Synthetic gene circuits: design with directed evolution

Evaluation of Human Performance Aiding Live Synthetically Engineered Bacteria in a Gut-on-a-Chip

Synbiotics are a new class of live therapeutics employing engineered genetic circuits. The rapid adoption of genetic editing tools has catalyzed the expansion of possible synbiotics, exceeding traditional testing paradigms in terms of both throughput and model complexity. Herein, we present a simplistic gut-chip model using common Caco2 and HT-29 cell lines to establish a dynamic human screening platform for a cortisol sensing tryptamine producing synbiotic for cognitive performance sustainment. The synbiotic, SYN, was engineered from the common probiotic E. coli Nissle 1917 strain. It had the ability to sense cortisol at physiological concentrations, resulting in the activation of a genetic circuit that produces tryptophan decarboxylase and converts bioavailable tryptophan to tryptamine. SYN was successfully cultivated within the gut-chip showing log-phase growth comparable to the wild-type strain. Tryptophan metabolism occurred quickly in the gut compartment when exposed to 5 μM cortisol, resulting in the complete conversion of bioavailable tryptophan into tryptamine. The flux of tryptophan and tryptamine from the gut to the vascular compartment of the chip was delayed by 12 h, as indicated by the detectable tryptamine in the vascular compartment. The gut-chip provided a stable environment to characterize the sensitivity of the cortisol sensor and dynamic range by altering cortisol and tryptophan dosimetry. Collectively, the human gut-chip provided human relevant apparent permeability to assess tryptophan and tryptamine metabolism, production, and transport, enabled host analyses of cellular viability and pro-inflammatory cytokine secretion, and succeeded in providing an efficacy test of a novel synbiotic. Organ-on-a-chip technology holds promise in aiding traditional therapeutic pipelines to more rapidly down select high potential compounds that reduce the failure rate and accelerate the opportunity for clinical intervention.

Synthetic Biology Design as a Paradigm Shift toward Manufacturing Affordable Adeno-Associated Virus Gene Therapies

Gene therapy has demonstrated enormous potential for changing how we combat disease. By directly engineering the genetic composition of cells, it provides a broad range of options for improving human health. Adeno-associated viruses (AAVs) represent a leading gene therapy vector and are expected to address a wide range of conditions in the coming decade. Three AAV therapies have already been approved by the FDA to treat Leber's congenital amaurosis, spinal muscular atrophy, and hemophilia B. Yet these therapies cost around $850,000, $2,100,000, and $3,500,000, respectively. Such prices limit the broad applicability of AAV gene therapy and make it inaccessible to most patients. Much of this problem arises from the high manufacturing costs of AAVs. At the same time, the field of synthetic biology has grown rapidly and has displayed a special aptitude for addressing biomanufacturing problems. Here, we discuss emerging efforts to apply synthetic biology design to decrease the price of AAV production, and we propose that such efforts could play a major role in making gene therapy much more widely accessible.

Design, mutate, screen: Multiplexed creation and arrayed screening of synchronized genetic clocks

A major goal in synthetic biology is coordinating cellular behavior using cell-cell interactions; however, designing and testing complex genetic circuits that function only in large populations remains challenging. Although directed evolution has commonly supplemented rational design methods for synthetic gene circuits, this method relies on the efficient screening of mutant libraries for desired phenotypes. Recently, multiple techniques have been developed for identifying dynamic phenotypes from large, pooled libraries. These technologies have advanced library screening for single-cell, time-varying phenotypes but are currently incompatible with population-level phenotypes dependent on cell-cell communication. Here, we utilize directed mutagenesis and multiplexed microfluidics to develop an arrayed-screening workflow for dynamic, population-level genetic circuits. Specifically, we create a mutant library of an existing oscillator, the synchronized lysis circuit, and discover variants with different period-amplitude characteristics. Lastly, we utilize our screening workflow to construct a transcriptionally regulated synchronized oscillator that functions over long timescales. A record of this paper's transparent peer review process is included in the supplemental information.

Synthetic Biology Approaches for Engineering Next-Generation Adenoviral Gene Therapies

Synthetic biology centers on the design and modular assembly of biological parts so as to construct artificial biological systems. Over the past decade, synthetic biology has blossomed into a highly productive field, yielding advances in diverse areas such as neuroscience, cell-based therapies, and chemical manufacturing. Similarly, the field of gene therapy has made enormous strides both in proof-of-concept studies and in the clinical setting. One viral vector of increasing interest for gene therapy is the adenovirus (Ad). A major part of the Ad's increasing momentum comes from synthetic biology approaches to Ad engineering. Convergence of gene therapy and synthetic biology has enhanced Ad vectors by mitigating Ad toxicity in vivo, providing precise Ad tropisms, and incorporating genetic circuits to make smart therapies which adapt to environmental stimuli. Synthetic biology engineering of Ad vectors may lead to superior gene delivery and editing platforms which could find applications in a wide range of therapeutic contexts.

Synthetic Biological Circuits within an Orthogonal Central Dogma

Synthetic biology strives to reliably control cellular behavior, typically in the form of user-designed interactions of biological components to produce a predetermined output. Engineered circuit components are frequently derived from natural sources and are therefore often hampered by inadvertent interactions with host machinery, most notably within the host central dogma. Reliable and predictable gene circuits require the targeted reduction or elimination of these undesirable interactions to mitigate negative consequences on host fitness and develop context-independent bioactivities. Here, we review recent advances in biological orthogonalization, namely the insulation of researcher-dictated bioactivities from host processes, with a focus on systematic developments that may culminate in the creation of an orthogonal central dogma and novel cellular functions.

Modeling SELEX for regulatory regions using Royal Road and Royal Staircase fitness functions

The field of evolutionary algorithms (EAs) emerged in the area of computer science due to transfer of ideas from biology and developed independently for several decades, enriched with techniques from probability theory, complexity theory and optimization methods. In this paper, we consider some recent results form the EAs theory transferred back into biology. The well-known biotechnological procedure SELEX (Systematic Evolution of Ligands by EXponential enrichment) is viewed as an experimental implementation of an evolutionary algorithm. Theoretical bounds on EAs runtime are applied to model SELEX search for a regulatory region consisting of promoter and enhancer sequences. A comparison of theoretical bounds to the results of computational simulation indicates some cases where the theoretical bounds give favorable prediction, while simulation requires prohibitive computational resource.

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.

Design Methodologies and the Limits of the Engineering-Dominated Conception of Synthetic Biology

Synthetic biology is described as a new field of biotechnology that models itself on engineering sciences. However, this view of synthetic biology as an engineering field has received criticism, and both biologists and philosophers have argued for a more nuanced and heterogeneous understanding of the field. This paper elaborates the heterogeneity of synthetic biology by clarifying the role of design and the variability of design methodologies in synthetic biology. I focus on two prominent design methodologies: rational design and directed evolution. Rational design resembles the design methodology of traditional engineering sciences. However, it is often replaced and complemented by the more biologically-inspired method of directed evolution, which models itself on natural evolution. These two approaches take philosophically different stances to the design of biological systems. Rational design aims to make biological systems more machine-like, whereas directed evolution utilizes variation and emergent features of living systems. I provide an analysis of the methodological basis of these design approaches, and highlight important methodological differences between them. By analyzing the respective benefits and limitations of these approaches, I argue against the engineering-dominated conception of synthetic biology and its "methodological monism", where the rational design approach is taken as the default design methodology. Alternative design methodologies, like directed evolution, should be considered as complementary, not competitive, to rational design.

Directed evolution of Escherichia coli with lower-than-natural plasmid mutation rates

Unwanted evolution of designed DNA sequences limits metabolic and genome engineering efforts. Engineered functions that are burdensome to host cells and slow their replication are rapidly inactivated by mutations, and unplanned mutations with unpredictable effects often accumulate alongside designed changes in large-scale genome editing projects. We developed a directed evolution strategy, Periodic Reselection for Evolutionarily Reliable Variants (PResERV), to discover mutations that prolong the function of a burdensome DNA sequence in an engineered organism. Here, we used PResERV to isolate Escherichia coli cells that replicate ColE1-type plasmids with higher fidelity. We found mutations in DNA polymerase I and in RNase E that reduce plasmid mutation rates by 6- to 30-fold. The PResERV method implicitly selects to maintain the growth rate of host cells, and high plasmid copy numbers and gene expression levels are maintained in some of the evolved E. coli strains, indicating that it is possible to improve the genetic stability of cellular chassis without encountering trade-offs in other desirable performance characteristics. Utilizing these new antimutator E. coli and applying PResERV to other organisms in the future promises to prevent evolutionary failures and unpredictability to provide a more stable genetic foundation for synthetic biology.

Engineered promoters enable constant gene expression at any copy number in bacteria

The internal environment of growing cells is variable and dynamic, making it difficult to introduce reliable parts, such as promoters, for genetic engineering. Here, we applied control-theoretic ideas to design promoters that maintained constant levels of expression at any copy number. Theory predicts that independence to copy number can be achieved by using an incoherent feedforward loop (iFFL) if the negative regulation is perfectly non-cooperative. We engineered iFFLs into Escherichia coli promoters using transcription-activator-like effectors (TALEs). These promoters had near-identical expression in different genome locations and plasmids, even when their copy number was perturbed by genomic mutations or changes in growth medium composition. We applied the stabilized promoters to show that a three-gene metabolic pathway to produce deoxychromoviridans could retain function without re-tuning when the stabilized-promoter-driven genes were moved from a plasmid into the genome.

Balancing gene expression without library construction via a reusable sRNA pool

Balancing protein expression is critical when optimizing genetic systems. Typically, this requires library construction to vary the genetic parts controlling each gene, which can be expensive and time-consuming. Here, we develop sRNAs corresponding to 15nt ‘target’ sequences that can be inserted upstream of a gene. The targeted gene can be repressed from 1.6- to 87-fold by controlling sRNA expression using promoters of different strength. A pool is built where six sRNAs are placed under the control of 16 promoters that span a ∼10³-fold range of strengths, yielding ∼10⁷ combinations. This pool can simultaneously optimize up to six genes in a system. This requires building only a single system-specific construct by placing a target sequence upstream of each gene and transforming it with the pre-built sRNA pool. The resulting library is screened and the top clone is sequenced to determine the promoter controlling each sRNA, from which the fold-repression of the genes can be inferred. The system is then rebuilt by rationally selecting parts that implement the optimal expression of each gene. We demonstrate the versatility of this approach by using the same pool to optimize a metabolic pathway (β-carotene) and genetic circuit (XNOR logic gate).

Parameter estimation for gene regulatory networks: a two-stage MCMC Bayesian approach

Genetic regulatory networks have emerged as a useful way to elucidate the biochemical pathways for biological functions. Yet, determination of the exact parametric forms for these models remain a major challenge. In this paper, we present a novel computational approach implemented in C++ to solve this inverse problem. This takes the form of an optimization stage first after which Bayesian filtering takes place. The key advantage of such a flexible, general and robust approach is that it provides us with a joint probability distribution of the model parameters instead of single estimates, which we can propagate to final predictions. We apply these ideas to time series data from gene circuit models using state space representation. We show that unsound terms from a more generalized model can be efficiently pruned by our approach. We believe our work offers a new insight towards understanding the behaviour, mechanisms and thermodynamics of system biology.

Model-guided combinatorial optimization of complex synthetic gene networks

Constructing gene circuits that satisfy quantitative performance criteria has been a long-standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three-gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high-throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model-guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.

Rapid Diversification of BetI-Based Transcriptional Switches for the Control of Biosynthetic Pathways and Genetic Circuits

Synthetic biologists are in need of genetic switches, or inducible sensor/promoter systems, that can be reliably integrated in multiple contexts. Using a liquid-based selection method, we systematically engineered the choline-inducible transcription factor BetI, yielding various choline-inducible and choline-repressive promoter systems with various input-output characteristics. In addition to having high stringency and a high maximum induction level, they underwent a graded and single-peaked response to choline. Taking advantage of these features, we demonstrated the utility of these systems for controlling the carotenoid biosynthetic pathway and for constructing two-input logic gates. Additionally, we demonstrated the rapidity, throughput, robustness, and cost-effectiveness of our selection method, which facilitates the conversion of natural genetic controlling systems into systems that are designed for various synthetic biology applications.

Strategies for manipulation of oxygen utilization by the electron transfer chain in microbes for metabolic engineering purposes

Microaerobic growth is of importance in ecological niches, pathogenic infections and industrial production of chemicals. The use of low levels of oxygen enables the cell to gain energy and grow more robustly in the presence of a carbon source that can be oxidized and provide electrons to the respiratory chain in the membrane. A considerable amount of information is available on the genes and proteins involved in respiratory growth and the regulation of genes involved in aerobic and anaerobic metabolism. The dependence of regulation on sensing systems that respond to reduced quinones (e.g. ArcB) or oxygen levels that affect labile redox components of transcription regulators (Fnr) are key in understanding the regulation. Manipulation of the amount of respiration can be difficult to control in dense cultures or inadequately mixed reactors leading to inhomogeneous cultures that may have lower than optimal performance. Efforts to control respiration through genetic means have been reported and address mutations affecting components of the electron transport chain. In a recent report completion for intermediates of the ubiquinone biosynthetic pathway was used to dial the level of respiration vs lactate formation in an aerobically grown E. coli culture.

Plant synthetic biology

Plant synthetic biology is an emerging field that combines engineering principles with plant biology toward the design and production of new devices. This emerging field should play an important role in future agriculture for traditional crop improvement, but also in enabling novel bioproduction in plants. In this review we discuss the design cycles of synthetic biology as well as key engineering principles, genetic parts, and computational tools that can be utilized in plant synthetic biology. Some pioneering examples are offered as a demonstration of how synthetic biology can be used to modify plants for specific purposes. These include synthetic sensors, synthetic metabolic pathways, and synthetic genomes. We also speculate about the future of synthetic biology of plants.

Rapid and liquid-based selection of genetic switches using nucleoside kinase fused with aminoglycoside phosphotransferase

The evolutionary design of genetic switches and circuits requires iterative rounds of positive (ON-) and negative (OFF-) selection. We previously reported a rapid OFF selection system based on the kinase activity of herpes simplex virus thymidine kinase (hsvTK) on the artificial mutator nucleoside dP. By fusing hsvTK with the kanamycin resistance marker aminoglycoside-(3')-phosphotransferase (APH), we established a novel selector system for genetic switches. Due to the bactericidal nature of kanamycin and nucleoside-based lethal mutagenesis, both positive and negative selection could be completed within several hours. Using this new selector system, we isolated a series of homoserine lactone-inducible genetic switches with different expression efficiencies from libraries of the Vibrio fischeri lux promoter in two days, using only liquid handling.

An autonomous in vivo dual selection protocol for boolean genetic circuits

Success in synthetic biology depends on the efficient construction of robust genetic circuitry. However, even the direct engineering of the simplest genetic elements (switches, logic gates) is a challenge and involves intense lab work. As the complexity of biological circuits grows, it becomes more complicated and less fruitful to rely on the rational design paradigm, because it demands many time-consuming trial-and-error cycles. One of the reasons is the context-dependent behavior of small assembly parts (like BioBricks), which in a complex environment often interact in an unpredictable way. Therefore, the idea of evolutionary engineering (artificial directed in vivo evolution) based on screening and selection of randomized combinatorial genetic circuit libraries became popular. In this article we build on the so-called dual selection technique. We propose a plasmid-based framework using toxin-antitoxin pairs together with the relaxase conjugative protein, enabling an efficient autonomous in vivo evolutionary selection of simple Boolean circuits in bacteria (E. coli was chosen for demonstration). Unlike previously reported protocols, both on and off selection steps can run simultaneously in various cells in the same environment without human intervention; and good circuits not only survive the selection process but are also horizontally transferred by conjugation to the neighbor cells to accelerate the convergence rate of the selection process. Our directed evolution strategy combines a new dual selection method with fluorescence-based screening to increase the robustness of the technique against mutations. As there are more orthogonal toxin-antitoxin pairs in E. coli, the approach is likely to be scalable to more complex functions. In silico experiments based on empirical data confirm the high search and selection capability of the protocol.

Synthetic Biology: A Bridge between Artificial and Natural Cells

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications.

Synthetic biology at the interface of functional genomics

Functional genomics is considered a powerful tool that helps understand the relation between an organism's genotype and possible phenotypes. Volumes of data generated on several 'omics' platforms have revealed the network complexities underlying biological processes. Systems and synthetic biology have garnered much attention because of the ability to infer and comprehend the uncertainties associated with such complexities. Also, part-wise characterization of the network components (e.g. DNA, RNA, protein) has rendered an engineering perspective in life sciences to build modular and functional devices. This approach can be used to combat one of the many concerns of the world, i.e. in the area of biomedical translational research by designing and constructing novel therapeutic devices to intervene network perturbation in a diseased state to transform to a healthy state.

Thrombin-mediated transcriptional regulation using DNA aptamers in DNA-based cell-free protein synthesis

Realizing the potential of cell-free systems will require development of ligand-sensitive gene promoters that control gene expression in response to a ligand of interest. Here, we describe an approach to designing ligand-sensitive transcriptional control in cell-free systems that is based on the combination of a DNA aptamer that binds thrombin and the T7 bacteriophage promoter. Placement of the aptamer near the T7 promoter, and using a primarily single-stranded template, results in up to a 6-fold change in gene expression in a ligand concentration-dependent manner. We further demonstrate that the sensitivity to thrombin concentration and the fold change in expression can be tuned by altering the position of the aptamer. The results described here pave the way for the use of DNA aptamers to achieve modular regulation of transcription in response to a wide variety of ligands in cell-free systems.

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.

The dawn of evolutionary genome engineering

Genome engineering strategies--such as genome editing, reduction and shuffling, and de novo genome synthesis--enable the modification of specific genomic locations in a directed and combinatorial manner. These approaches offer an unprecedented opportunity to study central evolutionary issues in which natural genetic variation is limited or biased, which sheds light on the evolutionary forces driving complex and extremely slowly evolving traits; the selective constraints on genome architecture; and the reconstruction of ancestral states of cellular structures and networks.

Designing synthetic biology

Synthetic biology is frequently defined as the application of engineering design principles to biology. Such principles are intended to streamline the practice of biological engineering, to shorten the time required to design, build, and test synthetic gene networks. This streamlining of iterative design cycles can facilitate the future construction of biological systems for a range of applications in the production of fuels, foods, materials, and medicines. The promise of these potential applications as well as the emphasis on design has prompted critical reflection on synthetic biology from design theorists and practicing designers from many fields, who can bring valuable perspectives to the discipline. While interdisciplinary connections between biologists and engineers have built synthetic biology via the science and the technology of biology, interdisciplinary collaboration with artists, designers, and social theorists can provide insight on the connections between technology and society. Such collaborations can open up new avenues and new principles for research and design, as well as shed new light on the challenging context-dependence-both biological and social-that face living technologies at many scales. This review is inspired by the session titled "Design and Synthetic Biology: Connecting People and Technology" at Synthetic Biology 6.0 and covers a range of literature on design practice in synthetic biology and beyond. Critical engagement with how design is used to shape the discipline opens up new possibilities for how we might design the future of synthetic biology.

Engineering reduced evolutionary potential for synthetic biology

The field of synthetic biology seeks to engineer reliable and predictable behaviors in organisms from collections of standardized genetic parts. However, unlike other types of machines, genetically encoded biological systems are prone to changes in their designed sequences due to mutations in their DNA sequences after these devices are constructed and deployed. Thus, biological engineering efforts can be confounded by undesired evolution that rapidly breaks the functions of parts and systems, particularly when they are costly to the host cell to maintain. Here, we explain the fundamental properties that determine the evolvability of biological systems. Then, we use this framework to review current efforts to engineer the DNA sequences that encode synthetic biology devices and the genomes of their microbial hosts to reduce their ability to evolve and therefore increase their genetic reliability so that they maintain their intended functions over longer timescales.

Synthetic circuit of inositol phosphorylceramide synthase in Leishmania : a chemical biology approach

Building circuits and studying their behavior in cells is a major goal of systems and synthetic biology. Synthetic biology enables the precise control of cellular states for systems studies, the discovery of novel parts, control strategies, and interactions for the design of robust synthetic systems. To the best of our knowledge, there are no literature reports for the synthetic circuit construction for protozoan parasites. This paper describes the construction of genetic circuit for the targeted enzyme inositol phosphorylceramide synthase belonging to the protozoan parasite Leishmania. To explore the dynamic nature of the circuit designed, simulation was done followed by circuit validation by qualitative and quantitative approaches. The genetic circuit designed for inositol phosphorylceramide synthase (Biomodels Database-MODEL1208030000) shows responsiveness, oscillatory and bistable behavior, together with intrinsic robustness.

MAPK transgenic circuit to improve plant stress-tolerance?

Thanks to their distinctive mode of action in a coordinated switch-like way, their multi-tiered signaling cascades and their involvement in cell responses to multiple internal and external stimuli, MAP kinases offer a remarkable possibility to be assembled into what we can call "MAPK transgenic circuits" to improve cell functions. Such circuit could be used to enhance cell signaling efficiency and boost cell functions for several purposes in plant biotechnology, medicine, and pharmaceutical industry.

Randomized BioBrick assembly: a novel DNA assembly method for randomizing and optimizing genetic circuits and metabolic pathways

The optimization of genetic circuits and metabolic pathways often involves constructing various iterations of the same construct or using directed evolution to achieve the desired function. Alternatively, a method that randomizes individual parts in the same assembly reaction could be used for optimization by allowing for the ability to screen large numbers of individual clones expressing randomized circuits or pathways for optimal function. Here we describe a new assembly method to randomize genetic circuits and metabolic pathways from modular DNA fragments derived from PCR-amplified BioBricks. As a proof-of-principle for this method, we successfully assembled CMY (Cyan-Magenta-Yellow) three-gene circuits using Gibson Assembly that express CFP, RFP, and YFP with independently randomized promoters, ribosome binding sites, transcriptional terminators, and all parts randomized simultaneously. Sequencing results from 24 CMY circuits with various parts randomized show that 20/24 circuits are distinct and expression varies over a 200-fold range above background levels. We then adapted this method to randomize the same parts with enzyme coding sequences from the lycopene biosynthesis pathway instead of fluorescent proteins, designed to independently express each enzyme in the pathway from a different promoter. Lycopene production is improved using this randomization method by about 30% relative to the highest polycistronic-expressing pathway. These results demonstrate the potential of generating nearly 20,000 unique circuit or pathway combinations when three parts are permutated at each position in a three-gene circuit or pathway, and the methodology can likely be adapted to other circuits and pathways to maximize products of interest.

Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits

Strain engineering for synthetic biology and metabolic engineering applications often requires the expression of foreign proteins that can reduce cellular fitness. In order to quantify and visualize the evolutionary stability dynamics in engineered populations of Escherichia coli , we constructed randomized CMY (cyan-magenta-yellow) genetic circuits with independently randomized promoters, ribosome binding sites, and transcriptional terminators that express cyan fluorescent protein (CFP), red fluorescent protein (RFP), and yellow fluorescent protein (YFP). Using a CMY color system allows for a spectrum of different colors to be produced under UV light since the relative ratio of fluorescent proteins vary between circuits, and this system can be used for the visualization of evolutionary stability dynamics. Evolutionary stability results from 192 evolved populations (24 CMY circuits with 8 replicates each) indicate that both the number of repeated sequences and overall expression levels contribute to circuit stability. The most evolutionarily robust circuit has no repeated parts, lower expression levels, and is about 3-fold more stable relative to a rationally designed circuit. Visualization results show that evolutionary dynamics are highly stochastic between replicate evolved populations and color changes over evolutionary time are consistent with quantitative data. We also measured the competitive fitness of different mutants in an evolved population and find that fitness is highest in mutants that express a lower number of genes (0 and 1 > 2 > 3). In addition, we find that individual circuits with expression levels below 10% of the maximum have significantly higher evolutionary stability, suggesting there may be a hypothetical "fitness threshold" that can be used for robust circuit design.

An attempt to construct a (general) mathematical framework to model biological "context-dependence"

Context-dependent nature of biological phenomena is well documented in every branch of biology. While there have been few previous attempts to (implicitly) model various (particular) facets of biological context-dependence, a formal and general mathematical construct to model the wide spectrum of context-dependence, eludes the students of biology. Such an objective model, from both 'bottom-up' as well as 'top-down' perspective, is proposed here to serve as the template to describe the various kinds of context-dependence that we encounter in different branches of biology. Interactions between biological contexts was found to be transitive but non-commutative. It is found that a hierarchical nature of dependence among the biological contexts models the emergent biological properties efficiently. Reasons for these findings are provided in a general model to describe biological reality. Scheme to algorithmically implement the hierarchic structure of organization of biological contexts was proposed with a construct named 'Context tree'. A 'Context tree' based analysis of context interactions among biophysical factors influencing protein structure was performed.

Analysis and design of a genetic circuit for dynamic metabolic engineering

Recent advances in synthetic biology have equipped us with new tools for bioprocess optimization at the genetic level. Previously, we have presented an integrated in silico design for the dynamic control of gene expression based on a density-sensing unit and a genetic toggle switch. In the present paper, analysis of a serine-producing Escherichia coli mutant shows that an instantaneous ON-OFF switch leads to a maximum theoretical productivity improvement of 29.6% compared to the mutant. To further the design, global sensitivity analysis is applied here to a mathematical model of serine production in E. coli coupled with a genetic circuit. The model of the quorum sensing and the toggle switch involves 13 parameters of which 3 are identified as having a significant effect on serine concentration. Simulations conducted in this reduced parameter space further identified the optimal ranges for these 3 key parameters to achieve productivity values close to the maximum theoretical values. This analysis can now be used to guide the experimental implementation of a dynamic metabolic engineering strategy and reduce the time required to design the genetic circuit components.

Synthetic biology and genetic causation

Synthetic biology research is often described in terms of programming cells through the introduction of synthetic genes. Genetic material is seemingly attributed with a high level of causal responsibility. We discuss genetic causation in synthetic biology and distinguish three gene concepts differing in their assumptions of genetic control. We argue that synthetic biology generally employs a difference-making approach to establishing genetic causes, and that this approach does not commit to a specific notion of genetic program or genetic control. Still, we suggest that a strong program concept of genetic material can be used as a successful heuristic in certain areas of synthetic biology. Its application requires control of causal context, and may stand in need of a modular decomposition of the target system. We relate different modularity concepts to the discussion of genetic causation and point to possible advantages of and important limitations to seeking modularity in synthetic biology systems.

Construction of synthetic gene circuits in the Escherichia coli genome

The construction of stable and functional synthetic circuits in bacteria is necessary in the areas of systems and synthetic biology. The common approach using plasmids to carry foreign genetic circuits offers convenience in genetic construction but is poor in genetic stability (e.g., variation in copy number). Genome recombination provides the stable genetic maintenance of synthetic circuits, but is often labor intensive and time consuming when the genetic circuits are complex and the DNA fragments become larger. The method introduced here is modified from that reported by Wanner's group and is available for integration of complex genetic circuits into the Escherichia coli chromosome.

In silico evolution of gene cooption in pattern-forming gene networks

Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC), we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability—a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean) approach is necessary for addressing realistic continuous variation in biochemical parameters.

A Heritable Recombination system for synthetic Darwinian evolution in yeast

Genetic recombination is central to the generation of molecular diversity and enhancement of evolutionary fitness in living systems. Methods such as DNA shuffling that recapitulate this diversity mechanism in vitro are powerful tools for engineering biomolecules with useful new functions by directed evolution. Synthetic biology now brings demand for analogous technologies that enable the controlled recombination of beneficial mutations in living cells. Thus, here we create a Heritable Recombination system centered around a library cassette plasmid that enables inducible mutagenesis via homologous recombination and subsequent combination of beneficial mutations through sexual reproduction in Saccharomyces cerevisiae. Using repair of nonsense codons in auxotrophic markers as a model, Heritable Recombination was optimized to give mutagenesis efficiencies of up to 6% and to allow successive repair of different markers through two cycles of sexual reproduction and recombination. Finally, Heritable Recombination was employed to change the substrate specificity of a biosynthetic enzyme, with beneficial mutations in three different active site loops crossed over three continuous rounds of mutation and selection to cover a total sequence diversity of 10(13). Heritable Recombination, while at an early stage of development, breaks the transformation barrier to library size and can be immediately applied to combinatorial crossing of beneficial mutations for cell engineering, adding important features to the growing arsenal of next generation molecular biology tools for synthetic biology.

Fine-tuning gene networks using simple sequence repeats

The parameters in a complex synthetic gene network must be extensively tuned before the network functions as designed. Here, we introduce a simple and general approach to rapidly tune gene networks in Escherichia coli using hypermutable simple sequence repeats embedded in the spacer region of the ribosome binding site. By varying repeat length, we generated expression libraries that incrementally and predictably sample gene expression levels over a 1,000-fold range. We demonstrate the utility of the approach by creating a bistable switch library that programmatically samples the expression space to balance the two states of the switch, and we illustrate the need for tuning by showing that the switch's behavior is sensitive to host context. Further, we show that mutation rates of the repeats are controllable in vivo for stability or for targeted mutagenesis--suggesting a new approach to optimizing gene networks via directed evolution. This tuning methodology should accelerate the process of engineering functionally complex gene networks.

Synthetic biology: an emerging engineering discipline

Over the past decade, synthetic biology has emerged as an engineering discipline for biological systems. Compared with other substrates, biology poses a unique set of engineering challenges resulting from an incomplete understanding of natural biological systems and tools for manipulating them. To address these challenges, synthetic biology is advancing from developing proof-of-concept designs to focusing on core platforms for rational and high-throughput biological engineering. These platforms span the entire biological design cycle, including DNA construction, parts libraries, computational design tools, and interfaces for manipulating and probing synthetic circuits. The development of these enabling technologies requires an engineering mindset to be applied to biology, with an emphasis on generalizable techniques in addition to application-specific designs. This review aims to discuss the progress and challenges in synthetic biology and to illustrate areas where synthetic biology may impact biomedical engineering and human health.

Evolutionary systems biology: historical and philosophical perspectives on an emerging synthesis

Systems biology (SB) is at least a decade old now and maturing rapidly. A more recent field, evolutionary systems biology (ESB), is in the process of further developing system-level approaches through the expansion of their explanatory and potentially predictive scope. This chapter will outline the varieties of ESB existing today by tracing the diverse roots and fusions that make up this integrative project. My approach is philosophical and historical. As well as examining the recent origins of ESB, I will reflect on its central features and the different clusters of research it comprises. In its broadest interpretation, ESB consists of five overlapping approaches: comparative and correlational ESB; network architecture ESB; network property ESB; population genetics ESB; and finally, standard evolutionary questions answered with SB methods. After outlining each approach with examples, I will examine some strong general claims about ESB, particularly that it can be viewed as the next step toward a fuller modern synthesis of evolutionary biology (EB), and that it is also the way forward for evolutionary and systems medicine. I will conclude with a discussion of whether the emerging field of ESB has the capacity to combine an even broader scope of research aims and efforts than it presently does.

Evolutionary principles underlying structure and response dynamics of cellular networks

The network view in systems biology, in conjunction with the continuing development of experimental technologies, is providing us with the key structural and dynamical features of both cell-wide and pathway-level regulatory, signaling and metabolic systems. These include for example modularity and presence of hub proteins at the structural level and ultrasensitivity and feedback control at the level of dynamics. The uncovering of such features, and the seeming commonality of some of them, makes many systems biologists believe that these could represent design principles that underpin cellular systems across organisms. Here, we argue that such claims on any observed feature requires an understanding of how it has emerged in evolution and how it can shape subsequent evolution. We review recent and past studies that aim to achieve such evolutionary understanding for observed features of cellular networks. We argue that this evolutionary framework could lead to deciphering evolutionary origin and relevance of proposed design principles, thereby allowing to predict their presence or absence in an organism based on its environment and biochemistry and their effect on its future evolution.

Synthetic biology triggers new era of antibiotics development

As a discipline to design and construct organisms with desired properties, synthetic biology has generated rapid progresses in the last decade. Combined synthetic biology with the traditional process, a new universal workflow for drug development has been becoming more and more attractive. The new methodology exhibits more efficient and inexpensive comparing to traditional methods in every aspect, such as new compounds discovery & screening, process design & drug manufacturing. This article reviews the application of synthetic biology in antibiotics development, including new drug discovery and screening, combinatorial biosynthesis to generate more analogues and heterologous expression of biosynthetic gene clusters with systematic engineering the recombinant microbial systems for large scale production.