Genetic circuit design automation
Programming circuitry for synthetic biology
As synthetic biology techniques become more powerful, researchers are anticipating a future in which the design of biological circuits will be similar to the design of integrated circuits in electronics. Nielsen et al. describe what is essentially a programming language to design computational circuits in living cells. The circuits generated on plasmids expressed in Escherichia coli required careful insulation from their genetic context, but primarily functioned as specified. The circuits could, for example, regulate cellular functions in response to multiple environmental signals. Such a strategy can facilitate the development of more complex circuits by genetic engineering.
Science, this issue p. 10.1126/science.aac7341
Structured Abstract
INTRODUCTION
Cells respond to their environment, make decisions, build structures, and coordinate tasks. Underlying these processes are computational operations performed by networks of regulatory proteins that integrate signals and control the timing of gene expression. Harnessing this capability is critical for biotechnology projects that require decision-making, control, sensing, or spatial organization. It has been shown that cells can be programmed using synthetic genetic circuits composed of regulators organized to generate a desired operation. However, the construction of even simple circuits is time-intensive and unreliable.
RATIONALE
Electronic design automation (EDA) was developed to aid engineers in the design of semiconductor-based electronics. In an effort to accelerate genetic circuit design, we applied principles from EDA to enable increased circuit complexity and to simplify the incorporation of synthetic gene regulation into genetic engineering projects. We used the hardware description language Verilog to enable a user to describe a circuit function. The user also specifies the sensors, actuators, and “user constraints file” (UCF), which defines the organism, gate technology, and valid operating conditions. Cello (www.cellocad.org) uses this information to automatically design a DNA sequence encoding the desired circuit. This is done via a set of algorithms that parse the Verilog text, create the circuit diagram, assign gates, balance constraints to build the DNA, and simulate performance.
RESULTS
Cello designs circuits by drawing upon a library of Boolean logic gates. Here, the gate technology consists of NOT/NOR logic based on repressors. Gate connection is simplified by defining the input and output signals as RNA polymerase (RNAP) fluxes. We found that the gates need to be insulated from their genetic context to function reliably in the context of different circuits. Each gate is isolated using strong terminators to block RNAP leakage, and input interchangeability is improved using ribozymes and promoter spacers. These parts are varied for each gate to avoid breakage due to recombination. Measuring the load of each gate and incorporating this into the optimization algorithms further reduces evolutionary pressure.
Cello was applied to the design of 60 circuits for Escherichia coli, where the circuit function was specified using Verilog code and transformed to a DNA sequence. The DNA sequences were built as specified with no additional tuning, requiring 880,000 base pairs of DNA assembly. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts). Across all circuits, 92% of the 412 output states functioned as predicted.
CONCLUSION
Our work constitutes a hardware description language for programming living cells. This required the co-development of design algorithms with gates that are sufficiently simple and robust to be connected by automated algorithms. We demonstrate that engineering principles can be applied to identify and suppress errors that complicate the compositions of larger systems. This approach leads to highly repetitive and modular genetics, in stark contrast to the encoding of natural regulatory networks. The use of a hardware-independent language and the creation of additional UCFs will allow a single design to be transformed into DNA for different organisms, genetic endpoints, operating conditions, and gate technologies.
Genetic programming using Cello.
A user specifies the desired circuit function in Verilog code, and this is transformed into a DNA sequence. An example circuit is shown (0xF6); red and blue curves are predicted output states for populations of cells, and solid black distributions are experimental flow cytometry data. The outputs are shown for all combinations of sensor states; plus and minus signs indicate the presence or absence of input signal. RBS, ribosome binding site; RPU, relative promoter unit; YFP, yellow fluorescent protein.
Abstract
Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits for Escherichia coli (880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.
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Supplementary Material
Summary
Supplementary Text
Materials and Methods
Figs. S1 to S44
Tables S1 to S10
Supplementary References
Data Files S1 to S4
Resources
References and Notes
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Volume 352 | Issue 6281
1 April 2016
1 April 2016
Copyright
Copyright © 2016, American Association for the Advancement of Science.
Submission history
Received: 4 June 2015
Accepted: 21 January 2016
Published in print: 1 April 2016
Acknowledgments
All data described in this paper are presented in the supplementary materials. Cello can be accessed at www.cellocad.org; the code is open source and maintained via the Nona Research Foundation (www.nonasoftware.org). The insulated gates, circuits, and plasmids defined in the UCF can be obtained via Addgene (www.addgene.org). The E. coli strain used in this UCF is available from New England Biolabs (C3019). D.D. is president and part owner of Lattice Automation, which works on software for synthetic biology. Supported by Office of Naval Research, Multidisciplinary University Research Initiative grant N00014-13-1-0074 (C.A.V. and A.A.K.N.); a Siebel Scholarship (Class of 2015) and Air Force Office of Scientific Research, National Defense Science and Engineering Graduate fellowship FA9550-11-C-0028 (A.A.K.N.); National Institute of General Medical Sciences grant P50 GMO98792 and NSF Synthetic Biology Engineering Research Center grant SynBERC EEC0540879 (C.A.V. and J.S.); NSF grant 1147158 (D.D. and P.V.); the NIST Strategic and Emerging Research Initiative (V.P., E.A.S., and D.R.); the National Research Council postdoctoral program (V.P.); an AWS in Education Grant; and Thermo Fischer (Life Technologies, A114510). We thank S. Bhatia (Boston University) for his work to enumerate the three-input, one-output logic motifs; R. Ghamari (Boston University) for early work on the Cello software foundation and design approach; N. Roehner for technical assistance with SBOL; and E. Oberortner for technical assistance with Eugene. The National Institute of Standards and Technology notes that certain commercial equipment, instruments, and materials are identified in this paper to specify an experimental procedure as completely as possible. In no case does the identification of particular equipment or materials imply a recommendation or endorsement by NIST, nor does it imply that the materials, instruments, or equipment are necessarily the best available for the purpose. The University of California, San Francisco, holds a patent application associated with this work (US20130005590 A1). The set of insulated gates, the plasmid backbones for the circuit and output plasmids (Fig. 1), and the standard RPU plasmid are available via Addgene (www.addgene.org/Christopher_Voigt/). Cello can be accessed as a web-based application (www.cellocad.org), the code for which will be open source and provided on Github (https://github.com/CIDARLAB/cello).
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