Synthetic biology: from hype to impact
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Sophisticated genetic toolkits allow precise and rapid design and automated high-throughput strain engineering and prototyping in E. coli and yeast.
“This has led to the development of sophisticated genetic toolkits that allow precise and rapid design (Temme et al., 2012b) and automated high-throughput strain engineering and prototyping (Gardner, 2013; Burk and Van Dien, 2016).”
From Gas Fermentation—A Flexible Platform for Commercial Scale Production of Low-Carbon-Fuels and Chemicals from Waste and Renewable Feedstocks (2016) · cites this paper as Gardner, 2013
Cites
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505 · see citing works
A synthetic oscillatory network of transcriptional regulators
Elowitz, M., & Leibler, S. (2000) · Nature · doi:10.1038/35002125 · Cited by 4,812 · see citing works
Why Most Published Research Findings Are False
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Construction of a genetic toggle switch in Escherichia coli
Gardner, T. S., Cantor, C. R., & Collins, J. J. (2000) · Nature · doi:10.1038/35002131 · Cited by 4,310 · see citing works
Raise standards for preclinical cancer research
Begley, C. G., & Ellis, L. M. (2012) · Nature · doi:10.1038/483531a · Cited by 2,979 · see citing works
Believe it or not: how much can we rely on published data on potential drug targets?
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Foundations for engineering biology
Endy, D. (2005) · Nature · doi:10.1038/nature04342 · Cited by 1,511 · see citing works
Emergent Properties of Reduced-Genome Escherichia coli
Pósfai, G., Plunkett, G., Fehér, T., Frisch, D. A., Keil, G. M., Umenhoffer, K., Kolisnychenko, V., Stahl, B., Sharma, S. S., de Arruda, M., et al. (2006) · Science · doi:10.1126/science.1126439 · Cited by 698 · see citing works
Chan, L. Y., Kosuri, S., & Endy, D. (2005) · Molecular Systems Biology · doi:10.1038/msb4100025 · Cited by 359 · see citing works
Genome‐scale engineering for systems and synthetic biology
Esvelt, K. M., & Wang, H. H. (2013) · Molecular Systems Biology · doi:10.1038/msb.2012.66 · Cited by 319 · see citing works
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Semi-synthetic artemisinin: a model for the use of synthetic biology in pharmaceutical development
Paddon, C. J., & Keasling, J. D. (2014) · Nature Reviews Microbiology · doi:10.1038/nrmicro3240 · Cited by 675 · see citing works
Liew, F., Martin, M. E., Tappel, R. C., Heijstra, B. D., Mihalcea, C., & Köpke, M. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00694 · Cited by 485 · see citing works
“This has led to the development of sophisticated genetic toolkits that allow precise and rapid design (Temme et al., 2012b) and automated high-throughput strain engineering and prototyping (Gardner, 2013; Burk and Van Dien, 2016).”
BackgroundMachine learning for metabolic engineering: A review
Lawson, C. E., Martí, J. M., Radivojević, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., et al. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.10.005 · Cited by 321 · see citing works
A machine learning Automated Recommendation Tool for synthetic biology
Radivojević, T., Costello, Z., Workman, K., & Martin, H. G. (2020) · Nature Communications · doi:10.1038/s41467-020-18008-4 · Cited by 298 · see citing works
A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
Costello, Z., & Martín, H. G. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0054-3 · Cited by 238 · see citing works
“13 At the same time, there is an exponentially increasing amount of functional genomics data available to the experimenter in order to phenotype the resulting bioengineered organism: transcriptomics data volume has a doubling rate of 7 months, 14 and high-throughput work fl ows for proteomics 15 and…”
BackgroundMethodSynthetic and systems biology for microbial production of commodity chemicals
Chubukov, V., Mukhopadhyay, A., Petzold, C. J., Keasling, J. D., & Martín, H. G. (2016) · npj Systems Biology and Applications · doi:10.1038/npjsba.2016.9 · Cited by 235 · see citing works
“Consequently, both systems and synthetic biology would benefit from research efforts that emphasize reproducibility, method and data sharing, and increased throughput of proteomic, metabolomic, and fluxomic analyses.(158) Limitations in reproducibility hamper predictability.”
BackgroundOpportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation
Carbonell, P., Radivojević, T., & Martín, H. G. (2019) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00540 · Cited by 174 · see citing works
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth
Culley, C., Vijayakumar, S., Zampieri, G., & Angione, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002959117 · Cited by 88 · see citing works
“However, the identification of such strains is a complex issue (15).”
BackgroundNazir, A., Hussain, F. H. N., & Raza, A. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1511149 · Cited by 77 · see citing works
Stevens, R. L., USDOE, Taylor, V., Nichols, J., Maccabe, A. B. ‘., Yelick, K., & Brown, D. (2020) · doi:10.2172/1604756 · Cited by 80 · see citing works
E. coli strain engineering for the production of advanced biopharmaceutical products
Selas Castiñeiras, T., Williams, S. G., Hitchcock, A. G., & Smith, D. C. (2018) · FEMS Microbiology Letters · doi:10.1093/femsle/fny162 · Cited by 92 · see citing works
Biomedically relevant circuit‐design strategies in mammalian synthetic biology
Bacchus, W., Aubel, D., & Fussenegger, M. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.48 · Cited by 59 · see citing works
“safe and reliable usage (Endy, 2005; Gardner, 2013).”
BackgroundA future perspective on the role of industrial biotechnology for chemicals production
Woodley, J. M., Breuer, M., & Mink, D. (2013) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2013.06.023 · Cited by 56 · see citing works
Morrell, W. C., Birkel, G. W., Forrer, M., López, T. D.-F., Backman, T. W. H., Dussault, M., Petzold, C. J., Baidoo, E. E. K., Costello, Z., Ando, D., et al. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.7b00204 · Cited by 50 · see citing works
“6 One of the main obstacles in predicting the behavior of biological systems is a concerning lack of repeatability in bioengineering, as compared to other engineering disciplines.”
BackgroundInfluentialArtificial intelligence for synthetic biology
Eslami, M., Adler, A., Caceres, R. S., Dunn, J. G., Kelley‐Loughnane, N., Varaljay, V. A., & Martín, H. G. (2022) · Communications of the ACM · doi:10.1145/3500922 · Cited by 35 · see citing works
Low-Carbon Fuel and Chemical Production by Anaerobic Gas Fermentation
Daniell, J., Nagaraju, S., Burton, F., Köpke, M., & Simpson, S. D. (2015) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2015_5005 · Cited by 28 · see citing works
Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering
Roy, S., Radivojević, T., Forrer, M., Martí, J. M., Jonnalagadda, V., Backman, T. W. H., Morrell, W. C., Plahar, H., Kim, J., Hillson, N. J., et al. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.612893 · Cited by 28 · see citing works
“DNA synthesis and CRISPR-based genetic editing (Ma et al., 2012; Doudna and Charpentier, 2014) allow us to produce and change DNA (the working code of the cell) with unparalleled ease, but we can rarely predict how that modified DNA will impact cell behavior (Gardner, 2013).”
BackgroundRegulation and management of the biosecurity for synthetic biology
Zeng, X., Jiang, H., Yang, G., Ou, Y., Lu, S., Jiang, J., Lei, R., & Su, L. (2022) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2022.03.005 · Cited by 26 · see citing works
Zournas, A., Incha, M. R., Radivojević, T., Blay, V., Martí, J. M., Costello, Z., Schmidt, M., Chung, T., Thompson, M. G., Pearson, A. S., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08039-2 · Cited by 26 · see citing works
Towards engineering and production of artificial spider silk using tools of synthetic biology
Poddar, H., Rainer Breitling, A., & Eriko Takano, A. (2020) · Engineering Biology · doi:10.1049/enb.2019.0017 · Cited by 26 · see citing works
Synthetic Biology: evolution or revolution? A co-founder's perspective
Gardner, T. S., & Hawkins, K. M. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2013.09.013 · Cited by 23 · see citing works
Recent advances in developing and applying biosensors for synthetic biology
Jones, K. A., Zinkus-Boltz, J., & Dickinson, B. C. (2019) · Nano Futures · doi:10.1088/2399-1984/ab4b78 · Cited by 19 · see citing works
Árnason, G. (2017) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180116000840 · Cited by 18 · see citing works
“Trends in Biotechnology 2012;31(3):123–5.”
Anticipating emerging biotechnology threats
Vogel, K. M., & Ouagrham-Gormley, S. B. (2018) · Politics and the Life Sciences · doi:10.1017/pls.2018.21 · Cited by 13 · see citing works
Future‐proofing synthetic biology: educating the next generation
Hallinan, J. S., Wipat, A., Kitney, R. I., Woods, S., Taylor, K., & Goñi‐Moreno, Á. (2019) · Engineering Biology · doi:10.1049/enb.2019.0001 · Cited by 12 · see citing works
The potential of future foods for a sustainable future
Wang, J., & Zhang, X. (2024) · eFood · doi:10.1002/efd2.133 · Cited by 16 · see citing works
Technical upgrade of an open-source liquid handler to support bacterial colony screening
Lianes, I. D. O., Yubero, P., Gómez-Luengo, Á., Nogales, J., & Espeso, D. R. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1202836 · Cited by 11 · see citing works
“The inherent tendency of such constructs to exhibit unpredictable outcomes due to their intrinsic biological complexity (Gardner, 2013) can be compensated by the high-throughput assembly capability that these techniques may deliver.”
BackgroundBayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale
Backman, T. W. H., Schenk, C., Radivojević, T., Ando, D., Singh, J., Czajka, J. J., Costello, Z., Keasling, J. D., Tang, Y., Akhmatskaya, E. V., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011111 · Cited by 9 · see citing works
“Synthetic biology enables us to bioengineer cells for synthesis of novel valuable molecules such as renewable biofuels or medical drugs [1–5], but its full potential is hindered by our inability to predict biological behavior [6, 7].”
BackgroundDiverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models
Jashnsaz, H., Fox, Z., Hughes, J. J., Li, G., Munsky, B. E., & Neuert, G. (2020) · iScience · doi:10.1016/j.isci.2020.101565 · Cited by 8 · see citing works
“…as optogenetics, gene regulatory networks, or synthetic biology, where predictive understanding of the system behavior with respect to extracellular kinetics or intercellular genetic perturbations is of immense interest (Aoki et al., 2019; Bashor et al., 2019; Gardner, 2013; Harrigan et al., 2018).”
BackgroundApplication of Genetic Engineering in Biotherapeutics Development
Schweickert, P. G., & Cheng, Z. (2019) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-019-09411-6 · Cited by 7 · see citing works
“With the recent advancement in the area of systems and synthetic biology [43], full characterization of E.”
BackgroundLentzos, F. (2016) · Connections The Quarterly Journal · doi:10.11610/connections.15.2.04 · Cited by 6 · see citing works
Milestones in synthetic (micro)biology
(2014) · Nature Reviews Microbiology · doi:10.1038/nrmicro3261 · Cited by 6 · see citing works
Biocatalytic Cascades for API Synthesis
Woodley, J. M. (2016) · doi:10.1002/9781118828083.ch20 · Cited by 6 · see citing works
RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle
Meng, X., Xu, P., & Tao, F. (2023) · iScience · doi:10.1016/j.isci.2023.107069 · Cited by 4 · see citing works
“7 Arguably, the main factors of the lack of emphasis on the L phase are: the lack of predictive power for biological system behavior, 8 resulted by the extreme asymmetry between the sparse learning data and the chaotic metabolic network.”
BackgroundPerspectives on Multienzyme Process Technology
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Advancements in Inducer Systems for Recombinant Protein Production in E. coli
Gupta, J. A., Pandi, K., & Rathore, A. S. (2022) · Microbiology monographs · doi:10.1007/978-3-031-06600-9_1 · Cited by 3 · see citing works
Misconceptions of Synthetic Biology: Lessons from an Interdisciplinary Summer School
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“The fact that assembling parts is becoming straightforward does not make their behaviour easy to predict: as pointed out by Gardner [23], B[n]atural selection is Nature’s method of trial and error (.”
BackgroundZhang, J., Petersen, S. D., Radivojević, T., Ramirez, A., Pérez, A., Abeliuk, E., Sánchez, B. J., Costello, Z., Chen, Y., Fero, M. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/858464 · Cited by 3 · see citing works
Synthetic Biology: Pioneering the Next Bio Revolution for a sustainable planetPreprint
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BayFlux: A Bay esian method to quantify metabolic Flux es and their uncertainty at the genome scalePreprint
Backman, T. W. H., Schenk, C., Radivojević, T., Ando, D., Singh, J., Czajka, J. J., Costello, Z., Keasling, J. D., Tang, Y., Akhmatskaya, E. V., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.19.537435 · Cited by 2 · see citing works
Technical upgrade of an open-source liquid handler to support bacterial colony screeningPreprint
Lianes, I. D. O., Yubero, P., Gómez-Luengo, Á., Nogales, J., & Espeso, D. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.28.530165 · Cited by 1 · see citing works
On Industrial Strength Bio-design Automation
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The First Annual Winter q-bio Meeting: Quantitative Biology on the Hawaiian Islands
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Prediction of metabolic dynamics through deep learning and high-throughput multiomics dataPreprint
Martí, J. M., Kinnunen, P. C., Held, M., Alikhani, A., Conley, A. B., Chen, Y., Radivojević, T., Gin, J. W., Zournas, A., Lennen, R. M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.27.656438
“However, the steep challenge of systematically predicting the outcomes of cell modifications as accurately as for physical or chemical systems, makes synthetic biology a costly and long process 24–26 .”
- Authors
- Gardner, T. S.
- Affiliations
- Amyris (United States) (US)
- Corresponding author
- Gardner, Timothy S.
- ORCID
- Gardner, Timothy S.
- Venue
- Trends in biotechnology
- Publisher
- Elsevier BV
- Published
- 2013-03
- Volume
- 31
- Issue
- 3
- Pages
- 123-125
- ISSN
- 0167-7799 (ISSN-L)
- OpenAlex
- W2085002908
- Cited by
- 46
- References
- 11
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 6 · 2022: 3 · 2021: 1 · 2020: 6 · 2019: 5 · 2018: 3 · 2017: 2 · 2016: 5 · 2015: 1 · 2014: 5 · 2013: 4
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in biotechnology (publishedVersion)
- PubMed (publishedVersion)
- 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 › CRISPR and Genetic Engineering
- Keywords
- synthetic biology, scientific impact, hype, biological complexity
- MeSH terms
- Biotechnology, History, 20th Century, History, 21st Century, Synthetic Biology, Metabolic Engineering
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