Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage.

To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.

Cellular checkpoint control using programmable sequential logic.

Biological processes that require orderly progression, such as growth and differentiation, proceed via regulatory checkpoints where the cell waits for signals before continuing to the next state. Implementing such control would allow genetic engineers to divide complex tasks into stages. We present genetic circuits that encode sequential logic to instruct Escherichia coli to proceed through a linear or cyclical sequence of states. These are built with 11 set-reset latches, designed with repressor-based NOR gates, which can connect to each other and sensors. The performance of circuits with up to three latches and four sensors, including a gated D latch, closely match predictions made by using nonlinear dynamics. Checkpoint control is demonstrated by switching cells between multiple circuit states in response to external signals over days.

Deep learning to predict the lab-of-origin of engineered DNA.

Genetic engineering projects are rapidly growing in scale and complexity, driven by new tools to design and construct DNA. There is increasing concern that widened access to these technologies could lead to attempts to construct cells for malicious intent, illegal drug production, or to steal intellectual property. Determining the origin of a DNA sequence is difficult and time-consuming. Here deep learning is applied to predict the lab-of-origin of a DNA sequence. A convolutional neural network was trained on the Addgene plasmid dataset that contained 42,364 engineered DNA sequences from 2230 labs as of February 2016. The network correctly identifies the source lab 48% of the time and 70% it appears in the top 10 predicted labs. Often, there is not a single "smoking gun" that affiliates a DNA sequence with a lab. Rather, it is a combination of design choices that are individually common but collectively reveal the designer.

Genetic circuit design automation.

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 forEscherichia 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.

DNA Assembly in 3D Printed Fluidics.

The process of connecting genetic parts-DNA assembly-is a foundational technology for synthetic biology. Microfluidics present an attractive solution for minimizing use of costly reagents, enabling multiplexed reactions, and automating protocols by integrating multiple protocol steps. However, microfluidics fabrication and operation can be expensive and requires expertise, limiting access to the technology. With advances in commodity digital fabrication tools, it is now possible to directly print fluidic devices and supporting hardware. 3D printed micro- and millifluidic devices are inexpensive, easy to make and quick to produce. We demonstrate Golden Gate DNA assembly in 3D-printed fluidics with reaction volumes as small as 490 nL, channel widths as fine as 220 microns, and per unit part costs ranging from $0.61 to $5.71. A 3D-printed syringe pump with an accompanying programmable software interface was designed and fabricated to operate the devices. Quick turnaround and inexpensive materials allowed for rapid exploration of device parameters, demonstrating a manufacturing paradigm for designing and fabricating hardware for synthetic biology.

Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks.

Genetic circuits require many regulatory parts in order to implement signal processing or execute algorithms in cells. A potentially scalable approach is to use dCas9, which employs small guide RNAs (sgRNAs) to repress genetic loci via the programmability of RNA:DNA base pairing. To this end, we use dCas9 and designed sgRNAs to build transcriptional logic gates and connect them to perform computation in living cells. We constructed a set of NOT gates by designing five synthetic Escherichia coli σ70 promoters that are repressed by corresponding sgRNAs, and these interactions do not exhibit crosstalk between each other. These sgRNAs exhibit high on-target repression (56- to 440-fold) and negligible off-target interactions (< 1.3-fold). These gates were connected to build larger circuits, including the Boolean-complete NOR gate and a 3-gate circuit consisting of four layered sgRNAs. The synthetic circuits were connected to the native E. coli regulatory network by designing output sgRNAs to target an E. coli transcription factor (malT). This converts the output of a synthetic circuit to a switch in cellular phenotype (sugar utilization, chemotaxis, phage resistance).

Permanent genetic memory with >1-byte capacity.

Genetic memory enables the recording of information in the DNA of living cells. Memory can record a transient environmental signal or cell state that is then recalled at a later time. Permanent memory is implemented using irreversible recombinases that invert the orientation of a unit of DNA, corresponding to the [0,1] state of a bit. To expand the memory capacity, we have applied bioinformatics to identify 34 phage integrases (and their cognate attB and attP recognition sites), from which we build 11 memory switches that are perfectly orthogonal to each other and the FimE and HbiF bacterial invertases. Using these switches, a memory array is constructed in Escherichia coli that can record 1.375 bytes of information. It is demonstrated that the recombinases can be layered and used to permanently record the transient state of a transcriptional logic gate.

Genomic mining of prokaryotic repressors for orthogonal logic gates.

Genetic circuits perform computational operations based on interactions between freely diffusing molecules within a cell. When transcription factors are combined to build a circuit, unintended interactions can disrupt its function. Here, we apply 'part mining' to build a library of 73 TetR-family repressors gleaned from prokaryotic genomes. The operators of a subset were determined using an in vitro method, and this information was used to build synthetic promoters. The promoters and repressors were screened for cross-reactions. Of these, 16 were identified that both strongly repress their cognate promoter (5- to 207-fold) and exhibit minimal interactions with other promoters. Each repressor-promoter pair was converted to a NOT gate and characterized. Used as a set of 16 NOT/NOR gates, there are >10(54) circuits that could be built by changing the pattern of input and output promoters. This represents a large set of compatible gates that can be used to construct user-defined circuits.

Advances in genetic circuit design: novel biochemistries, deep part mining, and precision gene expression.

Cells use regulatory networks to perform computational operations to respond to their environment. Reliably manipulating such networks would be valuable for many applications in biotechnology; for example, in having genes turn on only under a defined set of conditions or implementing dynamic or temporal control of expression. Still, building such synthetic regulatory circuits remains one of the most difficult challenges in genetic engineering and as a result they have not found widespread application. Here, we review recent advances that address the key challenges in the forward design of genetic circuits. First, we look at new design concepts, including the construction of layered digital and analog circuits, and new approaches to control circuit response functions. Second, we review recent work to apply part mining and computational design to expand the number of regulators that can be used together within one cell. Finally, we describe new approaches to obtain precise gene expression and to reduce context dependence that will accelerate circuit design by more reliably balancing regulators while reducing toxicity.

Characterization of 582 natural and synthetic terminators and quantification of their design constraints.

Large genetic engineering projects require more cistrons and consequently more strong and reliable transcriptional terminators. We have measured the strengths of a library of terminators, including 227 that are annotated in Escherichia coli--90 of which we also tested in the reverse orientation--and 265 synthetic terminators. Within this library we found 39 strong terminators, yielding >50-fold reduction in downstream expression, that have sufficient sequence diversity to reduce homologous recombination when used together in a design. We used these data to determine how the terminator sequence contributes to its strength. The dominant parameters were incorporated into a biophysical model that considers the role of the hairpin in the displacement of the U-tract from the DNA. The availability of many terminators of varying strength, as well as an understanding of the sequence dependence of their properties, will extend their usability in the forward design of synthetic cistrons.