Precision design of stable genetic circuits carried in highly‐insulated E. coli genomic landing pads

Abstract Genetic circuits have many applications, from guiding living therapeutics to ordering process in a bioreactor, but to be useful they have to be genetically stable and not hinder the host. Encoding circuits in the genome reduces burden, but this decreases performance and can interfere with native transcription. We have designed genomic landing pads in Escherichia coli at high‐expression sites, flanked by ultrastrong double terminators. DNA payloads >8 kb are targeted to the landing pads using phage integrases. One landing pad is dedicated to carrying a sensor array, and two are used to carry genetic circuits. NOT/NOR gates based on repressors are optimized for the genome and characterized in the landing pads. These data are used, in conjunction with design automation software (Cello 2.0), to design circuits that perform quantitatively as predicted. These circuits require fourfold less RNA polymerase than when carried on a plasmid and are stable for weeks in a recA+ strain without selection. This approach enables the design of synthetic regulatory networks to guide cells in environments or for applications where plasmid use is infeasible

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.Synthetic Biology Engineering Research Center (SynBERC EEC0540879)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014‐13‐1‐0074)United States. Department of Energy (DE‐SC0018368

Genetic circuit characterization and debugging using RNA-seq

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.United States. Defense Advanced Research Projects Agency. Living Foundries Program (award HR0011-13-1-0001)United States. Defense Advanced Research Projects Agency. Living Foundries Program (award HR0011-12-C-0067)United States. Defense Advanced Research Projects Agency. Living Foundries Program (award HR0011-15-C-0084)National Institute of Standards and Technology (U.S.) (award 70NANB16H164 )United States. Office of Naval Research. Multidisciplinary University Research Initiative (grant N00014-13-1-00740Samsung Scholarship Foundatio

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

Recognizing and engineering digital-like logic gates and switches in gene regulatory networks

A central aim of synthetic biology is to build organisms that can perform useful activities in response to specified conditions. The digital computing paradigm which has proved so successful in electrical engineering is being mapped to synthetic biological systems to allow them to make such decisions. However, stochastic molecular processes have graded input-output functions, thus, bioengineers must select those with desirable characteristics and refine their transfer functions to build logic gates with digital-like switching behaviour. Recent efforts in genome mining and the development of programmable RNA-based switches, especially CRISPRi, have greatly increased the number of parts available to synthetic biologists. Improvements to the digital characteristics of these parts are required to enable robust predictable design of deeply layered logic circuits

Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.National Institute of General Medical Sciences (U.S.) (Grant P50 GM098792)National Institute of General Medical Sciences (U.S.) (Grant R01 GM095765)National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (EEC0540879)Life Technologies, Inc. (A114510)National Science Foundation (U.S.). Graduate Research FellowshipUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant 4500000552

Translation Initiation is Controlled by RNA Folding Kinetics via a Ribosome Drafting Mechanism

RNA folding plays an important role in controlling protein synthesis as well as other cellular processes. Existing models have focused on how RNA folding energetics control translation initiation rate under equilibrium conditions but have largely ignored the effects of nonequilibrium RNA folding. We introduce a new mechanism, called “ribosome drafting”, that explains how a mRNA’s folding kinetics and the ribosome’s binding rate collectively control its translation initiation rate. During cycles of translation, ribosome drafting emerges whenever successive ribosomes bind to a mRNA faster than the mRNA can refold, maintaining it in a nonequilibrium state with an acceleration of protein synthesis. Using computational design, time-correlated single photon counting, and expression measurements, we demonstrate that slow-folding and fast-folding RNA structures with equivalent folding energetics can vary protein synthesis rates by 1000-fold. We determine the necessary conditions for ribosome drafting by characterizing mRNAs with rationally designed ribosome binding rates, folding kinetics, and folding energetics, confirming the predictions of a nonequilibrium Markov model of translation. Our results have widespread implications, illustrating how competitive folding and assembly kinetics can shape the gene expression machinery’s sequence–structure–function relationship inside cells

Translation Initiation is Controlled by RNA Folding Kinetics via a Ribosome Drafting Mechanism

RNA folding plays an important role in controlling protein synthesis as well as other cellular processes. Existing models have focused on how RNA folding energetics control translation initiation rate under equilibrium conditions but have largely ignored the effects of nonequilibrium RNA folding. We introduce a new mechanism, called “ribosome drafting”, that explains how a mRNA’s folding kinetics and the ribosome’s binding rate collectively control its translation initiation rate. During cycles of translation, ribosome drafting emerges whenever successive ribosomes bind to a mRNA faster than the mRNA can refold, maintaining it in a nonequilibrium state with an acceleration of protein synthesis. Using computational design, time-correlated single photon counting, and expression measurements, we demonstrate that slow-folding and fast-folding RNA structures with equivalent folding energetics can vary protein synthesis rates by 1000-fold. We determine the necessary conditions for ribosome drafting by characterizing mRNAs with rationally designed ribosome binding rates, folding kinetics, and folding energetics, confirming the predictions of a nonequilibrium Markov model of translation. Our results have widespread implications, illustrating how competitive folding and assembly kinetics can shape the gene expression machinery’s sequence–structure–function relationship inside cells