Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates’ structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello’s capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users’ expertise, jobs may take minutes or hours to complete.
This is a preview of subscription content, access via your institution
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data that support the findings of this study (i.e., standard UCF libraries, input sensor files and output device files) are openly available at https://doi.org/10.5281/zenodo.4676314.
Code for Cello 2.0 is divided among various openly available repositories. The core circuit design module is available at https://doi.org/10.5281/zenodo.4676314, the code for the web application is available at https://doi.org/10.5281/zenodo.4676310 and the source code of the GUI (which is compiled into the webapp) is available at https://doi.org/10.5281/zenodo.4676300. A file (in .zip format), containing all the source code in the version used in the study, associated test data, parameters and documentation, is openly available at https://publication-artifacts.s3.amazonaws.com/cellov2.zip. The source code is openly distributed in accordance with Boston University’s Data Protection Standards (https://www.bu.edu/policies/data-protection-standards/pdf/) and under the MIT license at https://opensource.org/licenses/MIT.
Cheng, A. A. & Lu, T. K. Synthetic biology: an emerging engineering discipline. Annu. Rev. Biomed. Eng. 14, 155–178 (2012).
Khalil, A. S. & Collins, J. J. Synthetic biology: applications come of age. Nat. Rev. Genet. 11, 367–379 (2010).
Endy, D. Foundations for engineering biology. Nature 438, 449–453 (2005).
Bueso, Y. F. & Tangney, M. Synthetic biology in the driving seat of the bioeconomy. Trends Biotechnol. 35, 373–378 (2017).
Purnick, P. E. & Weiss, R. The second wave of synthetic biology: from modules to systems. Nat. Rev. Mol. Cell Biol. 10, 410–422 (2009).
Mano, M. M. R. & Ciletti, M. D. Digital Design: With an Introduction to the Verilog HDL, VHDL, and SystemVerilog (Pearson, 2018).
Rabaey, J. M., Chandrakasan, A. P. & Nikolić, B. Digital Integrated Circuits: A Design Perspective (Prentice Hall, 2008).
Oliveira, S. M. D. et al. Temperature-dependent model of multi-step transcription initiation in Escherichia coli based on live single-cell measurements. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1005174 (2016).
Tabor, J. J. et al. A synthetic genetic edge detection program. Cell 137, 1272–1281 (2009).
Stephens, K. & Bentley, W. E. Synthetic biology for manipulating quorum sensing in microbial consortia. Trends Microbiol. 28, 633–643 (2020).
Brophy, J. A. N. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).
Nielsen, A. A. K. et al. Genetic circuit design automation. Science 352, aac7341 (2016).
Hasty, J., McMillen, D. & Collins, J. J. Engineered gene circuits. Nature 420, 224–230 (2002).
Andrews, L. B., Nielsen, A. A. K. & Voigt, C. A. Cellular checkpoint control using programmable sequential logic. Science 361, eaap8987 (2018).
Bilitchenko, L. et al. Eugene—a domain specific language for specifying and constraining synthetic biological parts, devices, and systems. PLoS ONE 6, e18882 (2011).
Woodruff, L. B. et al. Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration. Nucleic Acids Res. 45, 1553–1565 (2017).
Hossain, A. et al. Automated design of thousands of nonrepetitive parts for engineering stable genetic systems. Nat. Biotechnol. 38, 1466–1475 (2020).
Huynh, L., Tsoukalas, A., Köppe, M. & Tagkopoulos, I. SBROME: a scalable optimization and module matching framework for automated biosystems design. ACS Synth. Biol. 2, 263–273 (2013).
Yaman, F., Bhatia, S., Adler, A., Densmore, D. & Beal, J. Automated selection of synthetic biology parts for genetic regulatory networks. ACS Synth. Biol. 1, 332–344 (2012).
Beal, J., Lu, T. & Weiss, R. Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks. PLoS ONE 6, e22490 (2011).
Czar, M. J., Cai, Y. & Peccoud, J. Writing DNA with GenoCADTM. Nucleic Acids Res. 37, W40–W47 (2009).
Chen, J., Densmore, D., Ham, T. S., Keasling, J. D. & Hillson, N. J. DeviceEditor visual biological CAD canvas. J. Biol. Eng. 6, 1–12 (2012).
Roehner, N. & Myers, C. J. Directed acyclic graph-based technology mapping of genetic circuit models. ACS Synth. Biol. 3, 543–555 (2014).
Salis, H. M. The ribosome binding site calculator. Methods Enzymol. 498, 19–42 (2011).
Rodrigo, G., Carrera, J. & Jaramillo, A. Genetdes: automatic design of transcriptional networks. Bioinformatics 23, 1857–1858 (2007).
Wilson, E. H., Macklin, C., & Platt, D. Engineering genomes with genotype specification language. in Synthetic Biology 373–398 (Humana Press, 2018).
Pedersen, M. & Phillips, A. Towards programming languages for genetic engineering of living cells. J. R. Soc. Interface 6, S437–S450 (2009).
Wolf, C. Yosys Open Synthesis Suite (2016).
Vaidyanathan, P. et al. A framework for genetic logic synthesis. Proc. IEEE 103, 2196–2207 (2015).
Roehner, N. et al. Sharing structure and function in biological design with SBOL 2.0. ACS Synth. Biol. 5, 498–506 (2016).
McLaughlin, J. A. et al. SynBioHub: a standards-enabled design repository for synthetic biology. ACS Synth. Biol. 7, 682–688 (2018).
Shin, J., Zhang, S., Der, B. S., Nielsen, A. A. & Voigt, C. A. Programming Escherichia coli to function as a digital display. Mol. Syst. Biol. 16, e9401 (2020).
Der, B. S. et al. DNAplotlib: programmable visualization of genetic designs and associated data. ACS Synth. Biol. 6, 1115–1119 (2017).
Häkkinen, A., Oliveira, S. M. D., Neeli-Venkata, R. & Ribeiro, A. S. Transcription closed and open complex formation coordinate expression of genes with a shared promoter region. J. R. Soc. Interface 16, 20190507 (2019).
Park, Y., Espah Borujeni, A., Gorochowski, T. E., Shin, J. & Voigt, C. A. Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads. Mol. Syst. Biol. 16, e9584 (2020).
Taketani, M. et al. Genetic circuit design automation for the gut resident species Bacteroides thetaiotaomicron. Nat. Biotechnol. 38, 962–969 (2020).
Chen, Y. et al. Genetic circuit design automation for yeast. Nat. Microbiol. 5, 1349–1360 (2020).
This work was supported by funding from DARPA award FA8750-17-C-0229 ‘Synergistic Discovery and Design (SD2)’ (D.D., C.A.V., C.J.M., T.S.J. and S.M.D.O.), DARPA award HR011-12-C-0067 ‘Living Foundries: 1000 Molecules’ (C.A.V), US Department of Energy award DE-SC0018368 (C.A.V.), NSF Synthetic Biology Engineering Research Center SA5284-11210 (C.A.V.) and NSF Grant No. 1522074 ‘The Living Computing Project’ (D.D., C.J.M., T.S.J. and S.M.D.O.). The authors also acknowledge the support of William Jackson from D.D.’s CIDAR Lab in providing programming assistance and reviewing the manuscript.
D.D. and C.A.V. are co-founders of Asimov. Asimov is a company that uses software to engineer biology.
Peer review information
Nature Protocols thanks Mark Isalan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Key references using this protocol
Nielsen, A. et al. Science 352, aac7341 (2016): https://doi.org/10.1126/science.aac7341
Chen, Y. et al. Nat. Microbiol. 5, 1349–1360 (2020): https://doi.org/10.1038/s41564-020-0757-2
Vaidyanathan, P. et al. Proc. IEEE 11, 2196–2207 (2015): https://doi.org/10.1109/JPROC.2015.2443832
Key data used in this protocol
Taketani, M. et al. Nat. Biotechnol. 38, 962–969 (2020): https://doi.org/10.1038/s41587-020-0468-5
About this article
Cite this article
Jones, T.S., Oliveira, S.M.D., Myers, C.J. et al. Genetic circuit design automation with Cello 2.0. Nat Protoc 17, 1097–1113 (2022). https://doi.org/10.1038/s41596-021-00675-2
Nature Machine Intelligence (2022)
Neural Computing and Applications (2022)