advances in biotechnological applications (Lee and Kim, 2015; Yim et al., 2011).”
MethodMathematical models in microbial systems biology
Stelling, J. (2004) · Current Opinion in Microbiology · doi:10.1016/j.mib.2004.08.004 · Cited by 164 · see citing works
Campodonico, M. A., Andrews, B. A., Asenjo, J. A., Palsson, B. Ø., & Feist, A. M. (2014) · Metabolic Engineering · doi:10.1016/j.ymben.2014.07.009 · Cited by 162 · see citing works
“…together with COBRA methods and organism-specific models has proven successful for the calculation of wild type E. coli optimal growth rates (Ibarra et al., 2002), native E. coli metabolite production through knock-outs (Fong et al., 2005), and for non-native E. coli metabolite production…”
MethodSelf-citation
- Authors
- Ibarra, R. U., Edwards, J. S., & Palsson, B. O.
- Affiliations
- University of California San Diego (US)
- University of Delaware (US)
- ORCID
- Edwards, Jeremy S.
- Venue
- Nature
- Published
- 2002-11
- Volume
- 420
- Issue
- 6912
- Pages
- 186-189
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2025663053
- Cited by
- 857
- References
- 23
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 22 · 2023: 21 · 2022: 17 · 2021: 27 · 2020: 23 · 2019: 29 · 2018: 34 · 2017: 42 · 2016: 44 · 2015: 34 · 2014: 46 · 2013: 41 · 2012: 51
- FWCI
- 12.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation
- U.S. Department of Energy
- Istituto Superiore di Sanità
- National Institutes of Health
- Biological and Environmental Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Escherichia coli K-12, adaptive evolution, optimal growth rate, glycerol metabolism, constraint-based metabolic modeling, in silico prediction, metabolic network evolution, carbon source utilization
- MeSH terms
- Adaptation, Physiological, Carbon, Computer Simulation, Escherichia coli, Biological Evolution, Glucose, Glycerol, Kinetics, Malates, Models, Biological, Phenotype, Selection, Genetic, Genome, Bacterial, Computational Biology, Acetic Acid, Succinic Acid, Genomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
