Insulating gene circuits from context by RNA processing

Context-dependent redesign of robust synthetic gene circuits

Cells provide dynamic platforms for executing exogenous genetic programs in synthetic biology, resulting in highly context-dependent circuit performance. Recent years have seen an increasing interest in understanding the intricacies of circuit-host relationships, their influence on the synthetic bioengineering workflow, and in devising strategies to alleviate undesired effects. We provide an overview of how emerging circuit-host interactions, such as growth feedback and resource competition, impact both deterministic and stochastic circuit behaviors. We also emphasize control strategies for mitigating these unwanted effects. This review summarizes the latest advances and the current state of host-aware and resource-aware design of synthetic gene circuits.

Multiplexed cell-based diagnostic devices for detection of renal biomarkers

The number of synthetic biology-based solutions employed in the medical industry is growing every year. The whole cell biosensors being one of them, have been proven valuable tools for developing low-cost, portable, personalized medicine alternatives to conventional techniques. Based on this concept, we targeted one of the major health problems in the world, Chronic Kidney Disease (CKD). To do so, we developed two novel biosensors for the detection of two important renal biomarkers: urea and uric acid. Using advanced gene expression control strategies, we improved the operational range and the response profiles of each biosensor to meet clinical specifications. We further engineered these systems to enable multiplexed detection as well as an AND-logic gate operating system. Finally, we tested the applicability of these systems and optimized their working dynamics inside complex medium human blood serum. This study could help the efforts to transition from labor-intensive and expensive laboratory techniques to widely available, portable, low-cost diagnostic options.

Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways

The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.

Quantitative and Predictive Genetic Parts for Plant Synthetic Biology

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

Engineered systems of inducible anti-repressors for the next generation of biological programming

Traditionally engineered genetic circuits have almost exclusively used naturally occurring transcriptional repressors. Recently, non-natural transcription factors (repressors) have been engineered and employed in synthetic biology with great success. However, transcriptional anti-repressors have largely been absent with regard to the regulation of genes in engineered genetic circuits. Here, we present a workflow for engineering systems of non-natural anti-repressors. In this study, we create 41 inducible anti-repressors. This collection of transcription factors respond to two distinct ligands, fructose (anti-FruR) or D-ribose (anti-RbsR); and were complemented by 14 additional engineered anti-repressors that respond to the ligand isopropyl β-d-1-thiogalactopyranoside (anti-LacI). In turn, we use this collection of anti-repressors and complementary genetic architectures to confer logical control over gene expression. Here, we achieved all NOT oriented logical controls (i.e., NOT, NOR, NAND, and XNOR). The engineered transcription factors and corresponding series, parallel, and series-parallel genetic architectures represent a nascent anti-repressor based transcriptional programming structure.

Transcriptional anti-repressors have been largely absent in the design of regulated genetic circuits. Here, the authors present a workflow of the engineering of non-natural anti-reperssors that can be built into NOT oriented logic gates.

Transcriptional programming using engineered systems of transcription factors and genetic architectures

The control of gene expression is an important tool for metabolic engineering, the design of synthetic gene networks, and protein manufacturing. The most successful approaches to date are based on modulating mRNA synthesis via an inducible coupling to transcriptional effectors. Here we present a biological programming structure that leverages a system of engineered transcription factors and complementary genetic architectures. We use a modular design strategy to create 27 non-natural and non-synonymous transcription factors using the lactose repressor topology as a guide. To direct systems of engineered transcription factors we employ parallel and series genetic (DNA) architectures and confer fundamental and combinatorial logical control over gene expression. Here we achieve AND, OR, NOT, and NOR logical controls in addition to two non-canonical half-AND operations. The basic logical operations and corresponding parallel and series genetic architectures represent the building blocks for subsequent combinatorial programs, which display both digital and analog performance.

Successful approaches for controlling gene expression modulate mRNA synthesis by coupling it to inducible transcription effectors. Here the authors design 27 non-natural and non-synonymous transcription factors.

Engineering a New Class of Anti-LacI Transcription Factors with Alternate DNA Recognition

The lactose repressor, LacI (I⁺_YQR), is an archetypal transcription factor that has been a workhorse in many synthetic genetic networks. LacI represses gene expression (apo ligand) and is induced upon binding of the ligand isopropyl β-d-1-thiogalactopyranoside (IPTG). Recently, laboratory evolution was used to confer inverted function in the native LacI topology resulting in anti-LacI (antilac) function (I^A_YQR), where IPTG binding results in gene suppression. Here we engineered 46 antilacs with alternate DNA binding function (I^A_ADR). Phenotypically, I^A_ADR transcription factors are the inverse of wild-type I⁺_YQR function and possess alternate DNA recognition (ADR). This collection of bespoke I^A_ADR bind orthogonally to disparate non-natural operator DNA sequences and suppress gene expression in the presence of IPTG. This new class of I^A_ADR gene regulators were designed modularly via the systematic pairing of nine alternate allosteric regulatory cores with six alternate DNA binding domains that interact with complementary synthetic operator DNA sequences. The 46 I^A_ADR identified in this study are also orthogonal to the naturally occurring operator O¹. Finally, a demonstration of full orthogonality was achieved via the construction of synthetic genetic toggle switches composed of two nonsynonymous unit pair operations that control two distinct fluorescent outputs. This new class of I^A_ADR transcription factors will facilitate the expansion of the computational capacity of engineered gene circuits, via the scalable increase in the control over the number of gene outputs by way of the expansion of the number of unique transcription factors (or systems of transcription factors) that can simultaneously regulate one or more promoter(s).

Engineering Translational Resource Allocation Controllers: Mechanistic Models, Design Guidelines, and Potential Biological Implementations

The use of orthogonal ribosomes in combination with dynamic resource allocation controllers is a promising approach for relieving the negative effects of cellular resource limitations on the modularity of synthetic gene circuits. Here, we develop a detailed mechanistic model of gene expression and resource allocation, which when simplified to a tractable level of complexity, allows the rational design of translational resource allocation controllers. Analysis of this model reveals a fundamental design trade-off: that reducing coupling acts to decrease gene expression. Through a sensitivity analysis of the experimentally tunable controller parameters, we identify how each controller design parameter affects the overall closed-loop behavior of the system, leading to a detailed set of design guidelines for optimally managing this trade-off. On the basis of our designs, we evaluated a number of alternative potential experimental implementations of the proposed system using commonly available biological components. Finally, we show that the controller is capable of dynamically allocating ribosomes as needed to restore modularity in a number of more complex synthetic circuits, such as the repressilator, and activation cascades composed of multiple interacting modules.

Engineering synthetic regulatory circuits in plants

Plant synthetic biology is a rapidly emerging field that aims to engineer genetic circuits to function in plants with the same reliability and precision as electronic circuits. These circuits can be used to program predictable plant behavior, producing novel traits to improve crop plant productivity, enable biosensors, and serve as platforms to synthesize chemicals and complex biomolecules. Herein we introduce the importance of developing orthogonal plant parts and the need for quantitative part characterization for mathematical modeling of complex circuits. In particular, transfer functions are important when designing electronic-like genetic controls such as toggle switches, positive/negative feedback loops, and Boolean logic gates. We then discuss potential constraints and challenges in synthetic regulatory circuit design and integration when using plants. Finally, we highlight current and potential plant synthetic regulatory circuit applications.

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuit's behavior. Here, we create a library of genetic activation cascades in E. coli bacteria, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to nonregulatory interactions among genes that reshape the circuit's behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site and DNA copy number to restore the cascade's intended monotonically increasing response. Our results demonstrate how unintended circuit's behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.

A Novel Approach for Gene Expression Optimization through Native Promoter and 5' UTR Combinations Based on RNA-seq, Ribo-seq, and TSS-seq of Streptomyces coelicolor

Streptomycetes are Gram-positive mycelial bacteria, which synthesize a wide range of natural products including over two-thirds of the currently available antibiotics. However, metabolic engineering in Streptomyces species to overproduce a vast of natural products are hampered by a limited number of genetic tools. Here, two promoters and four 5' UTR sequences showing constant strengths were selected based upon multiomics data sets from Streptomyces coelicolor M145, including RNA-seq, Ribo-seq, and TSS-seq, for controllable transcription and translation. A total eight sets of promoter/5' UTR combinations, with minimal interferences of promoters on translation, were constructed using the transcription start site information, and evaluated with the GusA system. Expression of GusA could be controlled to various strengths in three different media, in a range of 0.03- to 2.4-fold, compared to that of the control, ermE*P/Shine-Dalgarno sequence. This method was applied to engineer three previously reported promoters to enhance gene expressions. The expressions of ActII-ORF4 and MetK were also tuned for actinorhodin overproductions in S. coelicolor as examples. In summary, we provide a novel approach and tool for optimizations of gene expressions in Streptomyces coelicolor.

Synthetic tunable amplifying buffer circuit in E. coli

While predictable design of a genetic circuit's output is a major goal of synthetic biology, it remains a significant challenge because DNA binding sites in the cell affect the concentration of available transcription factors (TF). To mitigate this problem, we propose to use a TF that results from the (reversible) phosphorylation of protein substrate as a circuit's output. We demonstrate that by comparatively increasing the amounts of substrate and phosphatase, the TF concentration becomes robust to the presence of DNA binding sites and can be kept at a desired value. The circuit's input/output gain can, in turn, be tuned by changing the relative amounts of the substrate and phosphatase, realizing an amplifying buffer circuit with tunable gain. In our experiments in E. coli, we employ phospho-NRI as the output TF, phosphorylated by the NRII kinase, and dephosphorylated by the NRII phosphatase. Amplifying buffer circuits such as ours could be used to insulate a circuit's output from the context, bringing synthetic biology one step closer to modular design.

A load driver device for engineering modularity in biological networks

The behavior of gene modules in complex synthetic circuits is often unpredictable^1–4. Upon joining modules to create a circuit, downstream elements (such as binding sites for a regulatory protein) apply a load to upstream modules that can negatively affect circuit function^1,5. Here we devise a genetic device named a load driver that mitigates the impact of load on circuit function, and we demonstrate its behavior in Saccharomyces cerevisiae. The load driver implements the design principle of time scale separation: inclusion of the load driver’s fast phosphotransfer processes restores the capability of a slower transcriptional circuit to respond to time-varying input signals even in the presence of substantial load. Without the load driver, we observe circuit behavior that suffers from 76% delay in response time and a 25% decrease in system bandwidth due to load. With the addition of a load driver, circuit performance is almost completely restored. Load drivers will serve as fundamental building blocks in the creation of complex, higher level genetic circuits.

A unified design space of synthetic stripe-forming networks

Synthetic biology is a promising tool to study the function and properties of gene regulatory networks. Gene circuits with predefined behaviours have been successfully built and modelled, but largely on a case-by-case basis. Here we go beyond individual networks and explore both computationally and synthetically the design space of possible dynamical mechanisms for 3-node stripe-forming networks. First, we computationally test every possible 3-node network for stripe formation in a morphogen gradient. We discover four different dynamical mechanisms to form a stripe and identify the minimal network of each group. Next, with the help of newly established engineering criteria we build these four networks synthetically and show that they indeed operate with four fundamentally distinct mechanisms. Finally, this close match between theory and experiment allows us to infer and subsequently build a 2-node network that represents the archetype of the explored design space.

Gene circuit performance characterization and resource usage in a cell-free "breadboard"

The many successes of synthetic biology have come in a manner largely different from those in other engineering disciplines; in particular, without well-characterized and simplified prototyping environments to play a role analogous to wind-tunnels in aerodynamics and breadboards in electrical engineering. However, as the complexity of synthetic circuits increases, the benefits--in cost savings and design cycle time--of a more traditional engineering approach can be significant. We have recently developed an in vitro "breadboard" prototyping platform based on E. coli cell extract that allows biocircuits to operate in an environment considerably simpler than, but functionally similar to, in vivo. The simplicity of this system makes it a promising tool for rapid biocircuit design and testing, as well as for probing fundamental aspects of gene circuit operation normally masked by cellular complexity. In this work, we characterize the cell-free breadboard using real-time and simultaneous measurements of transcriptional and translational activities of a small set of reporter genes and a transcriptional activation cascade. We determine the effects of promoter strength, gene concentration, and nucleoside triphosphate concentration on biocircuit properties, and we isolate the specific contributions of essential biomolecular resources-core RNA polymerase and ribosomes-to overall performance. Importantly, we show how limits on resources, particularly those involved in translation, are manifested as reduced expression in the presence of orthogonal genes that serve as additional loads on the system.

Tuning response curves for synthetic biology

Synthetic biology may be viewed as an effort to establish, formalize, and develop an engineering discipline in the context of biological systems. The ability to tune the properties of individual components is central to the process of system design in all fields of engineering, and synthetic biology is no exception. A large and growing number of approaches have been developed for tuning the responses of cellular systems, and here we address specifically the issue of tuning the rate of response of a system: given a system where an input affects the rate of change of an output, how can the shape of the response curve be altered experimentally? This affects a system's dynamics as well as its steady-state properties, both of which are critical in the design of systems in synthetic biology, particularly those with multiple components. We begin by reviewing a mathematical formulation that captures a broad class of biological response curves and use this to define a standard set of varieties of tuning: vertical shifting, horizontal scaling, and the like. We then survey the experimental literature, classifying the results into our defined categories, and organizing them by regulatory level: transcriptional, post-transcriptional, and post-translational.