Alec A. K. Nielsen

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 A programming language is devised for biological regulatory circuits. 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. 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.

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.

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 do not interact with other promoters. Each repressor:promoter pair was converted to a NOT gate and characterized. Used as a set of 16 NOR gates, there are >1054 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.

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.

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.

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

Genetic circuit characterization and debugging using RNA‐seq

0.61 to

Cellular checkpoint control using programmable sequential logic

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.

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

Genetic circuits implement computational operations within a cell. Debugging them is difficult because their function is defined by multiple states (e.g., combinations of inputs) that vary in time. Here, we develop RNA‐seq methods that enable the simultaneous measurement of: (i) the states of internal gates, (ii) part performance (promoters, insulators, terminators), and (iii) impact on host gene expression. This is applied to a three‐input one‐output circuit consisting of three sensors, five NOR/NOT gates, and 46 genetic parts. Transcription profiles are obtained for all eight combinations of inputs, from which biophysical models can extract part activities and the response functions of sensors and gates. Various unexpected failure modes are identified, including cryptic antisense promoters, terminator failure, and a sensor malfunction due to media‐induced changes in host gene expression. This can guide the selection of new parts to fix these problems, which we demonstrate by using a bidirectional terminator to disrupt observed antisense transcription. This work introduces RNA‐seq as a powerful method for circuit characterization and debugging that overcomes the limitations of fluorescent reporters and scales to large systems composed of many parts.

A GUIDE-RNA EXPRESSION SYSTEM FOR A HOST CELL

Building smarter synthetic biological circuits Synthetic genetic and biological regulatory circuits can enable logic functions to form the basis of biological computing; synthetic biology can also be used to control cell behaviors (see the Perspective by Glass and Alon). Andrews et al. used mathematical models and computer algorithms to combine standardized components and build programmable genetic sequential logic circuits. Such circuits can perform regulatory functions much like the biological checkpoint circuits of living cells. Circuits composed of interacting proteins could be used to bypass gene regulation, interfacing directly with cellular pathways without genome modification. Gao et al. engineered proteases that regulate one another, respond to diverse inputs that include oncogene activation, process signals, and conditionally activate responses such as those leading to cell death. This platform should facilitate development of “smart” therapeutic circuits for future biomedical applications. Science, this issue p. eaap8987, p. 1252; see also p. 1199 Progress has been made toward enabling synthetic biological computing via programmable genetic sequential logic circuits in bacteria. INTRODUCTION Modern computing is based on sequential logic, in which the state of a circuit depends both on the present inputs as well as the input history (memory). Implementing sequential logic inside a living cell would enable it to be programmed to progress through discrete states. For example, cells could be designed to differentiate into a multicellular structure or order the multistep construction of a material. A key challenge is that sequential logic requires the implementation of regulatory feedback, which has proven difficult to design and scale. RATIONALE We present a quantitative method to design regulatory circuits that encode sequential logic. Our approach uses NOT gates as the core unit of regulation, in which an input promoter drives the expression of a repressor protein that turns off an output promoter. Each gate is characterized by measuring its response function, in other words, how changing the input affects the output at steady state. Mathematically, the response functions are treated as nullclines, and tools from nonlinear dynamics (phase plane and bifurcation analyses) are applied to predict how combining gates leads to multiple steady states and dynamics. The circuits can be connected to genetic sensors that respond to environmental information. This is used to implement checkpoint control, in which the cell waits for the right signals before continuing to the next state. Circuits are built that instruct Escherichia coli to proceed through a linear or cyclical sequence of states. RESULTS First, pairs of repressors are combined to build the simplest unit of sequential logic: a set-reset (SR) latch, which records a digital bit of information. The SR latches can be easily connected to each other and to sensors because they are designed such that the inputs and outputs are both promoters. Each latch requires two repressors that inhibit each other’s expression. A total of 11 SR latches were designed by using a phase plane analysis. The computation accurately predicts the existence of multiple steady states by using only the empirical NOT gate response functions. A set of 43 circuits was constructed that connects these latches to different combinations of sensors that respond to small molecules in the media. These circuits are shown to reliably hold their state for >48 hours over many cell divisions, only switching states in response to the sensors that connect to the set and reset inputs of the latch. Larger circuits are constructed by combining multiple SR latches and additional feedback loops. A gated data (D) latch, common in electronic integrated circuits, is constructed where one input sets the state of the circuit and the second input locks this state. Up to three SR latches (based on six repressors) are combined in a single cell, thus allowing three bits to be reversibly stored. The performances of these circuits closely match those predicted by the responses of the component gates and a bifurcation analysis. Circuits are designed to implement checkpoint control, in which cells wait indefinitely in a state until the correct signals are received to progress to the next state. The progression can be designed to be cyclical, analogous to cell cycle phases, during which cells progress through a series of states until returning to the starting state. The length of time in each state is indefinite, which is confirmed by demonstrating stability for days when the checkpoint conditions are not met. CONCLUSION This work demonstrates the implementation of sequential logic circuits in cells by combining reliable units of regulation according to simple rules. This approach is conducive to design automation software, which can use these rules to combine gates to build larger circuits. This provides a designable path to building regulatory networks with feedback loops, critical to many cellular functions and ubiquitous in natural networks. This represents a critical step toward performing advanced computing inside of cells. Quantitative design of sequential logic in living cells. Cells can be genetically programmed to respond to temporal stimuli by using complex sequential logic circuits. (Left) Checkpoint control is one such example in which the circuit state (s0 and s1) transitions when the specified input signals are presented. (Middle) Sequential logic circuits can be designed from simple steady-state response functions measured in relative promoter units by using principles of nonlinear dynamics. Bistable latches are used as rewritable memory. The colored symbols represent gates. (Right) The circuit output (Y) was measured for cells that were grown in inputs that were varied over time. The square waveforms indicate the presence or absence of the input signals. Over multiple days, the cells can be cycled through the circuit states or held waiting for the next checkpoint. 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.