References
(1)
Henkel, J.; Maurer, S. M. The Economics of
Synthetic Biology. Molecular Systems Biology
2007, 3 (1), 117.
(2)
Appleton, E.; Densmore, D.; Madsen, C.;
Roehner, N. Needs and Opportunities in Bio-Design Automation: Four Areas
for Focus. Current Opinion in Chemical Biology
2017, 40, 111–118.
(3)
Appleton, E.; Madsen, C.; Roehner, N.;
Densmore, D. Design Automation in Synthetic Biology. Cold Spring
Harbor perspectives in biology 2017, 9
(4), a023978.
(4)
Torres-Acosta, M. A.; Lye, G. J.; Dikicioglu,
D. Automated Liquid-Handling Operations for Robust, Resilient, and
Efficient Bio-Based Laboratory Practices. Biochemical Engineering
Journal 2022, 188, 108713.
(5)
Yeoh, J. W.; Swainston, N.; Vegh, P.; Zulkower,
V.; Carbonell, P.; Holowko, M. B.; Peddinti, G.; Poh, C. L.
SynBiopython: An Open-Source Software Library for Synthetic Biology,
2021.
(6)
Rand, K. D.; Grytten, I.; Pavlović, M.;
Kanduri, C.; Sandve, G. K. BioNumPy: Array Programming for Biology.
Nature Methods 2024, 1–2.
(7)
Kwon, K. K.; Lee, J.; Kim, H.; Lee, D.-H.; Lee,
S.-G. Advancing High-Throughput Screening Systems for Synthetic Biology
and Biofoundry. Current Opinion in Systems Biology
2023, 100487.
(8)
Sogi, G. Research Waste. Contemporary
Clinical Dentistry, 2023, 14, 179.
(9)
Kwok, R. How to Pick an Electronic Laboratory
Notebook. Nature 2018, 560 (7717),
269–270. https://doi.org/10.1038/d41586-018-05895-3.
(10)
Schmerker, J. Switch to an Electronic Lab
Notebook? Pros and Cons, 2020. https://www.idtdna.com/pages/community/blog/post/thinking-about-making-the-switch-to-an-electronic-lab-notebook-here-are-some-pros-and-cons.
(11)
Eyers, D.; Stevens, S.; Turner, A.; Cohen, J.
Switch to an Electronic Lab Notebook? Pros and Cons, 2020. https://github.com/ImperialCollegeLondon/2020-07-13-Containers-Online/tree/gh-pages?tab=readme-ov-file.
(12)
Leahy, D.; Thorpe, C. Zero Trust Container
Architecture (ZTCA). International Conference on Cyber Warfare and
Security 2022, 17 (1), 111–120. https://doi.org/10.34190/iccws.17.1.35.
(13)
Mirdita, M.; Schütze, K.; Moriwaki, Y.; Heo,
L.; Ovchinnikov, S.; Steinegger, M. ColabFold: Making Protein Folding
Accessible to All. Nature methods 2022,
19 (6), 679–682.
(14)
Wong, B. Visualizing Biological Data. Nat
Methods 2012, 9 (12), 1131–1131.
(15)
Verschaffelt, P.; Collier, J.; Botzki, A.;
Martens, L.; Dawyndt, P.; Mesuere, B. Unipept Visualizations: An
Interactive Visualization Library for Biological Data.
Bioinformatics 2022, 38 (2),
562–563.
(16)
Kerren, A.; Kucher, K.; Li, Y.-F.; Schreiber,
F. BioVis Explorer: A Visual Guide for Biological Data Visualization
Techniques. PLoS One 2017, 12 (11),
e0187341.
(17)
Keller, M. Interactive Visualization of
Biological Data on the Web, 2020. https://github.com/keller-mark/awesome-biological-visualizations.
(18)
White, S.; Quinn, J.; Enzor, J.; Staats, J.;
Mosier, S. M.; Almarode, J.; Denny, T. N.; Weinhold, K. J.; Ferrari, G.;
Chan, C. FlowKit: A Python Toolkit for Integrated Manual and Automated
Cytometry Analysis Workflows. Frontiers in immunology
2021, 12, 768541.
(19)
Castillo-Hair, S. M.; Sexton, J. T.; Landry, B.
P.; Olson, E. J.; Igoshin, O. A.; Tabor, J. J. FlowCal: A User-Friendly,
Open Source Software Tool for Automatically Converting Flow Cytometry
Data from Arbitrary to Calibrated Units. ACS synthetic biology
2016, 5 (7), 774–780.
(20)
Myers, C. J.; Beal, J.; Gorochowski, T. E.;
Kuwahara, H.; Madsen, C.; McLaughlin, J. A.; Mısırlı, G.; Nguyen, T.;
Oberortner, E.; Samineni, M.; others. A Standard-Enabled Workflow for
Synthetic Biology. Biochemical Society Transactions
2017, 45 (3), 793–803.
(21)
Wenzel, T. Open Hardware: From DIY Trend to
Global Transformation in Access to Laboratory Equipment. PLoS
Biology 2023, 21 (1), e3001931.
(22)
Kouba, P.; Kohout, P.; Haddadi, F.; Bushuiev,
A.; Samusevich, R.; Sedlar, J.; Damborsky, J.; Pluskal, T.; Sivic, J.;
Mazurenko, S. Machine Learning-Guided Protein Engineering. ACS
catalysis 2023, 13 (21),
13863–13895.
(23)
Kazlauskas, R. J.; Bornscheuer, U. T. Finding
Better Protein Engineering Strategies. Nature chemical biology
2009, 5 (8), 526–529.
(24)
Listgarten, J. The Perpetual Motion Machine of
AI-Generated Data and the Distraction of ChatGPT as a
“Scientist.” Nature Biotechnology
2024, 42 (3), 371–373.
(25)
Slusarczyk, A. L.; Lin, A.; Weiss, R.
Foundations for the Design and Implementation of Synthetic Genetic
Circuits. Nature Reviews Genetics 2012,
13 (6), 406–420.
(26)
Kaczmarek, J. A.; Prather, K. L. Effective Use
of Biosensors for High-Throughput Library Screening for Metabolite
Production. Journal of Industrial Microbiology and
Biotechnology 2021, 48 (9-10),
kuab049.
(27)
Yokobayashi, Y.; Weiss, R.; Arnold, F. H.
Directed Evolution of a Genetic Circuit. Proceedings of the National
Academy of Sciences 2002, 99 (26),
16587–16591.
(28)
Wang, G.; Jia, W.; Chen, N.; Zhang, K.; Wang,
L.; Lv, P.; He, R.; Wang, M.; Zhang, D. A GFP-Fusion Coupling FACS
Platform for Advancing the Metabolic Engineering of Filamentous Fungi.
Biotechnology for biofuels 2018, 11,
1–12.
(29)
Yeom, S.-J.; Kim, M.; Kwon, K. K.; Fu, Y.; Rha,
E.; Park, S.-H.; Lee, H.; Kim, H.; Lee, D.-H.; Kim, D.-M.; others. A
Synthetic Microbial Biosensor for High-Throughput Screening of Lactam
Biocatalysts. Nature Communications 2018,
9 (1), 5053.
(30)
Choi, S.-L.; Rha, E.; Lee, S. J.; Kim, H.;
Kwon, K.; Jeong, Y.-S.; Rhee, Y. H.; Song, J. J.; Kim, H.-S.; Lee, S.-G.
Toward a Generalized and High-Throughput Enzyme Screening System Based
on Artificial Genetic Circuits. ACS synthetic biology
2014, 3 (3), 163–171.
(31)
Kurczab, R.; Smusz, S.; Bojarski, A. J. The
Influence of Negative Training Set Size on Machine Learning-Based
Virtual Screening. Journal of cheminformatics
2014, 6, 1–9.
(32)
Maloney, M. P.; Coley, C. W.; Genheden, S.;
Carson, N.; Helquist, P.; Norrby, P.-O.; Wiest, O. Negative Data in Data
Sets for Machine Learning Training. Organic Letters, 2023,
25, 2945–2947.
(33)
Park, K.-H.; Kim, S.; Lee, S.-J.; Cho, J.-E.;
Patil, V. V.; Dumbrepatil, A. B.; Song, H.-N.; Ahn, W.-C.; Joo, C.; Lee,
S.-G.; others. Tetrameric Architecture of an Active Phenol-Bound Form of
the AAA+ Transcriptional Regulator DmpR. Nature communications
2020, 11 (1), 2728.
(34)
Gupta, S.; Saxena, M.; Saini, N.;
Mahmooduzzafar; Kumar, R.; Kumar, A. An Effective Strategy for a
Whole-Cell Biosensor Based on Putative Effector Interaction Site of the
Regulatory DmpR Protein. 2012.
(35)
Kim, H.; Seong, W.; Rha, E.; Lee, H.; Kim, S.
K.; Kwon, K. K.; Park, K.-H.; Lee, D.-H.; Lee, S.-G. Machine Learning
Linked Evolutionary Biosensor Array for Highly Sensitive and Specific
Molecular Identification. Biosensors and Bioelectronics
2020, 170, 112670.
(36)
Pavel, H.; Forsman, M.; Shingler, V. An
Aromatic Effector Specificity Mutant of the Transcriptional Regulator
DmpR Overcomes the Growth Constraints of Pseudomonas Sp. Strain CF600 on
Para-Substituted Methylphenols. Journal of bacteriology
1994, 176 (24), 7550–7557.
(37)
Hecko, S.; Schiefer, A.; Badenhorst, C. P.;
Fink, M. J.; Mihovilovic, M. D.; Bornscheuer, U. T.; Rudroff, F.
Enlightening the Path to Protein Engineering: Chemoselective Turn-on
Probes for High-Throughput Screening of Enzymatic Activity. Chemical
Reviews 2023, 123 (6), 2832–2901.
(38)
Li, H. Minimap2: Pairwise Alignment for
Nucleotide Sequences. Bioinformatics 2018,
34 (18), 3094–3100.
(39)
Danecek, P.; Bonfield, J. K.; Liddle, J.;
Marshall, J.; Ohan, V.; Pollard, M. O.; Whitwham, A.; Keane, T.;
McCarthy, S. A.; Davies, R. M.; others. Twelve Years of SAMtools and
BCFtools. Gigascience 2021, 10 (2),
giab008.
(40)
Quinlan, A. R.; Hall, I. M. BEDTools: A
Flexible Suite of Utilities for Comparing Genomic Features.
Bioinformatics 2010, 26 (6),
841–842.
(41)
Thorvaldsdóttir, H.; Robinson, J. T.; Mesirov,
J. P. Integrative Genomics Viewer (IGV): High-Performance Genomics Data
Visualization and Exploration. Briefings in bioinformatics
2013, 14 (2), 178–192.
(42)
Merkel, D.; others. Docker: Lightweight Linux
Containers for Consistent Development and Deployment. Linux j
2014, 239 (2), 2.
(43)
Technologies, O. N. 2024. https://nanoporetech.com/accuracy.
(44)
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.;
Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žı́dek,
A.; Potapenko, A.; others. Highly Accurate Protein Structure Prediction
with AlphaFold. nature 2021, 596
(7873), 583–589.
(45)
Hodgman, C. E.; Jewett, M. C. Cell-Free
Synthetic Biology: Thinking Outside the Cell. Metabolic
engineering 2012, 14 (3), 261–269.
(46)
King, R. D.; Whelan, K. E.; Jones, F. M.;
Reiser, P. G.; Bryant, C. H.; Muggleton, S. H.; Kell, D. B.; Oliver, S.
G. Functional Genomic Hypothesis Generation and Experimentation by a
Robot Scientist. Nature 2004, 427
(6971), 247–252.
(47)
Köpke, M.; Simpson, S. D. Pollution to
Products: Recycling of ‘Above Ground’carbon by Gas Fermentation.
Current Opinion in Biotechnology 2020,
65, 180–189.
(48)
King, R. D.; Rowland, J.; Oliver, S. G.; Young,
M.; Aubrey, W.; Byrne, E.; Liakata, M.; Markham, M.; Pir, P.; Soldatova,
L. N.; others. The Automation of Science. Science
2009, 324 (5923), 85–89.
(49)
Vögeli, B.; Schulz, L.; Garg, S.; Tarasava, K.;
Clomburg, J. M.; Lee, S. H.; Gonnot, A.; Moully, E. H.; Kimmel, B. R.;
Tran, L.; others. Cell-Free Prototyping Enables Implementation of
Optimized Reverse β-Oxidation
Pathways in Heterotrophic and Autotrophic Bacteria. Nature
communications 2022, 13 (1), 3058.
(50)
Kim, K. J.; Lee, S.-J.; Kim, D.-M. The Use of
Cell-Free Protein Synthesis to Push the Boundaries of Synthetic Biology.
Biotechnology and Bioprocess Engineering 2023,
28 (6), 922–928.
(51)
Kelly, J. Ginkgo Bioworks Launches Ginkgo
Enzyme Services, Enabling Applications Across Pharmaceuticals and
Diagnostics, Food and Agriculture, and Beyond, 2022. https://www.prnewswire.com/news-releases/ginkgo-bioworks-launches-ginkgo-enzyme-services-enabling-applications-across-pharmaceuticals-and-diagnostics-food-and-agriculture-and-beyond-301697912.html.
(52)
TD Cowen, 2020. https://www.cowen.com/transactions/amyris-inc-6-4-2020/.
(53)
Müller, K. M.; Arndt, K. M. Standardization in
Synthetic Biology. Synthetic gene networks: methods and
protocols 2012, 23–43.
(54)
Jainarayanan, A. K.; Galanis, A.; Sreejith, A.;
Suresh, S.; Nakara, A. M.; Kundlatsch, G. E.; Rubio-Sánchez, R. iGEM
Comes of Age: Trends in Its Research Output. Nature
Biotechnology 2021, 39 (12),
1599–1601.
(55)
Galdzicki, M.; Clancy, K. P.; Oberortner, E.;
Pocock, M.; Quinn, J. Y.; Rodriguez, C. A.; Roehner, N.; Wilson, M. L.;
Adam, L.; Anderson, J. C.; others. The Synthetic Biology Open Language
(SBOL) Provides a Community Standard for Communicating Designs in
Synthetic Biology. Nature biotechnology 2014,
32 (6), 545–550.
(56)
Garner, K. L. Principles of Synthetic Biology.
Essays in biochemistry 2021, 65 (5),
791–811.
(57)
Pei, L.; Garfinkel, M.; Schmidt, M. Bottlenecks
and Opportunities for Synthetic Biology Biosafety Standards. Nature
communications 2022, 13 (1), 2175.
(58)
Lux, M. W.; Strychalski, E. A.; Vora, G. J.
Advancing Reproducibility Can Ease the “Hard Truths” of
Synthetic Biology. Synthetic Biology 2023,
8 (1), ysad014.
(59)
Endy, D. Foundations for Engineering Biology.
Nature 2005, 438 (7067),
449–453.
(60)
Purnick, P. E.; Weiss, R. The Second Wave of
Synthetic Biology: From Modules to Systems. Nature reviews Molecular
cell biology 2009, 10 (6), 410–422.
(61)
Bultelle, M.; Casas, A.; Kitney, R. Engineering
Biology and Automation–Replicability as a Design Principle.
Engineering Biology 2024.
(62)
Andrianantoandro, E.; Basu, S.; Karig, D. K.;
Weiss, R. Synthetic Biology: New Engineering Rules for an Emerging
Discipline. Molecular systems biology 2006,
2 (1), 2006–0028.
(63)
Kim, K. H.; Chandran, D.; Sauro, H. M. Toward
Modularity in Synthetic Biology: Design Patterns and Fan-Out. Design
and Analysis of Biomolecular Circuits: Engineering Approaches to Systems
and Synthetic Biology 2011, 117–138.
(64)
Mózsik, L.; Pohl, C.; Meyer, V.; Bovenberg, R.
A.; Nygård, Y.; Driessen, A. J. Modular Synthetic Biology Toolkit for
Filamentous Fungi. ACS Synthetic Biology 2021,
10 (11), 2850–2861.
(65)
Sarand, I.; Skärfstad, E.; Forsman, M.;
Romantschuk, M.; Shingler, V. Role of the DmpR-Mediated Regulatory
Circuit in Bacterial Biodegradation Properties in Methylphenol-Amended
Soils. Applied and environmental microbiology
2001, 67 (1), 162–171.
(66)
Madani, A.; McCann, B.; Naik, N.; Keskar, N.
S.; Anand, N.; Eguchi, R. R.; Huang, P.-S.; Socher, R. Progen: Language
Modeling for Protein Generation. arXiv preprint
arXiv:2004.03497 2020.
(67)
Brandes, N.; Ofer, D.; Peleg, Y.; Rappoport,
N.; Linial, M. ProteinBERT: A Universal Deep-Learning Model of Protein
Sequence and Function. Bioinformatics 2022,
38 (8), 2102–2110.
(68)
Rives, A.; Meier, J.; Sercu, T.; Goyal, S.;
Lin, Z.; Liu, J.; Guo, D.; Ott, M.; Zitnick, C. L.; Ma, J.; Fergus, R.
Biological Structure and Function Emerge from Scaling Unsupervised
Learning to 250 Million Protein Sequences. PNAS
2019. https://doi.org/10.1101/622803.
(69)
Rao, R. M.; Meier, J.; Sercu, T.; Ovchinnikov,
S.; Rives, A. Transformer Protein Language Models Are Unsupervised
Structure Learners. bioRxiv 2020. https://doi.org/10.1101/2020.12.15.422761.
(70)
Rao, R.; Liu, J.; Verkuil, R.; Meier, J.;
Canny, J. F.; Abbeel, P.; Sercu, T.; Rives, A. MSA Transformer.
bioRxiv 2021. https://doi.org/10.1101/2021.02.12.430858.
(71)
Meier, J.; Rao, R.; Verkuil, R.; Liu, J.;
Sercu, T.; Rives, A. Language Models Enable Zero-Shot Prediction of the
Effects of Mutations on Protein Function. bioRxiv
2021. https://doi.org/10.1101/2021.07.09.450648.
(72)
Hsu, C.; Verkuil, R.; Liu, J.; Lin, Z.; Hie,
B.; Sercu, T.; Lerer, A.; Rives, A. Learning Inverse Folding from
Millions of Predicted Structures. ICML 2022.
https://doi.org/10.1101/2022.04.10.487779.
(73)
Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.;
Lu, W.; Smetanin, N.; Santos Costa, A. dos; Fazel-Zarandi, M.; Sercu,
T.; Candido, S.; others. Language Models of Protein Sequences at the
Scale of Evolution Enable Accurate Structure Prediction.
bioRxiv 2022.
(74)
Ferruz, N.; Höcker, B. Controllable Protein
Design with Language Models. Nature Machine Intelligence
2022, 4 (6), 521–532.
(75)
Venter, J. C.; Glass, J. I.; Hutchison, C. A.;
Vashee, S. Synthetic Chromosomes, Genomes, Viruses, and Cells.
Cell 2022, 185 (15), 2708–2724.
(76)
Hutchison III, C. A.; Chuang, R.-Y.; Noskov, V.
N.; Assad-Garcia, N.; Deerinck, T. J.; Ellisman, M. H.; Gill, J.;
Kannan, K.; Karas, B. J.; Ma, L.; others. Design and Synthesis of a
Minimal Bacterial Genome. Science 2016,
351 (6280), aad6253.
(77)
Rapp, J. T.; Bremer, B. J.; Romero, P. A.
Self-Driving Laboratories to Autonomously Navigate the Protein Fitness
Landscape. Nature chemical engineering 2024,
1 (1), 97–107.
(78)
Hughes, R. A.; Ellington, A. D. Synthetic DNA
Synthesis and Assembly: Putting the Synthetic in Synthetic Biology.
Cold Spring Harbor perspectives in biology
2017, 9 (1), a023812.
(79)
Hendling, M.; Barišić, I. In-Silico Design of
DNA Oligonucleotides: Challenges and Approaches. Computational and
Structural Biotechnology Journal 2019,
17, 1056–1065.
(80)
Richardson, S. M.; Wheelan, S. J.; Yarrington,
R. M.; Boeke, J. D. GeneDesign: Rapid, Automated Design of Multikilobase
Synthetic Genes. Genome research 2006,
16 (4), 550–556.
(81)
Richardson, S. M.; Nunley, P. W.; Yarrington,
R. M.; Boeke, J. D.; Bader, J. S. GeneDesign 3.0 Is an Updated Synthetic
Biology Toolkit. Nucleic Acids Research 2010,
38 (8), 2603–2606.
(82)
Villalobos, A.; Ness, J. E.; Gustafsson, C.;
Minshull, J.; Govindarajan, S. Gene Designer: A Synthetic Biology Tool
for Constructing Artificial DNA Segments. BMC bioinformatics
2006, 7, 1–8.
(83)
Hoover, D. M.; Lubkowski, J. DNAWorks: An
Automated Method for Designing Oligonucleotides for PCR-Based Gene
Synthesis. Nucleic acids research 2002,
30 (10), e43–e43.
(84)
SantaLucia Jr, J.; Hicks, D. The Thermodynamics
of DNA Structural Motifs. Annu. Rev. Biophys. Biomol. Struct.
2004, 33 (1), 415–440.
(85)
Panjkovich, A.; Melo, F. Comparison of
Different Melting Temperature Calculation Methods for Short DNA
Sequences. Bioinformatics 2005, 21
(6), 711–722.