The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Programming cells by multiplex genome engineering and accelerated evolution
Open accessAbstract
The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments. However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales. Although in vitro and directed evolution methods have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-D-xylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“(c) epistasis is a major factor for adaptive optimization of gene regulation [89,157].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [157]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
In vitro selection of RNA molecules that bind specific ligands
Ellington, A. D., & Szostak, J. W. (1990) · Nature · doi:10.1038/346818a0 · Cited by 9,845 · cited 1×
“7); (2) induce cells for allelic replacement via 42 uC heat shock for 15 min; (3) chill cells at 4 uC to halt cellular metabolism; (4) wash cells through 15–20 iterations of filtration and resuspension with dH2O; (5) mix cell suspension and synthetic DNA; (6) deliver DNA into cells by electroporation; and (7) resuspen…”
BackgroundEnvironmental genome shotgun sequencing of the sargasso sea
Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., Wu, D., Paulsen, I., Nelson, K. E., Nelson, W., et al. (2004) · Science · doi:10.1126/science.1093857 · Cited by 4,376 · cited 3×
“The device then executed repeatedly and continuously through the following steps: (1) grow cells at 30 uC to a pre-set density (that is, OD600 nm of 0.”
BackgroundMethodAn efficient recombination system for chromosome engineering in Escherichia coli
Yu, D., Ellis, H. M., Lee, E.-C., Jenkins, N. A., Copeland, N. G., & Court, D. L. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.100127597 · Cited by 1,688
Comparative Metagenomics of Microbial Communities
Tringe, S. G., von Mering, C., Kobayashi, A., Salamov, A. A., Chen, K., Chang, H. W., Podar, M., Short, J. M., Mathur, E. J., Detter, J. C., et al. (2005) · Science · doi:10.1126/science.1107851 · Cited by 1,677 · cited 3×
“Oligonucleotides used in the MAGE process contained the following modifications: (1) 30–110 bp in length for optimization experiments; (2) different numbers of phosphorothioated bases; and (3) degenerate nucleotides as described elsewhere in this paper.”
BackgroundMethodAccurate Multiplex Polony Sequencing of an Evolved Bacterial Genome
Shendure, J., Porreca, G. J., Reppas, N. B., Lin, X., McCutcheon, J. P., Rosenbaum, A. M., Wang, M. D., Zhang, K., Mitra, R. D., & Church, G. M. (2005) · Science · doi:10.1126/science.1117389 · Cited by 1,402
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 · cited 3×
“Oligonucleotides used in the MAGE process contained the following modifications: (1) 30–110 bp in length for optimization experiments; (2) different numbers of phosphorothioated bases; and (3) degenerate nucleotides as described elsewhere in this paper.”
BackgroundMethodComplete Chemical Synthesis, Assembly, and Cloning of a Mycoplasma genitalium Genome
Gibson, D. G., Benders, G. A., Andrews-Pfannkoch, C., Denisova, E. A., Baden-Tillson, H., Zaveri, J., Stockwell, T. B., Brownley, A., Thomas, D. W., Algire, M. A., et al. (2008) · Science · doi:10.1126/science.1151721 · Cited by 1,243
A new logic for DNA engineering using recombination in Escherichia coli
Zhang, Y., Buchholz, F., Muyrers, J. P. P., & Stewart, A. F. (1998) · Nature Genetics · doi:10.1038/2417 · Cited by 1,225
Randomization of genes by PCR mutagenesis.Preprint
Cadwell, R. C., & Joyce, G. F. (1992) · Genome Research · doi:10.1101/gr.2.1.28 · Cited by 1,082
DINAMelt web server for nucleic acid melting prediction
Markham, N. R., & Zuker, M. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki591 · Cited by 947
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 1,000
Multiplex genome editing by natural transformation
Dalia, A. B., McDonough, E., & Camilli, A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1406478111 · Cited by 207
“Mechanistically this method relies on annealing of ssDNA oligos to the lagging strand during DNA replication and can introduce point mutations or small insertions and deletions into the genome at efficiencies of up to ∼20% (2, 3, 6, 7).”
MethodZymomonas mobilis: a novel platform for future biorefineries
He, M., Wu, B., Qin, H., Ruan, Z., Tan, F., Wang, J., Shui, Z., Dai, L., Zhu, Q., Pan, K., et al. (2014) · Biotechnology for Biofuels · doi:10.1186/1754-6834-7-101 · Cited by 203
Dietrich, J. A., Shis, D. L., Alikhani, A., & Keasling, J. D. (2012) · ACS Synthetic Biology · doi:10.1021/sb300091d · Cited by 206
Synthetic Biology—The Synthesis of Biology
Ausländer, S., Ausländer, D., & Fussenegger, M. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201609229 · Cited by 210
Rodriguez, A., Martnez, J. A., Flores, N., Escalante, A., Gosset, G., & Bolívar, F. G. (2014) · Microbial Cell Factories · doi:10.1186/s12934-014-0126-z · Cited by 208
“This method relies on a cyclical oligo-mediated allelic replacement to modify genomic targets [107] that was later improved by linking the process with the recovery of an inactivated selection marker, enhancing the size and efficiency of insertions [108].”
MethodZhu, F., Zhong, X., Hu, M., Lü, L., Deng, Z., & Liu, T. (2014) · Biotechnology and Bioengineering · doi:10.1002/bit.25198 · Cited by 203
“These achievements, however, were based on traditional labor-intensive combinations of molecular cloning or high throughput screening method (Dietrich et al., 2010; Wang et al., 2009).”
MethodAutomated multiplex genome-scale engineering in yeast
Si, T., Chao, R., Min, Y., Wu, Y., Ren, W., & Zhao, H. (2017) · Nature Communications · doi:10.1038/ncomms15187 · Cited by 200
Carr, P. A., & Church, G. M. (2009) · Nature Biotechnology · doi:10.1038/nbt.1590 · Cited by 203
Self-citationMultiplex recording of cellular events over time on CRISPR biological tape
Sheth, R. U., Yim, S. S., Wu, F. L., & Wang, H. H. (2017) · Science · doi:10.1126/science.aao0958 · Cited by 200
Self-citationSustainable life support on Mars – the potential roles of cyanobacteria
Verseux, C., Baqué, M., Lehto, K., de Vera, J.-P. P., Rothschild, L. J., & Billi, D. (2016) · International Journal of Astrobiology · doi:10.1017/s147355041500021x · Cited by 194
“2002) and multiplex genome engineering (Wang et al. 2009).”
MethodAdaptation of cells to new environments
Brooks, A. N., Turkarslan, S., Beer, K. D., Lo, F. Y., & Baliga, N. S. (2010) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.136 · Cited by 205
Spanning high-dimensional expression space using ribosome-binding site combinatorics
Zelcbuch, L., Antonovsky, N., Bar‐Even, A., Levin-Karp, A., Barenholz, U., Dayagi, M., Liebermeister, W., Flamholz, A. I., Noor, Ε., Amram, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt151 · Cited by 197
“(8) and the ribosome-binding site (RBS) (9,10) sequences,”
Lambda Red Recombineering in Escherichia coli Occurs Through a Fully Single-Stranded Intermediate
Mosberg, J. A., Lajoie, M. J., & Church, G. M. (2010) · Genetics · doi:10.1534/genetics.110.120782 · Cited by 200
Self-citationRNA processing enables predictable programming of gene expression
Qi, L. S., Haurwitz, R. E., Shao, W., Doudna, J. A., & Arkin, A. P. (2012) · Nature Biotechnology · doi:10.1038/nbt.2355 · Cited by 198
Directed evolution: Past, present, and future
Cobb, R. E., Chao, R., & Zhao, H. (2012) · AIChE Journal · doi:10.1002/aic.13995 · Cited by 197
“coli cells with single-stranded oligos containing degenerate bases.(47) [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.”
BackgroundEngineering biological systems using automated biofoundries
Chao, R., Mishra, S., Si, T., & Zhao, H. (2017) · Metabolic Engineering · doi:10.1016/j.ymben.2017.06.003 · Cited by 209
“There are also fully automated systems developed to run complete workflows (Dorr et al., 2016; King et al., 2009, 2004; Wang et al., 2009), but they are extensively customized towards specific applications, and hence are difficult to reconfigure for other tasks.”
Systems metabolic engineering for chemicals and materials
Lee, J. W., Kim, T. Y., Jang, Y., Choi, S., & Lee, S. Y. (2011) · Trends in biotechnology · doi:10.1016/j.tibtech.2011.04.001 · Cited by 194
Genome-scale engineering of Saccharomyces cerevisiae with single-nucleotide precision
Bao, Z., HamediRad, M., Xue, P., Xiao, H., Tasan, I., Chao, R., Liang, J., & Zhao, H. (2018) · Nature Biotechnology · doi:10.1038/nbt.4132 · Cited by 195
Designing and engineering evolutionary robust genetic circuits
Sleight, S. C., Bartley, B., Lieviant, J. A., & Sauro, H. M. (2010) · Journal of Biological Engineering · doi:10.1186/1754-1611-4-12 · Cited by 200
“On the genome level, entire metabolic pathways have been engineered to overproduce an antimalaria compound [9], biofuels from plant biomass [10,11] lycopene through automated genome engineering and accelerated evolution [12], and a synthetic chromosome [13] transplanted into a host bacterium [14].”
BackgroundMetabolic pathway balancing and its role in the production of biofuels and chemicals
Jones, J. A., Toparlak, Ö. D., & Koffas, M. A. G. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.11.013 · Cited by 193
“Even though there are novel approaches for automation, such as Multiplex Automated Genome Engineering (MAGE) and Yeast Oligo-mediated Genome Engineering (YOGE) [12–14], this hurdle still highlights one of the biggest challenges for the future of pathway balancing and optimization.”
Chen, X., Gao, C., Guo, L., Hu, G., Luo, Q., Liu, J., Nielsen, J., Chen, J., & Liu, L. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00804 · Cited by 191
Modular control of multiple pathways using engineered orthogonal T7 polymerases
Temme, K., Hill, R. M., Segall-Shapiro, T. H., Moser, F., & Voigt, C. A. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks597 · Cited by 192
“Further quantification was performed by extracting pigments and measuring relative absorbance under the different inducing states (50,54).”
BackgroundMethodEngineered biosynthesis of natural products in heterologous hosts
Luo, Y., Li, B., Liu, D., Zhang, L., Chen, Y., Jia, B., Zeng, B., Zhao, H., & Yuan, Y. (2015) · Chemical Society Reviews · doi:10.1039/c5cs00025d · Cited by 193
Application of combinatorial optimization strategies in synthetic biology
Naseri, G., & Koffas, M. A. G. (2020) · Nature Communications · doi:10.1038/s41467-020-16175-y · Cited by 187
Isoprenoid drugs, biofuels, and Chemicals–Artemisinin, farnesene, and beyond
George, K. W., Alonso-Gutierrez, J., Keasling, J. D., & Lee, T. S. (2015) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2014_288 · Cited by 185
“Though targeted efflux pump expression has been proven successful as a tolerance mechanism, traditional strain improvement methods such as chemostatmediated adaptation, chemical mutagenesis, directed evolutionary engineering, genome shuffling [111], or targeted recombineering methodologies [112] constitute powerful to…”
BackgroundMinty, J. J., Lesnefsky, A. A., Lin, F., Chen, Y., Zaroff, T. A., Veloso, A. B., Xie, B., McConnell, C. A., Ward, R. J., Schwartz, D. R., et al. (2011) · Microbial Cell Factories · doi:10.1186/1475-2859-10-18 · Cited by 185
“coli EcHW24 was used to engineer chromosomal SNPs and short indels, as per previously described procedures [37].”
BackgroundMethodInfluentialProgramming adaptive control to evolve increased metabolite production
Chou, H. H., & Keasling, J. D. (2013) · Nature Communications · doi:10.1038/ncomms3595 · Cited by 184
Genetically encoded sensors enable real-time observation of metabolite production
Rogers, J. K., & Church, G. M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600375113 · Cited by 182
“Even sophisticated modeling paradigms can result in very large design spaces (1, 2).”
BackgroundSelf-citationRandom sequences rapidly evolve into de novo promoters
Yona, A. H., Alm, E. J., & Gore, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04026-w · Cited by 182
Wiltschi, B., Cernava, T., Dennig, A., Casas, M. G., Geier, M., Gruber, S., Haberbauer, M., Heidinger, P., Acero, E. H., Kratzer, R., et al. (2020) · Biotechnology Advances · doi:10.1016/j.biotechadv.2020.107520 · Cited by 195
Model-Driven Engineering of RNA Devices to Quantitatively Program Gene Expression
Carothers, J. M., Goler, J. A., Juminaga, D., & Keasling, J. D. (2011) · Science · doi:10.1126/science.1212209 · Cited by 180
Targeted proteomics for metabolic pathway optimization: Application to terpene production
Redding‐Johanson, A. M., Batth, T. S., Chan, R., Krupa, R. A., Szmidt, H. L., Adams, P. D., Keasling, J. D., Lee, T. S., Mukhopadhyay, A., & Petzold, C. J. (2011) · Metabolic Engineering · doi:10.1016/j.ymben.2010.12.005 · Cited by 179
“Additionally, a variety of directed evolution techniques (Bloom et al., 2005; Alper et al., 2005; Wang et al., 2009) have been successfully applied to overcome pathway imbalances and improve production; however, they rely heavily on rapid screening methods that are not universally applicable (e.”
BackgroundMunck, C., Gumpert, H., Wallin, A. I. N., Wang, H. H., & Sommer, M. O. A. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3009940 · Cited by 176
Self-citationRapid and Efficient One-Step Metabolic Pathway Integration in E. coli
Bassalo, M. C., Garst, A. D., Halweg‐Edwards, A. L., Grau, W. C., Domaille, D. W., Mutalik, V. K., Arkin, A. P., & Gill, R. T. (2016) · ACS Synthetic Biology · doi:10.1021/acssynbio.5b00187 · Cited by 179
Gaspar, P., Carvalho, A. L., Vinga, S., Santos, H., & Neves, A. R. (2013) · Biotechnology Advances · doi:10.1016/j.biotechadv.2013.03.011 · Cited by 185
Design and use of synthetic regulatory small RNAs to control gene expression in Escherichia coli
Yoo, S. M., Na, D., & Lee, S. Y. (2013) · Nature Protocols · doi:10.1038/nprot.2013.105 · Cited by 173
Engineering genes for predictable protein expression
Gustafsson, C., Minshull, J., Govindarajan, S., Ness, J. E., Villalobos, A., & Welch, M. (2012) · Protein Expression and Purification · doi:10.1016/j.pep.2012.02.013 · Cited by 172
“Similar work has also been performed in the Harvard lab of George Church using oligonucleotide directed mutations [11] and artificial zinc fingers [12].”
Microbial Chassis Development for Natural Product Biosynthesis
Xu, X., Liu, Y., Du, G., Ledesma‐Amaro, R., & Liu, L. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.01.002 · Cited by 172
Biochemical Characteristics of Microbial Enzymes and Their Significance from Industrial Perspectives
Thapa, S., Li, H., O’Hair, J., Bhatti, S., Chen, F., Nasr, K. A., Johnson, T. L., & Zhou, S. (2019) · Molecular Biotechnology · doi:10.1007/s12033-019-00187-1 · Cited by 183
Ng, C. Y., Farasat, I., Maranas, C. D., & Salis, H. M. (2015) · Metabolic Engineering · doi:10.1016/j.ymben.2015.03.001 · Cited by 167
“We then applied oligo-mediated allelic replacement, also known as MAGE (Wang et al., 2009), to incorporate these ribosome binding site mutations directly into the E.”
BackgroundMethodInfluentialSynthetic RNA devices to expedite the evolution of metabolite-producing microbes
Yang, J., Seo, S. W., Jang, S., Shin, S.-I., Lim, C. H., Roh, T., & Jung, G. Y. (2013) · Nature Communications · doi:10.1038/ncomms2404 · Cited by 167
Engineering microbial hosts for production of bacterial natural products
Zhang, M. M., Wang, Y., Ang, E. L., & Zhao, H. (2016) · Natural Product Reports · doi:10.1039/c6np00017g · Cited by 167
Directed evolution of genetic parts and circuits by compartmentalized partnered replication
Ellefson, J. W., Meyer, A. J., Hughes, R. A., Cannon, J. R., Brodbelt, J. S., & Ellington, A. D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2714 · Cited by 162
Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays
Reis, A. C., Halper, S. M., Vezeau, G. E., Cetnar, D. P., Hossain, A., Clauer, P. R., & Salis, H. M. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0286-9 · Cited by 160
Engineering Living Functional Materials
Chen, A. Y., Zhong, C., & Lu, T. K. (2015) · ACS Synthetic Biology · doi:10.1021/sb500113b · Cited by 162
Sandberg, T. E., Pedersen, M., LaCroix, R. A., Ebrahim, A., Bonde, M., Herrgård, M. J., Palsson, B. Ø., Sommer, M. O. A., & Feist, A. M. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu209 · Cited by 160
“Recombinant formation frequency is influenced largely by oligo length (Wang et al. 2009), which, along with all other experimental variables, was kept constant for all recombineering, thus allelic biases should not be present.”
BackgroundMethodResultInfluentialRock-paper-scissors: Engineered population dynamics increase genetic stability
Liao, M. J., Din, M. O., Tsimring, L. S., & Hasty, J. (2019) · Science · doi:10.1126/science.aaw0542 · Cited by 161
High-throughput enzyme evolution in Saccharomyces cerevisiae using a synthetic RNA switch
Michener, J. K., & Smolke, C. D. (2012) · Metabolic Engineering · doi:10.1016/j.ymben.2012.04.004 · Cited by 160
Exploring the potential of genome editing CRISPR-Cas9 technology
Singh, V., Braddick, D., & Dhar, P. K. (2016) · Gene · doi:10.1016/j.gene.2016.11.008 · Cited by 172
“Other recently developed synthetic biology technologies include multiplex automated genome engineering (MAGE), which has been used to improve lycopene production (Wang et al., 2009), modify the genetic code (Isaacs et al.”
BackgroundMethodORBIT: a New Paradigm for Genetic Engineering of Mycobacterial Chromosomes
Murphy, K. C., Nelson, S. J., Nambi, S., Papavinasasundaram, K. G., Baer, C. E., & Sassetti, C. M. (2018) · mBio · doi:10.1128/mbio.01467-18 · Cited by 160
“coli for specific metabolic engineering purposes (44).”
Synthetic Biology: Advancing the Design of Diverse Genetic Systems
Wang, Y., Wei, K. Y., & Smolke, C. D. (2013) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-061312-103351 · Cited by 162
“As one example, synthetic biology provides new tools for DNA synthesis and assembly, allowing for the combinatorial generation and testing of many pathway and enzyme variants (81, 82).”
BackgroundA primer to directed evolution: current methodologies and future directions
Vidal, L. S., Isalan, M., Heap, J. T., & Ledesma‐Amaro, R. (2023) · RSC Chemical Biology · doi:10.1039/d2cb00231k · Cited by 171
Sawitzke, J. A., Costantino, N., Li, X., Thomason, L. C., Bubunenko, M. G., Court, C., & Court, D. L. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2011.01.030 · Cited by 164
“We expect that numerous different oligos can be used to modify single or multiple loci simultaneously, with the limiting factors being the amount of DNA that can be introduced into a cell and having effective screens or selections to find the changes.(6) The presence of several DNA replication forks in rapidly dividin…”
InfluentialMultilayered genetic safeguards limit growth of microorganisms to defined environments
Gallagher, R. R., Patel, J. R., Interiano, A. L., Rovner, A. J., & Isaacs, F. J. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1378 · Cited by 156
“Introduction of these mutations in a contained background using multiplex automated genome engineering (MAGE; (23,29)) leads to escape.”
BackgroundMethodSelf-citationSaini, R. K., & Keum, Y. (2018) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-018-2104-7 · Cited by 158
Recent Advances in the Discovery and Development of Marine Microbial Natural Products
Xiong, Z., Wang, J.-F., Hao, Y.-Y., & Wang, Y. (2013) · Marine Drugs · doi:10.3390/md11030700 · Cited by 159
“Based on the development of genome-wide genetic manipulation techniques such as hierarchical conjugative assembly genome engineering (CAGE) [81] and multiplex automated genome engineering (MAGE) [82], and the functional definition of abundant genetic materials (e.g., functional genes derived from different sources, co…”
BackgroundOligonucleotide‐directed mutagenesis for precision gene editing
Sauer, N. J., Mozoruk, J., Miller, R. B., Warburg, Z. J., Walker, K. A. M., Beetham, P. R., Schöpke, C. R., & Gocal, G. F. W. (2015) · Plant Biotechnology Journal · doi:10.1111/pbi.12496 · Cited by 155
Natural products as biofuels and bio-based chemicals: fatty acids and isoprenoids
Beller, H. R., Lee, T. S., & Katz, L. (2015) · Natural Product Reports · doi:10.1039/c5np00068h · Cited by 153
Dalia, T. N., Hayes, C. A., Stolyar, S. M., Marx, C. J., McKinlay, J. B., & Dalia, A. B. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.7b00116 · Cited by 153
CRISPR-Cas9/Cas12a biotechnology and application in bacteria
Yao, R., Liu, D., Jia, X., Zheng, Y., Liu, W. Y., & Xiao, Y. (2018) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2018.09.004 · Cited by 165
“To expand the capacity of such systems to large-scale genomic editing that can efficiently modify multiple genomic loci, multiplex automated genome engineering (MAGE) and conjugative assembly genome engineering (CAGE) have been developed [109,110].”
BackgroundBuilding-in biosafety for synthetic biology
Wright, O., Stan, G.-B. V., & Ellis, T. (2013) · Microbiology · doi:10.1099/mic.0.066308-0 · Cited by 156
Kim, S. K., Han, G. H., Seong, W., Kim, H., Kim, S.-W., Lee, D., & Lee, S. (2016) · Metabolic Engineering · doi:10.1016/j.ymben.2016.08.006 · Cited by 150
Yeast Oligo-Mediated Genome Engineering (YOGE)
DiCarlo, J. E., Conley, A. J., Penttilä, M. E., Jäntti, J., Wang, H. H., & Church, G. M. (2013) · ACS Synthetic Biology · doi:10.1021/sb400117c · Cited by 149
Self-citationCRISPR technologies for bacterial systems: Current achievements and future directions
Choi, K. R., & Lee, S. Y. (2016) · Biotechnology Advances · doi:10.1016/j.biotechadv.2016.08.002 · Cited by 151
Multivariate modular metabolic engineering for pathway and strain optimization
Biggs, B. W., Paepe, B. D., Santos, C. N. S., Mey, M. D., & Ajikumar, P. K. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.05.005 · Cited by 146
“Recombination with short single-stranded oligonucleotides allows one to change small sequences, such as promoters and RBSs directly in the genome and has been shown to be amenable to multiplexing as in multiplex automated genome engineering (MAGE) [17,50,51].”
BackgroundZhang, C., Seow, V. Y., Chen, X., & Too, H. (2018) · Nature Communications · doi:10.1038/s41467-018-04211-x · Cited by 146
An orthogonalized platform for genetic code expansion in both bacteria and eukaryotes
Italia, J. S., Addy, P. S., Wrobel, C. J. J., Crawford, L. A., Lajoie, M. J., Zheng, Y., & Chatterjee, A. (2017) · Nature Chemical Biology · doi:10.1038/nchembio.2312 · Cited by 143
Development of a fast and easy method for Escherichia coli genome editing with CRISPR/Cas9
Zhao, D., Yuan, S., Xiong, B., Sun, H., Ye, L., Li, J., Zhang, X., & Bi, C. (2016) · Microbial Cell Factories · doi:10.1186/s12934-016-0605-5 · Cited by 144
“High throughput and automatic genomic editing is one highly desired technique by molecular biologists [24, 25].”
MethodPhage Therapy: What Have We Learned?
Górski, A., Międzybrodzki, R., Łobocka, M. B., Głowacka-Rutkowska, A., Bednarek, A., Borysowski, J., Jończyk‐Matysiak, E., Łusiak-Szelachowska, M., Weber‐Dąbrowska, B., Bagińska, N., et al. (2018) · Viruses · doi:10.3390/v10060288 · Cited by 161
Liu, W., Luo, Z., Wang, Y., Pham, N. T., Tuck, L., Pérez‐Pi, I., Liu, L., Shen, Y., French, C. E., Auer, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04254-0 · Cited by 142
RNA-aptamers-in-droplets (RAPID) high-throughput screening for secretory phenotypes
Abatemarco, J., Sarhan, M. F., Wagner, J. M., Lin, J., Liu, L., Hassouneh, W., Yuan, S., Alper, H. S., & Abate, A. R. (2017) · Nature Communications · doi:10.1038/s41467-017-00425-7 · Cited by 142
Probing the Limits of Genetic Recoding in Essential Genes
Lajoie, J. G., Kosuri, S., Mosberg, J. A., Gregg, C. J., Zhang, D., & Church, G. M. (2013) · Science · doi:10.1126/science.1241460 · Cited by 140
Self-citationHong, S. H., Ntai, I., Haimovich, A. D., Kelleher, N. L., Isaacs, F. J., & Jewett, M. C. (2013) · ACS Synthetic Biology · doi:10.1021/sb400140t · Cited by 142
Self-citationGenetic code flexibility in microorganisms: novel mechanisms and impact on physiology
Ling, J., O'Donoghue, P., & Söll, D. (2015) · Nature Reviews Microbiology · doi:10.1038/nrmicro3568 · Cited by 135
Tuning and controlling gene expression noise in synthetic gene networks
Murphy, K. F., Adams, R., Wang, X., Balázsi, G., & Collins, J. J. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq091 · Cited by 138
“…small-scale gene circuits in microbes (1–8) and coordinating cell population behavior (9,10), to the development of programmable cells (6,11–13) and the metabolic engineering of microbes for biofuel production (14,15), the last decade has witnessed a broad range of advances towards a…”
BackgroundSynthesis of chemicals by metabolic engineering of microbes
Sun, X., Shen, X., Jain, R., Lin, Y., Wang, J., Sun, J. Y., Wang, J., Yan, Y., & Yuan, Q. (2015) · Chemical Society Reviews · doi:10.1039/c5cs00159e · Cited by 138
Su, T., Liu, F., Gu, P., Jin, H., Chang, Y., Wang, Q., Liang, Q., & Qi, Q. (2016) · Scientific Reports · doi:10.1038/srep37895 · Cited by 138
Designing Metabolic Division of Labor in Microbial Communities
Thommes, M., Wang, T., Zhao, Q., Paschalidis, I. C., & Segrè, D. (2019) · mSystems · doi:10.1128/msystems.00263-18 · Cited by 140
Mitochondrial 16S rRNA Is Methylated by tRNA Methyltransferase TRMT61B in All Vertebrates
Bar-Yaacov, D., Frumkin, I., Yashiro, Y., Chujo, T., Ishigami, Y., Chemla, Y., Blumberg, A., Schlesinger, O., Bieri, P., Greber, B. J., et al. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002557 · Cited by 139
Stress-tolerant non-conventional microbes enable next-generation chemical biosynthesis
Thorwall, S., Schwartz, C., Chartron, J. W., & Wheeldon, I. R. (2020) · Nature Chemical Biology · doi:10.1038/s41589-019-0452-x · Cited by 136
A user’s guide to golden gate cloning methods and standards
Bird, J. E., Marles-Wright, J., & Giachino, A. (2022) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00355 · Cited by 135
Update on designing and building minimal cells
Jewett, M. C., & Forster, A. C. (2010) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.06.008 · Cited by 134
“coli cell followed by single batch purification [18,19 ,63].”
BackgroundMethodSustainable production of natural phenolics for functional food applications
Mark, R., Lyu, X., Lee, J. J. L., Parra‐Saldívar, R., & Chen, W. N. (2019) · Journal of Functional Foods · doi:10.1016/j.jff.2019.04.008 · Cited by 134
Predictable tuning of protein expression in bacteria
Bonde, M., Pedersen, M., Klausen, M. S., Jensen, S. I., Wulff, T., Harrison, S. J., Nielsen, A. T., Herrgård, M. J., & Sommer, M. O. A. (2016) · Nature Methods · doi:10.1038/nmeth.3727 · Cited by 133
Multiplexed Genome Engineering and Genotyping Methods
Wang, H. H., & Church, G. M. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385120-8.00018-8 · Cited by 132
“1 Allelic replacement efficiency prediction function based on fitting empirically determined efficiencies from Wang et al. (2009a) where b is the base-pair”
BackgroundMethodInfluentialSelf-citationParts plus pipes: Synthetic biology approaches to metabolic engineering
Boyle, P. M., & Silver, P. A. (2011) · Metabolic Engineering · doi:10.1016/j.ymben.2011.10.003 · Cited by 131
“Genome Engineering (MAGE) is a high throughput technique for the directed evolution of microbial genomes (Wang et al., 2009).”
InfluentialOptimization of a blueprint for in vitro glycolysis by metabolic real-time analysis
Bujara, M., Schümperli, M., Pellaux, R., Heinemann, M., & Panke, S. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.541 · Cited by 132
“Consequently, recent examples of metabolic network fine-tuning have applied evolutionary strategies and high-throughput screening of phenotypes to circumvent the laborious construction and analysis of multiple variants suggested by rational approache”
BackgroundEcological speciation of bacteriophage lambda in allopatry and sympatry
Meyer, J. R., Dobias, D. T., Medina, S. J., Servilio, L. C., Gupta, A., & Lenski, R. E. (2016) · Science · doi:10.1126/science.aai8446 · Cited by 138
Modular engineering for microbial production of carotenoids
Li, C., Swofford, C. A., & Sinskey, A. J. (2019) · Metabolic Engineering Communications · doi:10.1016/j.mec.2019.e00118 · Cited by 132
Vibrio natriegens : an ultrafast‐growing marine bacterium as emerging synthetic biology chassis
Hoff, J., Daniel, B. B. J., Stukenberg, D., Thuronyi, B. W., Waldminghaus, T., & Fritz, G. (2020) · Environmental Microbiology · doi:10.1111/1462-2920.15128 · Cited by 137
“In the future, V. natriegens also holds enormous potential to speed up directed evolution protocols as well as methods like multiplexed automated genome editing (MAGE) (Wang et al., 2009), since both of are highly time consuming due to repetitive cycles of exponential cell growth and dilution.”
MethodCRISPR/Cas-based genome engineering in natural product discovery
Tong, Y., Weber, T., & Lee, S. Y. (2018) · Natural Product Reports · doi:10.1039/c8np00089a · Cited by 131
The developing toolkit of continuous directed evolution
Morrison, M. S., Podracky, C. J., & Liu, D. R. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0532-y · Cited by 133
Microbial Platform for Terpenoid Production: Escherichia coli and Yeast
Wang, C., Liwei, M., Park, J.-B., Jeong, S.-H., Wei, G., Wang, Y., & Kim, S.-W. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02460 · Cited by 135
A unified design space of synthetic stripe-forming networks
Schaerli, Y., Munteanu, A., Gili, M., Cotterell, J., Sharpe, J. A., & Isalan, M. (2014) · Nature Communications · doi:10.1038/ncomms5905 · Cited by 129
“Experience gained in this way deepens our understanding of natural networks 7-10 and widens our perspective on building more complex networks for applications 11-20 .”
Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms
Lee, S., & Kim, P. (2020) · Journal of Microbiology and Biotechnology · doi:10.4014/jmb.2003.03072 · Cited by 131
“As to altering complex phenotypes, genome evolution approaches including genome shuffling [78], global transcription machinery engineering (gTME) [79], and MAGE (multiplex automated genome engineering) [80] are suitable.”
BackgroundSynthetic genetic circuits for programmable biological functionalities
Xia, P., Ling, H., Foo, J. L., & Chang, M. W. (2019) · Biotechnology Advances · doi:10.1016/j.biotechadv.2019.04.015 · Cited by 129
Fourth generation biofuel from genetically modified algal biomass: Challenges and future directions.
Shokravi, H., Shokravi, Z., Heidarrezaei, M., Ong, H. C., Rahimian Koloor, S. S., Petrů, M., Lau, W. J., & Ismail, A. F. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.131535 · Cited by 127
In vivo continuous evolution of genes and pathways in yeast
Crook, N., Abatemarco, J., Sun, J., Wagner, J. M., Schmitz, A., & Alper, H. S. (2016) · Nature Communications · doi:10.1038/ncomms13051 · Cited by 127
“Genome-editing technologies (such as MAGE 17 and CRISPR-Cas9 (refs 18,19)) have enabled discovery of sequence–function relationships across a wide range of species but remain, at least at present, a method for introducing finite, defined mutations across several base pairs (ideal when important…”
Khambhati, K., Bhattacharjee, G., Gohil, N., Braddick, D., Kulkarni, V. V., & Singh, V. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00248 · Cited by 127
Improved bacterial recombineering by parallelized protein discovery
Wannier, T. M., Nyerges, Á., Kuchwara, H., Czikkely, M. S., Balogh, D., Filsinger, G. T., Borders, N. C., Gregg, C. J., Lajoie, M. J., Rios, X., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001588117 · Cited by 127
Self-citation
Metadata
- Authors
- Wang, H. H., Isaacs, F. J., Carr, P. A., Sun, Z. Z., Xu, G., Forest, C. R., & Church, G. M.
- Affiliations
- Harvard University (US)
- MIT Media Lab (US)
- Georgia Institute of Technology (US)
- Corresponding author
- Wang, Harris H.
- Isaacs, Farren J.
- ORCID
- Venue
- Nature
- Published
- 2009-07-26
- Volume
- 460
- Issue
- 7257
- Pages
- 894-898
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4590770
- OpenAlex
- W2144779133
- Cited by
- 1,529
- References
- 32
- Citations by year
- 2026: 56 · 2025: 60 · 2024: 70 · 2023: 58 · 2022: 97 · 2021: 94 · 2020: 88 · 2019: 96 · 2018: 119 · 2017: 113 · 2016: 121 · 2015: 141 · 2014: 114 · 2013: 94 · 2012: 81
- FWCI
- 27.54
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health (DP5OD009172)
- National Defense Science and Engineering Graduate
- Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- 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
- Keywords
- accelerated evolution, directed evolution, genome editing, lycopene production, isoprenoid biosynthesis, Escherichia coli, synthetic DNA, metabolic engineering
- MeSH terms
- Lycopene, Alleles, Biotechnology, Carotenoids, Chromosomes, Bacterial, DNA, Escherichia coli, Pentosephosphates, Genetic Variation, Genome, Bacterial, Directed Molecular Evolution
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