Next-generation synthetic gene networks

Engineering microbes for tolerance to next-generation biofuels

A major challenge when using microorganisms to produce bulk chemicals such as biofuels is that the production targets are often toxic to cells. Many biofuels are known to reduce cell viability through damage to the cell membrane and interference with essential physiological processes. Therefore, cells must trade off biofuel production and survival, reducing potential yields. Recently, there have been several efforts towards engineering strains for biofuel tolerance. Promising methods include engineering biofuel export systems, heat shock proteins, membrane modifications, more general stress responses, and approaches that integrate multiple tolerance strategies. In addition, in situ recovery methods and media supplements can help to ease the burden of end-product toxicity and may be used in combination with genetic approaches. Recent advances in systems and synthetic biology provide a framework for tolerance engineering. This review highlights recent targeted approaches towards improving microbial tolerance to next-generation biofuels with a particular emphasis on strategies that will improve production.

Synthetic biology: integrated gene circuits

A major goal of synthetic biology is to develop a deeper understanding of biological design principles from the bottom up, by building circuits and studying their behavior in cells. Investigators initially sought to design circuits "from scratch" that functioned as independently as possible from the underlying cellular system. More recently, researchers have begun to develop a new generation of synthetic circuits that integrate more closely with endogenous cellular processes. These approaches are providing fundamental insights into the regulatory architecture, dynamics, and evolution of genetic circuits and enabling new levels of control across diverse biological systems.

Bayesian design of synthetic biological systems

Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, whereas in the latter, we seek to construct the system that produces the data that we would like to observe, i.e., the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. To deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or in silico prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.

The ribosome binding site calculator

The Ribosome Binding Site (RBS) Calculator is a design method for predicting and controlling translation initiation and protein expression in bacteria. The method can predict the rate of translation initiation for every start codon in an mRNA transcript. The method may also optimize a synthetic RBS sequence to achieve a targeted translation initiation rate. Using the RBS Calculator, a protein coding sequence's translation rate may be rationally controlled across a 100,000+ fold range. We begin by providing an overview of the potential biotechnology applications of the RBS Calculator, including the optimization of synthetic metabolic pathways and genetic circuits. We then detail the definitions, methodologies, and algorithms behind the RBS Calculator's thermodynamic model and optimization method. Finally, we outline a protocol for precisely measuring steady-state fluorescent protein expression levels. These methods and protocols provide a clear explanation of the RBS Calculator and its uses.

The interaction graph structure of mass-action reaction networks

Behaviour of chemical networks that are described by systems of ordinary differential equations can be analysed via the associated graph structures. This paper deals with observations based on the interaction graph which is defined by the signs of the Jacobian matrix entries. Some of the important graph structures linked to network dynamics are signed circuits and the nucleus (or Hamiltonian hooping). We use mass-action chemical reaction networks as an example to showcase interesting observations about the aforementioned interaction graph structures. We show that positive circuits and specific nucleus structures (associated to multistationarity) are always present in a great generic class of mass-action chemical and biological networks. The theory of negative circuits remains poorly understood, but there is some evidence that they are indicators of stable periodicity. Here we introduce the concept of non-isolated circuits which indicate the presence of a negative circuit.

Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks

BACKGROUND

The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry.

METHODOLOGY/PRINCIPAL FINDINGS

To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks.

CONCLUSIONS/SIGNIFICANCE

Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template

Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Gene product levels could potentially be decoupled from these changes via built-in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post-transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post-transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability.

Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate

Synthetic biology uses modeling to facilitate the design of new genetic constructions. In particular, it is of utmost importance to model the reaction of the cellular chassis when expressing heterologous systems. We constructed a mathematical model for the response of a bacterial cell chassis under heterologous expression. For this, we relied on previous characterization of the growth-rate dependence on cellular resource availability (in this case, DNA and RNA polymerases and ribosomes). Accordingly, we estimated the maximum capacities of the cell for heterologous expression to be 46% of the total RNA and the 33% of the total protein. To experimentally validate our model, we engineered two genetic constructions that involved the constitutive expression of a fluorescent reporter in a vector with a tunable origin of replication. We performed fluorescent measurements using population and single-cell fluorescent measurements. Our model predicted cell growth for several heterologous constructions under five different culture conditions and various plasmid copy numbers with significant accuracy, and confirmed that ribosomes act as the limiting resource. Our study also confirmed that the bacterial response to synthetic gene expression could be understood in terms of the requirement for cellular resources and could be predicted from relevant cellular parameters.

Bacterial growth laws and their applications

Quantitative empirical relationships between cell composition and growth rate played an important role in the early days of microbiology. Gradually, the focus of the field began to shift from growth physiology to the ever more elaborate molecular mechanisms of regulation employed by the organisms. Advances in systems biology and biotechnology have renewed interest in the physiology of the cell as a whole. Furthermore, gene expression is known to be intimately coupled to the growth state of the cell. Here, we review recent efforts in characterizing such couplings, particularly the quantitative phenomenological approaches exploiting bacterial 'growth laws.' These approaches point toward underlying design principles that can guide the predictive manipulation of cell behavior in the absence of molecular details.

Microbial linguistics: perspectives and applications of microbial cell-to-cell communication

Inter-cellular communication via diffusible small molecules is a defining character not only of multicellular forms of life but also of single-celled organisms. A large number of bacterial genes are regulated by the change of chemical milieu mediated by the local population density of its own species or others. The cell density-dependent "autoinducer" molecules regulate the expression of those genes involved in genetic competence, biofilm formation and persistence, virulence, sporulation, bioluminescence, antibiotic production, and many others. Recent innovations in recombinant DNA technology and micro-/nano-fluidics systems render the genetic circuitry responsible for cell-to-cell communication feasible to and malleable via synthetic biological approaches. Here we review the current understanding of the molecular biology of bacterial intercellular communication and the novel experimental protocols and platforms used to investigate this phenomenon. A particular emphasis is given to the genetic regulatory circuits that provide the standard building blocks which constitute the syntax of the biochemical communication network. Thus, this review gives focus to the engineering principles necessary for rewiring bacterial chemo-communication for various applications, ranging from population-level gene expression control to the study of host-pathogen interactions.

Synthetic biology: putting synthesis into biology

The ability to manipulate living organisms is at the heart of a range of emerging technologies that serve to address important and current problems in environment, energy, and health. However, with all its complexity and interconnectivity, biology has for many years been recalcitrant to engineering manipulations. The recent advances in synthesis, analysis, and modeling methods have finally provided the tools necessary to manipulate living systems in meaningful ways and have led to the coining of a field named synthetic biology. The scope of synthetic biology is as complicated as life itself—encompassing many branches of science and across many scales of application. New DNA synthesis and assembly techniques have made routine customization of very large DNA molecules. This in turn has allowed the incorporation of multiple genes and pathways. By coupling these with techniques that allow for the modeling and design of protein functions, scientists have now gained the tools to create completely novel biological machineries. Even the ultimate biological machinery—a self‐replicating organism—is being pursued at this moment. The aim of this article is to dissect and organize these various components of synthetic biology into a coherent picture. WIREs Syst Biol Med 2011 3 7–20 DOI: 10.1002/wsbm.104

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

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Laboratory Methods and Technologies > Genetic/Genomic Methods

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Laboratory Methods and Technologies > Metabolomics

A multi-functional synthetic gene network: a frequency multiplier, oscillator and switch

We present the design and analysis of a synthetic gene network that performs frequency multiplication. It takes oscillatory transcription factor concentrations, such as those produced from the currently available genetic oscillators, as an input, and produces oscillations with half the input frequency as an output. Analysis of the bifurcation structure also reveals novel, programmable multi-functionality; in addition to functioning as a frequency multiplier, the network is able to function as a switch or an oscillator, depending on the temporal nature of the input. Multi-functionality is often observed in neuronal networks, where it is suggested to allow for the efficient coordination of different responses. This network represents a significant theoretical addition that extends the capabilities of synthetic gene networks.

Synthetic human cell fate regulation by protein-driven RNA switches

Understanding how to control cell fate is crucial in biology, medical science and engineering. In this study, we introduce a method that uses an intracellular protein as a trigger for regulating human cell fate. The ON/OFF translational switches, composed of an intracellular protein L7Ae and its binding RNA motif, regulate the expression of a desired target protein and control two distinct apoptosis pathways in target human cells. Combined use of the switches demonstrates that a specific protein can simultaneously repress and activate the translation of two different mRNAs: one protein achieves both up- and downregulation of two different proteins/pathways. A genome-encoded protein fused to L7Ae controlled apoptosis in both directions (death or survival) depending on its cellular expression. The method has potential for curing cellular defects or improving the intracellular production of useful molecules by bypassing or rewiring intrinsic signal networks.

The control of cell fate and apoptosis is a continuing challenge in synthetic biology. In this study, systems are developed in which an intracellularly expressed genome-encoded protein simultaneously achieves up- and downregulation of two distinct apoptosis pathways.

Early Career Research Award Lecture. Structure, evolution and dynamics of transcriptional regulatory networks

The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the structure and organization of transcriptional networks. (ii) Evolution: I then describe the different mechanisms and forces that influence network evolution and shape network structure. (iii) Dynamics: I discuss studies that have integrated information on dynamics such as mRNA abundance or half-life, with data on transcriptional network in order to elucidate general principles of regulatory network dynamics. In particular, I discuss how cell-to-cell variability in the expression level of TFs could permit differential utilization of the same underlying network by distinct members of a genetically identical cell population. Finally, I conclude by discussing open questions for future research and highlighting the implications for evolution, development, disease and applications such as genetic engineering.

Grand challenge commentary: Chassis cells for industrial biochemical production

Hyper-performing whole-cell catalysts are required for the renewable and sustainable production of petrochemical replacements. Chassis cells—self-replicating minimal machines that can be tailored for the production of specific chemicals—will provide the starting point for designing these hyper-performing 'turbo cells'.

Introduction of customized inserts for s-treamlined assembly and optimization of BioBrick synthetic genetic circuits

BACKGROUND

BioBrick standard biological parts are designed to make biological systems easier to engineer (e.g. assemble, manipulate, and modify). There are over 5,000 parts available in the Registry of Standard Biological Parts that can be easily assembled into genetic circuits using a standard assembly technique. The standardization of the assembly technique has allowed for wide distribution to a large number of users -- the parts are reusable and interchangeable during the assembly process. The standard assembly process, however, has some limitations. In particular it does not allow for modification of already assembled biological circuits, addition of protein tags to pre-existing BioBrick parts, or addition of non-BioBrick parts to assemblies.

RESULTS

In this paper we describe a simple technique for rapid generation of synthetic biological circuits using introduction of customized inserts. We demonstrate its use in Escherichia coli (E. coli) to express green fluorescent protein (GFP) at pre-calculated relative levels and to add an N-terminal tag to GFP. The technique uses a new BioBrick part (called a BioScaffold) that can be inserted into cloning vectors and excised from them to leave a gap into which other DNA elements can be placed. The removal of the BioScaffold is performed by a Type IIB restriction enzyme (REase) that recognizes the BioScaffold but cuts into the surrounding sequences; therefore, the placement and removal of the BioScaffold allows the creation of seamless connections between arbitrary DNA sequences in cloning vectors. The BioScaffold contains a built-in red fluorescent protein (RFP) reporter; successful insertion of the BioScaffold is, thus, accompanied by gain of red fluorescence and its removal is manifested by disappearance of the red fluorescence.

CONCLUSIONS

The ability to perform targeted modifications of existing BioBrick circuits with BioScaffolds (1) simplifies and speeds up the iterative design-build-test process through direct reuse of existing circuits, (2) allows incorporation of sequences incompatible with BioBrick assembly into BioBrick circuits (3) removes scar sequences between standard biological parts, and (4) provides a route to adapt synthetic biology innovations to BioBrick assembly through the creation of new parts rather than new assembly standards or parts collections.

Synthetic toxicology: where engineering meets biology and toxicology

This article examines the implications of synthetic biology (SB) for toxicological sciences. Starting with a working definition of SB, we describe its current subfields, namely, DNA synthesis, the engineering of DNA-based biological circuits, minimal genome research, attempts to construct protocells and synthetic cells, and efforts to diversify the biochemistry of life through xenobiology. Based on the most important techniques, tools, and expected applications in SB, we describe the ramifications of SB for toxicology under the label of synthetic toxicology. We differentiate between cases where SB offers opportunities for toxicology and where SB poses challenges for toxicology. Among the opportunities, we identified the assistance of SB to construct novel toxicity testing platforms, define new toxicity-pathway assays, explore the potential of SB to improve in vivo biotransformation of toxins, present novel biosensors developed by SB for environmental toxicology, discuss cell-free protein synthesis of toxins, reflect on the contribution to toxic use reduction, and the democratization of toxicology through do-it-yourself biology. Among the identified challenges for toxicology, we identify synthetic toxins and novel xenobiotics, biosecurity and dual-use considerations, the potential bridging of toxic substances and infectious agents, and do-it-yourself toxin production.

Classifying DNA assembly protocols for devising cellular architectures

DNA assembly is one of the most fundamental techniques in synthetic biology. Efficient methods can turn traditional DNA cloning into time-saving and higher efficiency practice, which is a foundation to accomplish the dreams of synthetic biologists for devising cellular architectures, reprogramming cellular behaviors, or creating synthetic cells. In this review, typical strategies of DNA assembly are discussed with special emphasis on the assembly of long and multiple DNA fragments into intact plasmids or assembled compositions. Constructively, all reported strategies were categorized into in vivo and in vitro types, and protocols are presented in a functional and practice-oriented way in order to portray the general nature of DNA assembly applications. Significantly, a five-step blueprint is proposed for devising cell architectures that produce valuable chemicals.

Engineering of a synthetic electron conduit in living cells

Engineering efficient, directional electronic communication between living and nonliving systems has the potential to combine the unique characteristics of both materials for advanced biotechnological applications. However, the cell membrane is designed by nature to be an insulator, restricting the flow of charged species; therefore, introducing a biocompatible pathway for transferring electrons across the membrane without disrupting the cell is a significant challenge. Here we describe a genetic strategy to move intracellular electrons to an inorganic extracellular acceptor along a molecularly defined route. To do so, we reconstitute a portion of the extracellular electron transfer chain of Shewanella oneidensis MR-1 into the model microbe Escherichia coli. This engineered E. coli can reduce metal ions and solid metal oxides ∼8× and ∼4× faster than its parental strain. We also find that metal oxide reduction is more efficient when the extracellular electron acceptor has nanoscale dimensions. This work demonstrates that a genetic cassette can create a conduit for electronic communication from living cells to inorganic materials, and it highlights the importance of matching the size scale of the protein donors to inorganic acceptors.

Linear control theory for gene network modeling

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Rationally designed logic integration of regulatory signals in mammalian cells

Molecular-level information processing is essential for 'smart' in vivo nanosystems. Natural molecular computing, such as the regulation of messenger RNA (mRNA) synthesis by special proteins called transcription factors, has inspired engineered systems that can control the levels of mRNA with certain combinations of transcription factors. Here, we show an alternative approach to achieving general-purpose control of mRNA and protein levels by logic integration of transcription factor input signals in mammalian cells. The transcription factors regulate synthetic genes coding for small regulatory RNAs (called microRNAs), which, in turn, control the mRNA of interest (the output) via an RNA interference pathway. The simplicity of these modular interactions makes it possible, in theory, to implement any arbitrary logic relation between the transcription factors and the output. We construct, test and optimize increasingly complex circuits with up to three transcription factor inputs, establishing a platform for in vivo molecular computing.

Tracking, tuning, and terminating microbial physiology using synthetic riboregulators

The development of biomolecular devices that interface with biological systems to reveal new insights and produce novel functions is one of the defining goals of synthetic biology. Our lab previously described a synthetic, riboregulator system that affords for modular, tunable, and tight control of gene expression in vivo. Here we highlight several experimental advantages unique to this RNA-based system, including physiologically relevant protein production, component modularity, leakage minimization, rapid response time, tunable gene expression, and independent regulation of multiple genes. We demonstrate this utility in four sets of in vivo experiments with various microbial systems. Specifically, we show that the synthetic riboregulator is well suited for GFP fusion protein tracking in wild-type cells, tight regulation of toxic protein expression, and sensitive perturbation of stress response networks. We also show that the system can be used for logic-based computing of multiple, orthogonal inputs, resulting in the development of a programmable kill switch for bacteria. This work establishes a broad, easy-to-use synthetic biology platform for microbiology experiments and biotechnology applications.

Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides

A fundamental goal in biotechnology and biology is the development of approaches to better understand the genetic basis of traits. Here we report a versatile method, trackable multiplex recombineering (TRMR), whereby thousands of specific genetic modifications are created and evaluated simultaneously. To demonstrate TRMR, in a single day we modified the expression of >95% of the genes in Escherichia coli by inserting synthetic DNA cassettes and molecular barcodes upstream of each gene. Barcode sequences and microarrays were then used to quantify population dynamics. Within a week we mapped thousands of genes that affect E. coli growth in various media (rich, minimal and cellulosic hydrolysate) and in the presence of several growth inhibitors (beta-glucoside, D-fucose, valine and methylglyoxal). This approach can be applied to a broad range of traits to identify targets for future genome-engineering endeavors.

Genetic flexibility of regulatory networks

Gene regulatory networks are based on simple building blocks such as promoters, transcription factors (TFs) and their binding sites on DNA. But how diverse are the functions that can be obtained by different arrangements of promoters and TF binding sites? In this work we constructed synthetic regulatory regions using promoter elements and binding sites of two noninteracting TFs, each sensing a single environmental input signal. We show that simply by combining these three kinds of elements, we can obtain 11 of the 16 Boolean logic gates that integrate two environmental signals in vivo. Further, we demonstrate how combination of logic gates can result in new logic functions. Our results suggest that simple elements of transcription regulation form a highly flexible toolbox that can generate diverse functions under natural selection.

Positive autoregulation shapes response timing and intensity in two-component signal transduction systems

Positive feedback loops are regulatory elements that can modulate expression output, kinetics and noise in genetic circuits. Transcriptional regulators participating in such loops are often expressed from two promoters, one constitutive and one autoregulated. Here, we investigate the interplay of promoter strengths and the intensity of the stimulus activating the transcriptional regulator in defining the output of a positively autoregulated genetic circuit. Using a mathematical model of two-component regulatory systems, which are present in all domains of life, we establish that positive feedback strongly affects the steady-state output levels at both low and high levels of stimulus if the constitutive promoter of the regulator is weak. By contrast, the effect of positive feedback is negligible when the constitutive promoter is sufficiently strong, unless the stimulus intensity is very high. Furthermore, we determine that positive feedback can affect both transient and steady state output levels even in the simplest genetic regulatory systems. We tested our modeling predictions by abolishing the positive feedback loop in the two-component regulatory system PhoP/PhoQ of Salmonella enterica, which resulted in diminished induction of PhoP-activated genes.

A comparative analysis of synthetic genetic oscillators

Synthetic biology is a rapidly expanding discipline at the interface between engineering and biology. Much research in this area has focused on gene regulatory networks that function as biological switches and oscillators. Here we review the state of the art in the design and construction of oscillators, comparing the features of each of the main networks published to date, the models used for in silico design and validation and, where available, relevant experimental data. Trends are apparent in the ways that network topology constrains oscillator characteristics and dynamics. Also, noise and time delay within the network can both have constructive and destructive roles in generating oscillations, and stochastic coherence is commonplace. This review can be used to inform future work to design and implement new types of synthetic oscillators or to incorporate existing oscillators into new designs.

Synthetic biology: applications come of age

Early synthetic biology designs, namely the genetic toggle switch and repressilator, showed that regulatory components can be characterized and assembled to bring about complex, electronics-inspired behaviours in living systems (for example, memory storage and timekeeping).

Through the characterization and assembly of genetic parts and biological building blocks, many more devices have been constructed, including switches, memory elements, oscillators, pulse generators, digital logic gates, filters and communication modules.

Advances in the field are now allowing expansion beyond small gene networks to the realm of larger biological programs, which hold promise for a wide range of applications, including biosensing, therapeutics and the production of biofuels, pharmaceuticals and biomaterials.

Synthetic biosensing circuits consist of sensitive elements that bind analytes and transducer modules that mobilize cellular responses. Balancing these two modules involves engineering modularity and specificity into the various circuits.

Biosensor sensitive elements include environment-responsive promoters (transcriptional), RNA aptamers (translational) and protein receptors (post-translational).

Biosensor transducer modules include engineered gene networks (transcriptional), non-coding regulatory RNAs (translational) and protein signal-transduction circuits (post-translational).

The contributions of synthetic biology to therapeutics include: engineered networks and organisms for disease-mechanism elucidation, drug-target identification, drug-discovery platforms, therapeutic treatment, therapeutic delivery, and drug production and access.

In the microbial production of biofuels and pharmaceuticals, synthetic biology has supplemented traditional genetic and metabolic engineering efforts by aiding the construction of optimized biosynthetic pathways.

Optimizing metabolic flux through biosynthetic pathways is traditionally accomplished by driving the expression of pathway enzymes with strong, inducible promoters. New synthetic approaches include the rapid diversification of various pathway components, the rational and model-guided assembly of pathway components, and hybrid solutions.

Advances in the synthetic biology field are allowing an expansion beyond small gene networks towards larger biological programs that hold promise for a wide range of applications, including biosensing, therapeutics and the production of biofuels, pharmaceuticals and biomaterials.

Synthetic biology is bringing together engineers and biologists to design and build novel biomolecular components, networks and pathways, and to use these constructs to rewire and reprogram organisms. These re-engineered organisms will change our lives over the coming years, leading to cheaper drugs, 'green' means to fuel our cars and targeted therapies for attacking 'superbugs' and diseases, such as cancer. The de novo engineering of genetic circuits, biological modules and synthetic pathways is beginning to address these crucial problems and is being used in related practical applications.

Synthetic biology approaches in drug discovery and pharmaceutical biotechnology

Synthetic biology is the attempt to apply the concepts of engineering to biological systems with the aim to create organisms with new emergent properties. These organisms might have desirable novel biosynthetic capabilities, act as biosensors or help us to understand the intricacies of living systems. This approach has the potential to assist the discovery and production of pharmaceutical compounds at various stages. New sources of bioactive compounds can be created in the form of genetically encoded small molecule libraries. The recombination of individual parts has been employed to design proteins that act as biosensors, which could be used to identify and quantify molecules of interest. New biosynthetic pathways may be designed by stitching together enzymes with desired activities, and genetic code expansion can be used to introduce new functionalities into peptides and proteins to increase their chemical scope and biological stability. This review aims to give an insight into recently developed individual components and modules that might serve as parts in a synthetic biology approach to pharmaceutical biotechnology.

Design and characterization of molecular tools for a Synthetic Biology approach towards developing cyanobacterial biotechnology

Cyanobacteria are suitable for sustainable, solar-powered biotechnological applications. Synthetic biology connects biology with computational design and an engineering perspective, but requires efficient tools and information about the function of biological parts and systems. To enable the development of cyanobacterial Synthetic Biology, several molecular tools were developed and characterized: (i) a broad-host-range BioBrick shuttle vector, pPMQAK1, was constructed and confirmed to replicate in Escherichia coli and three different cyanobacterial strains. (ii) The fluorescent proteins Cerulean, GFPmut3B and EYFP have been demonstrated to work as reporter proteins in cyanobacteria, in spite of the strong background of photosynthetic pigments. (iii) Several promoters, like P_rnpB and variants of P_rbcL, and a version of the promoter P_trc with two operators for enhanced repression, were developed and characterized in Synechocystis sp. strain PCC6803. (iv) It was shown that a system for targeted protein degradation, which is needed to enable dynamic expression studies, is working in Synechocystis sp. strain PCC6803. The pPMQAK1 shuttle vector allows the use of the growing numbers of BioBrick parts in many prokaryotes, and the other tools herein implemented facilitate the development of new parts and systems in cyanobacteria.

Toward design-based engineering of industrial microbes

Engineering industrial microbes has been hampered by incomplete knowledge of cell biology. Thus an iterative engineering cycle of modeling, implementation, and analysis has been used to increase knowledge of the underlying biology while achieving engineering goals. Recent advances in Systems Biology technologies have drastically improved the amount of information that can be collected in each iteration. As well, Synthetic Biology tools are melding modeling and molecular implementation. These advances promise to move microbial engineering from the iterative approach to a design-oriented paradigm, similar to electrical circuits and architectural design. Genome-scale metabolic models, new tools for controlling expression, and integrated -omics analysis are described as key contributors in moving the field toward Design-based Engineering.

Pairwise selection assembly for sequence-independent construction of long-length DNA

The engineering of biological components has been facilitated by de novo synthesis of gene-length DNA. Biological engineering at the level of pathways and genomes, however, requires a scalable and cost-effective assembly of DNA molecules that are longer than ∼10 kb, and this remains a challenge. Here we present the development of pairwise selection assembly (PSA), a process that involves hierarchical construction of long-length DNA through the use of a standard set of components and operations. In PSA, activation tags at the termini of assembly sub-fragments are reused throughout the assembly process to activate vector-encoded selectable markers. Marker activation enables stringent selection for a correctly assembled product in vivo, often obviating the need for clonal isolation. Importantly, construction via PSA is sequence-independent, and does not require primary sequence modification (e.g. the addition or removal of restriction sites). The utility of PSA is demonstrated in the construction of a completely synthetic 91-kb chromosome arm from Saccharomyces cerevisiae.

Evolved orthogonal ribosome purification for in vitro characterization

We developed orthogonal ribosome−mRNA pairs in which the orthogonal ribosome (O-ribosome) specifically translates the orthogonal mRNA and the orthogonal mRNA is not a substrate for cellular ribosomes. O-ribosomes have been used to create new cellular circuits to control gene expression in new ways, they have been used to provide new information about the ribosome, and they form a crucial part of foundational technologies for genetic code expansion and encoded and evolvable polymer synthesis in cells. The production of O-ribosomes in the cell makes it challenging to study the properties of O-ribosomes in vitro, because no method exists to purify functional O-ribosomes from cellular ribosomes and other cellular components. Here we present a method for the affinity purification of O-ribosomes, via tagging of the orthogonal 16S ribosomal RNA. We demonstrate that the purified O-ribosomes are pure by primer extension assays, and structurally homogenous by gel electrophoresis and sucrose gradients. We demonstrate the utility of this purification method by providing a preliminary in vitro characterization of Ribo-X, an O-ribosome previously evolved for enhanced unnatural amino acid incorporation in response to amber codons. Our data suggest that the basis of Ribo-X’s in vivo activity is a decreased affinity for RF1.

Genome engineering

For more than 50 years, those engineering genetic material have pursued increasingly challenging targets. During that time, the tools and resources available to the genetic engineer have grown to encompass new extremes of both scale and precision, opening up new opportunities in genome engineering. Today, our capacity to generate larger de novo assemblies of DNA is increasing at a rapid pace (with concomitant decreases in manufacturing cost). We are also witnessing potent demonstrations of the power of merging randomness and selection with engineering approaches targeting large numbers of specific sites within genomes. These developments promise genetic engineering with unprecedented levels of design originality and offer new avenues to expand both our understanding of the biological world and the diversity of applications for societal benefit.

Statecharts for gene network modeling

State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks.