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Institute of Medicine (US) Forum on Microbial Threats. The Science and Applications of Synthetic and Systems Biology: Workshop Summary. Washington (DC): National Academies Press (US); 2011.

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The Science and Applications of Synthetic and Systems Biology: Workshop Summary.

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4 and 4,5.


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.

The circuit-like connectivity of biological parts and their ability to collectively process logical operations was first appreciated nearly 50 years ago (Monod and Jacob, 1961). This inspired attempts to describe biological regulation schemes with mathematical models (Glass and Kauffman, 1973; Savageau, 1974; Kauffman, 1974; Glass, 1975) and to apply electrical circuit analogies to biological pathways (McAdams and Arkin, 2000; McAdams and Shapiro, 1995). Meanwhile, breakthroughs in genomic research and genetic engineering (for example, recombinant DNA technology) were supplying the inventory and methods necessary to physically construct and assemble biomolecular parts. As a result, synthetic biology was born with the broad goal of engineering or ‘wiring’ biological circuitry — be it genetic, protein, viral, pathway or genomic — for manifesting logical forms of cellular control. Synthetic biology, equipped with the engineering-driven approaches of modularization, rationalization and modelling, has progressed rapidly and generated an ever-increasing suite of genetic devices and biological modules.

The successful design and construction of the first synthetic gene networks — the genetic toggle switch (Gardner et al., 2000) and the repressilator (Elowitz and Leibler, 2000) (Box A2-1) — showed that engineering-based methodology could indeed be used to build sophisticated, computing-like behaviour into biological systems. In these two cases, basic transcriptional regulatory elements were designed and assembled to realize the biological equivalents of electronic memory storage and timekeeping (Box A2-1). Within the framework provided by these two synthetic systems, biological circuits can be built from smaller, well-defined parts according to model blueprints. They can then be studied and tested in isolation, and their behaviour can be evaluated against model predictions of the system dynamics. This methodology has been applied to the synthetic construction of additional genetic switches (Gardner et al., 2000; Atkinson et al., 2003; Bayer and Smolke, 2005; Deans et al., 2007; Dueber et al., 2003; Friedland et al., 2009; Ham et al., 2006, 2008; Kramer and Fussenegger, 2005; Kramer et al., 2004), memory elements6 (Gardner et al., 2000; Friedland et al., 2009; Ham et al., 2006; Ajo-Franklin et al., 2005) and oscillators (Elowitz and Leibler, 2000; Atkinson et al., 2003; Fung et al., 2005; Stricker et al., 2008, Tigges et al., 2009; Danino et al., 2010), as well as to other electronics-inspired genetic devices, including pulse generators7 (Basu et al., 2004), digital logic gates8 (Anderson et al., 2007; Guet et al., 2002; Rackham and Chin, 2005; Rinaudo et al., 2007; Stojanovic and Stefanovic, 2003; Win and Smolke, 2008), filters9 (Basu et al., 2005; Hooshangi et al., 2005; Sohka et al., 2009) and communication modules (Danino et al., 2010; Basu et al., 2005; Kobayashi et al., 2004; You et al., 2004).

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BOX A2-1

Early Synthetic Biology Designs: Switches and Oscillators. Switches and oscillators that occur in electronic systems are also seen in biology and have been engineered into synthetic biological systems. In electronics, one of the most basic elements for (more...)

Now, 10 years after the demonstration of synthetic biology’s inaugural devices (Gardner et al., 2000; Elowitz and Leibler, 2000), engineered biomolecular networks are beginning to move into the application stage and yield solutions to many complex societal problems. Although work remains to be done on elucidating biological design principles (Mukherji and van Oudenaarden, 2009), this foray into practical applications signals an exciting coming-of-age time for the field.

Here, we review the practical applications of synthetic biology in biosensing, therapeutics and the production of biofuels, pharmaceuticals and novel biomaterials. Many of the examples herein do not fit exclusively or neatly into only one of these three application categories; however, it is precisely this multivalent applicability that makes synthetic biology platforms so powerful and promising.


Cells have evolved a myriad of regulatory circuits — from transcriptional to post-translational — for sensing and responding to diverse and transient environmental signals. These circuits consist of exquisitely tailored sensitive elements that bind analytes and set signal-detection thresholds, and transducer modules that filter the signals and mobilize a cellular response (Box A2-2). The two basic sensing modules must be delicately balanced: this is achieved by programming modularity and specificity into biosensing circuits at the transcriptional, translational and post-translational levels, as described below.

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BOX A2-2

Synthetic Biosensors: Transcriptional and Translational Architectures and Examples. Biosensors consist of two basic modules (see the figure): sensitive elements for recognizing and binding analytes, and transducer modules for transmitting and reporting (more...)

Transcriptional biosensing. As the first dedicated phase of gene expression, transcription serves as one method by which cells mobilize a cellular response to an environmental perturbation. As such, the genes to be expressed, their promoters, rNA polymerase, transcription factors and other parts of the transcription machinery all serve as potential engineering components for transcriptional biosensors. Most synthetic designs have focused on the promoters and their associated transcription factors, given the abundance of known and characterized bacterial, archaeal and eukaryotic environment-responsive promoters, which include the well-known promoters of the Escherichia coli lac, tet and ara operons.

Both the sensory and transducer behaviours of a biosensor can be placed under synthetic control by directly engineering environment-responsive promoter sequences. In fact, this was the early design strategy adopted for establishing inducible expression systems (Brown et al., 1987; Deuschle et al., 1989; Hu and Davidson, 1987; Lutz and Bujard, 1997). By introducing, removing or modifying activator and repressor sites, a promoter’s sensitivity to a molecule can be tuned. Synthetic mammalian transactivation systems are generic versions of this strategy in which an environmentally sensitive transcription factor is fused to a mammalian transactivation domain to cause inducer-dependent changes in gene expression. Synthetic mammalian biosensors based on this scheme have been created for sensing signals such as antibiotics (Fussenegger et al., 2000; Gossen and Bujard, 1992; Weber et al., 2002), quorum-sensing molecules (Neddermann et al., 2003; Weber et al., 2003b), gases and metabolites (Malphettes et al., 2005; Mullick et al., 2006; Weber et al., 2006; Weber et al., 2004), and temperature changes (Boorsma et al., 2000; Weber et al., 2003a). Fussenegger and colleagues have even incorporated this transgene design into mammalian circuits, creating synthetic networks that are responsive to electrical signals (Weber et al., 2009).

Although the engineering of environment-responsive promoters has been valuable, additional control over modularity10 and specificity can be achieved by embedding environment-responsive promoters11 in engineered gene networks. Achieving true modularity with genetic parts is inherently difficult because of unintended interference among native and synthetic parts and therefore requires careful decoupling of functional modules. One such modular design strategy was used by Kobayashi et al. (2004) to develop whole-cell E. coli biosensors that respond to signals in a programmable fashion. In this design, a sensory module (that is, an environment-responsive promoter and associated transcription factor) was coupled to an engineered gene circuit that functions like a central processing unit. E. coli cells were programmed to respond to a deleterious endogenous input — specifically, DNA-damaging stimuli, such as ultraviolet radiation or mitomycin C. The gene circuit, which was chosen to be the toggle switch (Box A2-1), processes the incoming sensory information and flips from an ‘OFF’ to an ‘ON’ state when a signal threshold is exceeded. Because the biosensor has a decoupled, modular nature, it can be wired to any desired output, from the expression of a standard fluorescent reporter to the activation of natural phenotypes, such as biofilm formation (for example, through expression of traA) or cell suicide (for example, through expression of ccdB).

Sometimes a single signal may be too general to characterize or define an environment. For such situations, Anderson et al. (2007) devised a transcriptional AND gate that could be used to integrate multiple environmental signals into a single genetic circuit (Box A2-2), therefore programming the desired level of biosensing specificity. Genetic biosensors of this sort could be useful for communicating the state of a specific microenvironment (for example, in an industrial bioreactor) within a ‘sea’ of environmental conditions, such as temperature, metabolite levels or cell density.

Translational biosensing. RNA molecules have a diverse and important set of cellular functions (Eddy, 2001). Non-coding RNAs can splice and edit RNA, modify ribosomal RNA, catalyse biochemical reactions and regulate gene expression at the level of transcription or translation (Eddy, 2001; Doudna and Cech, 2002; Guerrier-Takada et al., 1983; Kruger et al., 1982). The regulatory subset of non-coding RNAs (Lee et al., 1993; Stougaard and Nordstrom, 1981; Wagner and Simons, 1994) is well-suited for rational design (Isaacs et al., 2006) and, in particular, for biosensing applications. Many regulatory RNA molecules are natural environmental sensors (Gelfand et al., 1999; Johansson et al., 2002; Lease and Belfort, 2000; Majdalani et al., 2002; Mandal et al., 2003; Mironov et al., 2002; Morita et al., 1999; Winkler et al., 2002; Winkler et al., 2004), and because of their ability to take on complex structures defined by their sequence, these molecules can mediate diverse modular functions across distinct sequence domains. Riboswitches (Winkler and Breaker, 2005), for instance, bind specific small-molecule ligands through aptamer12 domains and induce conformational changes in the 5′ UTR of their own mRNA, thereby regulating gene expression. Aptamer domains that are modelled after riboswitches are versatile and widely used sensitive elements for RNA-based biosensing. The choice and number of aptamer domains can provide control over specificity. Building an entire RNA-based biosensor typically requires coupling an aptamer domain (the sensitive element) with a post-transcriptional regulatory domain (the transducer module) on a modular RNA molecule scaffold.

Antisense RNAs13 (Wagner and Simons, 1994; Good, 2003) are one such class of natural regulatory RNAs that can control gene expression through post-transcriptional mechanisms. By linking a riboswitch aptamer to an antisense repressor on a single RNA molecule, Bayer and Smolke (2005) engineered trans-acting, ligand-responsive riboregulators14 of gene expression in Saccharomyces cerevisiae (Box A2-2). Binding of the aptamer to its ligand (for example, the small molecule theophylline) induces a conformational change in the RNA sensor that either sequesters the antisense domain in a stable stem loop (ON switch) or liberates it to inhibit translation of an output gene reporter (OFF switch). As a result of the cooperative dependence on both ligand and target mRNA, this biosensor shows binary-like switching at a threshold ligand concentration, similar to the genetic toggle design. Importantly, this detection threshold can be adjusted by altering the RNA sequence and therefore the thermodynamic properties of the structure. In principle, the ‘antiswitch’ framework is modular; in other words, aptamers for different ligands and antisense stems targeting different downstream genes could be incorporated into the scaffold to create new sensors. In practice, developing new sensors by aptamer and antisense replacement often involves re-screening compatible secondary structures to create functioning switches. In the future, this platform could be combined with rapid, in vitro aptameric selection techniques (Cox et al., 2002; Ellington and Szostak, 1990; Hermann and Patel, 2000; Tuerk and Gold, 1990) for generating a suite of RNA biosensors that report on the levels of various mRNA species and metabolites in a cell. However, here it should also be noted that aptamers show specificity for a biased ligand space, and as a result aptamers for a target ligand cannot always be found.

Another method for transducing the sensory information captured by aptamer domains is to regulate translation through RNA self-cleavage (Winkler et al., 2004; Winkler and Breaker, 2005). RNA cleavage is catalysed by ribozymes, some of which naturally possess aptameric domains and are responsive to metabolites (Winkler et al., 2004). Yen et al. (2004) took advantage of this natural framework and encoded ligand-sensitive ribozymes in the mRNA sequences of reporter genes. In the absence of its cognate ligand, constitutive autocleavage of the reporter mRNA resulted in little or no signal. The RNA biosensor is flipped when the cognate ligand is present to inhibit the ribozyme’s activity. Similar to the ‘antiswitch’ framework (and with the same technical challenges), these engineered RNAs could potentially be used as endogenous sensors for reporting on a variety of intracellular species and metabolites.

Post-translational biosensing. Signal transduction pathways show vast diversity and complexity. Factors such as the nature of the molecular interactions, the number of interconnected proteins in a cascade and the use of spatial mechanisms dictate which signals are transmitted, whether a signal is amplified or attenuated and the dynamics of the response. Despite the multitude of factors and interacting components, signal transduction pathways are essentially hierarchical schemes based on sensitive elements and downstream transducer modules, and as such can be rationalized for engineering protein-based biosensors.

The primary sensitive element for most signal transduction pathways is the protein receptor. Whereas environment-responsive promoters and RNA aptamers are typically identified from nature or selected with high-throughput combinatorial methods, protein receptors can be designed de novo at the level of molecular interactions. For instance, Looger et al. (2003) devised a computational method for redesigning natural protein receptors to bind new target ligands. Starting with a ‘basis’ of five proteins from the E. coli periplasmic binding protein (PBP) superfamily, the researchers replaced each of the wild-type ligands with a new, non-native target ligand and then used an algorithm to combinatorially explore all binding-pocket-residue mutations and ligand-docking configurations. This procedure was used to predict novel receptors for trinitrotoluene (TNT; a carcinogen and explosive), L-lactate (a medically-important metabolite) and serotonin (a chemical associated with psychiatric conditions). The predicted receptor designs were experimentally confirmed to be strong and specific in vitro sensors, as well as in vivo cell-based biosensors.

Protein receptors, such as the ones discussed above, are typically membrane-bound; they trigger protein signalling cascades that ultimately result in a cellular response. However, several synthetic methods can be used to transmit captured sensory information in a tunable and desirable manner. Skerker et al. (2008) rationally rewired the transmission of information through two-component systems15 by identifying rules governing the specificity of a histidine kinase to its cognate response regulator. Alternatively, engineered protein scaffolds can be designed to physically recruit pathway modulators and synthetically reshape the dynamical response behaviour of a system (Bashor et al., 2008) (Box A2-3). This constitutes a modular method for programming protein-based biosensors to have any desired response, including accelerated, delayed or ultrasensitive responses, to upstream signals.

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BOX A2-3

Synthetic Biosensors: Post-translational and Hybrid Architectures and Examples. A basic biosensor has two modules (see the figure): the sensitive element recognizes and binds analytes, whereas the transducer module transmits and reports signals. Post-translational (more...)

Hybrid approaches. Combining synthetic transcriptional, translational and post-translational circuits into hybrid solutions and harnessing desired characteristics from each could lead to the creation of cell-based biosensors that are as robust as those of natural organisms. Using a synthetic hybrid approach, Voigt and colleagues (Levskaya et al., 2005; Levskaya et al., 2009; Tabor et al., 2009) developed E. coli-based optical sensors. A synthetic sensor kinase was engineered to allow cells to identify and report the presence of red light. As a result, a bacterial lawn of the engineered cells could faithfully ‘print’ a projected image in the biological equivalent of photographic film. Specifically, a membrane-bound photoreceptor from cyanobacteria was fused to an E. coli intracellular histidine kinase to induce light-dependent changes in gene expression (Levskaya et al., 2005) (Box A2-3). In a clever example of its use, the bacterial optical sensor was applied in image edge detection (Tabor et al., 2009). In this case, by wiring the optical sensor to transcriptional circuits that perform cell–cell communication (the quorum-sensing 16 system from Vibrio fischeri) and logical functions (Box A2-3), the researchers programmed only the cells that receive light and directly neighbour cells that do not receive light to produce a pigment, allowing the edges of a projected image to be traced. This work demonstrates that complex behaviour can emerge from properly wiring together smaller genetic programs, and that these programs can lead to unique real-world applications.


Human health is afflicted by new and old foes, including emergent drug-resistant microbes, cancer and obesity. Meanwhile, progress in medicine is faced with challenges at each stage of the therapeutic spectrum, ranging from the drying up of pharmaceutical pipelines to limited global access to viable medicines. In a relatively short amount of time, synthetic biology has made promising strides in reshaping and streamlining this spectrum (Box A2-4). Indeed, the rational and model-guided construction of biological parts is enabling new therapeutic platforms, from the identification of disease mechanisms and drug targets to the production and delivery of small molecules.

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BOX A2-4

The Impact of Synthetic Biology on the Therapeutic Spectrum. Part a of the figure shows a synthetic mammalian gene circuit that enabled drug discovery for antituberculosis compounds (Weber etal., 2008). The antibiotic ethionamide is rendered cytotoxic (more...)

Disease mechanism. An electrical engineer is likely to prototype portions of a circuit on a ‘breadboard’ before printing it as an entire integrated circuit. This allows for the rigorous testing of submodules in an isolated, well-characterized environment. Similarly, synthetic biology provides a framework for synthetically reconstructing natural biological systems to explore how pathological behaviours may emerge. This strategy was used to give mechanistic insights into a primary immunodeficiency, agammaglobulinaemia, in which patients cannot generate mature B cells and as a result are unable to properly fight infections (Ferrari et al., 2007). The researchers developed a synthetic testbed by systematically reconstructing the various components of the human B cell antigen receptor (BCr) signalling pathway in an orthogonal environment.17 This allowed them to identify network topology features that trigger BCR signalling and assembly. A rare mutation in the immunoglobulin-β-encoding gene was identified in one patient and introduced into the synthetic system, in which it was shown to abolish assembly of the BCR on the cell surface, thereby linking this faulty pathway component with disease onset. Pathogenic viral genomes can similarly be reconstructed for studying the molecular underpinnings of infectious disease pandemics. For instance, synthetic reconstruction of the severe acquired respiratory syndrome (SARS) coronavirus (Becker et al., 2008) and the 1918 Spanish influenza virus (Tumpey et al., 2005) helped to identify genetic mutations that may have conferred human tropism and increased virulence.

Drug-target identification. Building up synthetic pathways and systems from individual parts is one way of identifying disease mechanisms and therapeutic targets. Another is to deploy synthetic biology devices to systematically probe the function of individual components of a natural pathway. Our group, for instance, has engineered modular riboregulators that can be used to tune the expression of a toxic protein or any gene in a biological network (Isaacs et al., 2004). To achieve post-transcriptional control over a target gene, the mRNA sequence of the riboregulator 5′ UTR is designed to form a hairpin structure that sequesters the ribosomal binding site (RBS) and prevents ribosome access to it. Translational repression of this cis-repressed mRNA can be alleviated by an independently regulated transactivating RNA that targets the stem-loop for unfolding. Engineered riboregulators have been used to tightly regulate the expression of CcdB, a toxic bacterial protein that inhibits DNA gyrase,18 to gain a better understanding of the sequence of events leading to induced bacterial cell death (Dwyer et al., 2007). These synthetic biology studies, in conjunction with systems biology studies of quinolones (antibiotics that inhibit gyrase) (Dwyer et al., 2007), led to the discovery that all major classes of bactericidal antibiotics induce a common cellular death pathway by stimulating oxidative damage (Kohanski et al., 2007, 2008). This work provided new insights into how bacteria respond to lethal stimuli and paved the way for the development of more effective antibacterial therapies.

Drug discovery. After a faulty pathway component or target is identified, whole-cell screening assays can be designed using synthetic biology strategies for drug discovery. As a demonstration of this approach, Fussenegger and colleagues (Weber et al., 2008) developed a synthetic platform for screening small molecules that could potentiate a Mycobacterium tuberculosis antibiotic (Box A2-4). ethionamide, currently the last line of defence in the treatment of multidrug-resistant tuberculosis, depends on activation by the M. tuberculosis enzyme EthA for efficacy. However, due to transcriptional repression of ethA by the protein EthR, ethionamide-based therapy is often rendered ineffective. To address this problem, the researchers designed a synthetic mammalian gene circuit that featured an EthR-based transactivator of a reporter gene and used it to screen for and identify EthR inhibitors that could abrogate resistance to ethionamide. Importantly, because the system is a cell-based assay, it intrinsically enriches for inhibitors that are non-toxic and membrane-permeable to mammalian cells, which are key drug criteria as M. tuberculosis is an intracellular pathogen. This framework, in which drug discovery is applied to whole cells that have been engineered with circuits that highlight a pathogenic mechanism, could be extended to other diseases and phenotypes.

Therapeutic treatment. Synthetic biology devices have additionally been developed to serve as therapies themselves. Entire engineered viruses and organisms can be programmed to target specific pathogenic agents and pathological mechanisms. For instance, in two separate studies (Lu and Collins, 2007, 2009) researchers used engineered bacteriophages to combat antibiotic-resistant bacteria by endowing them with genetic mechanisms that target and thwart bacterial mechanisms for evading antibiotic action. The first study was prompted by the observation that biofilms,19 in which bacteria are encapsulated in an extracellular matrix, have inherent resistance to antimicrobial therapies and are sources of persistent infections. To more effectively penetrate this protective environment, T7 phage was engineered to express the biofilm matrix-degrading enzyme dispersin B (DspB) upon infection (Lu and Collins, 2007). The two-pronged attack of T7 expressing DspB and phage-induced lysis fuelling the creation and spread of DspB resulted in the removal of 99.997% of the biofilm bacterial cells. In the second study (Lu and Collins, 2009), it was suggested that inhibition of certain bacterial genetic programs could improve the effectiveness of current antibiotic therapies. In this case, bacteriophages were deliberately designed to be non-lethal so as not to elicit resistance mechanisms; instead, a non-lytic M13 phage was used to suppress the bacterial SOS DNA-damage response by overexpression of its repressor, lexA3. The engineered bacteriophage significantly enhanced killing by three major classes of antibiotics in traditional cell culture and in E. coli-infected mice, potentiated killing of antibiotic-resistant bacteria and, importantly, reduced the incidence of cells with antibiotic-induced resistance.

Synthetically engineered viruses and organisms that are able to sense and link their therapeutic activity to pathological cues may be useful in the treatment of cancer, in which current therapies often indiscriminately attack tumours and normal tissues. For instance, adenoviruses were programmed to couple their replication to the state of the p53 pathway in human cells (Ramachandra et al., 2001). Normal p53 production would result in inhibition of a crucial viral replication component, whereas a defunct p53 pathway, which is characteristic of tumour cells, would allow viral replication and cell killing. In another demonstration of translational synthetic biology applied to cancer therapy, Voigt and colleagues (Anderson et al., 2006) developed cancer-targeting bacteria and linked their ability to invade the cancer cells to specific environmental signals. Constitutive expression of the heterologous invasin (inv) gene (from Yersinia pseudotuberculosis) can induce E. coli cells to invade both normal human cell lines and cancer cell lines. So, to preferentially invade cancer cells, the researchers placed inv under the control of transcriptional operons that are activated by environmental signals specific to the tumour microenvironment. These engineered bacteria could be made to carry or synthesize cancer therapies for the treatment of tumours.

Therapeutic delivery. In addition to engineered therapeutic organisms, synthetic circuits and pathways can be used for the controlled delivery of drugs as well as for gene and metabolic therapy. In some cases, sophisticated kinetic control over drug release in the body may yield therapeutic advantages and reduce undesired side effects. Most hormones in the body are released in time-dependent pulses. Glucocorticoid secretion, for instance, has a circadian and ultradian20 pattern of release, with important transcriptional consequences for glucocorticoid-responsive cells (Stavreva et al., 2009). Faithfully mimicking these patterns in the administration of synthetic hormones to patients with glucocorticoid-responsive diseases, such as rheumatoid arthritis, may decrease known side effects and improve therapeutic response (Stavreva et al, 2009). Periodic synthesis and release of biologic drugs can be autonomously achieved with synthetic oscillator circuits (Elowitz and Leibler, 2000; Atkinson et al., 2003; Fung et al., 2005; Stricker et al., 2008; Tigges et al., 2009) or programmed time-delay circuits (Weber et al., 2007). In other cases, one may wish to place a limit on the amount of drug released by programming the synthetic system to self-destruct after a defined number of cell cycles or drug release pulses. Our group has recently developed two variants of a synthetic gene counter (Friedland et al., 2009) that could be adapted for such an application.

Gene therapy is beginning to make some promising advances in clinical areas in which traditional drug therapy is ineffective, such as in the treatment of many hereditary and metabolic diseases. Synthetic circuits offer a more controlled approach to gene therapy, such as the ability to dynamically silence, activate and tune the expression of desired genes. In one such example (Deans et al., 2007), a genetic switch was developed in mammalian cells that couples transcriptional repressor proteins and an RNAi module for tight, tunable and reversible control over the expression of desired genes (Box A2-4). This system would be particularly useful in gene-silencing applications, as it was shown to yield >99% repression of a target gene.

Additionally, the construction of non-native pathways offers a unique and versatile approach to gene therapy, such as for the treatment of metabolic disorders. Operating at the interface of synthetic biology and metabolic engineering, Liao and colleagues (Dean et al., 2009) recently introduced the glyoxylate shunt pathway21 into mammalian liver cells and mice to explore its effects on fatty acid metabolism and, more broadly, on whole-body metabolism. Remarkably, the researchers found that when transplanted into mammals, the shunt actually increased fatty acid oxidation, evidently by creating an alternative cycle. Furthermore, mice expressing the shunt showed resistance to diet-induced obesity when placed on a high-fat diet, with corresponding decreases in total fat mass, plasma triglycerides and cholesterol levels. This work offers a new synthetic biology model for studying metabolic networks and disorders, and for developing treatments for the increasing problem of obesity.

Finally, the discovery of drugs and effective treatments may not quickly — or ever — translate to the people who need them the most because drug production processes can be difficult and costly. As discussed below, synthetic biology is allowing rare and costly drugs to be manufactured more cost-effectively (Box A2-4).

Biofuels, pharmaceuticals and biomaterials

Recent excitement surrounding the production of biofuels, pharmaceuticals and biomaterials from engineered microorganisms is matched by the challenges that loom in bringing these technologies to production scale and quality. The most widely used biofuel is ethanol produced from corn or sugar cane (Fortman et al., 2008); however, the heavy agricultural burden combined with the suboptimal fuel properties of ethanol make this approach to biofuels problematic and limited. Microorganisms engineered with optimized biosynthetic pathways to efficiently convert biomass into biofuels are an alternative and promising source of renewable energy. These strategies will succeed only if their production costs can be made to compete with, or even outcompete, current fuel production costs. Similarly, there are many drugs for which expensive production processes preclude their capacity for a wider therapeutic reach. New synthetic biology tools would also greatly advance the microbial production of biomaterials and the development of novel materials.

Constructing biosynthetic pathways. When engineering for biofuels, drugs or biomaterials, two of the first design decisions are choosing which biosynthetic pathway or pathways to focus on and which host organism to use. Typically, these decisions begin with the search for organisms that are innately capable of achieving some desired biosynthetic activity or phenotype (Alper and Stephanopoulos, 2009). For biofuel production, for instance, certain microorganisms have evolved to be proficient in converting lignocellulosic material to ethanol, biobutanol and other biofuels. These native isolates possess unique catabolic activity, heightened tolerances for toxic materials and a host of enzymes designed to break down the lignocellulosic components. Unfortunately, these highly desired properties exist in pathways that are tightly regulated according to the host’s evolved needs and therefore may not be suitable in their native state for production scale. A long-standing challenge in metabolic and genetic engineering is determining whether to improve the isolate host’s production capacity or whether to transplant the desired genes or pathways into an industrial model host, such as E. coli or S. cerevisiae; these important considerations and trade-offs are reviewed elsewhere (Alper and Stephanopoulos, 2009).

The example of the microbial production of biobutanol, a higher energy density alternative to ethanol, provides a useful glimpse into these design trade-offs. Butanol is converted naturally from acetyl-CoA by Clostridium acetobutylicum (Jones and Woods, 1986). However, it is produced in low yields and as a mixture with acetone and ethanol, so substantial cellular engineering of a microorganism for which standard molecular biology techniques do not apply is needed to produce usable amounts of butanol (Tummala et al., 2003; Shao et al., 2007). Furthermore, importing the biosynthetic genes into an industrial microbial host can lead to metabolic imbalances (Inui et al., 2008). In an altogether different approach, Liao and colleagues (Atsumi et al., 2008) bypassed standard fermentation pathways and recognized that a broad set of the 2-keto acid intermediates of E. coli amino acid biosynthesis could be synthetically shunted to achieve high-yield production of butanol and other higher alcohols in two enzymatic steps.

Indeed, complementary to efforts in traditional metabolic and genetic engineering is the use of engineering principles for constructing functional, predictable and non-native biological pathways de novo to control and improve microbial production. In an exemplary illustration of this, Keasling and colleagues engineered the microbial production of precursors to the antimalarial drug artemisinin to industrial levels (Martin et al., 2003; Ro et al., 2006) (Box A2-4). There are now many such examples of the successful application of synthetic approaches to biosynthetic pathway construction — such approaches have been used in the microbial production of fatty-acid-derived fuels and chemicals (such as fatty esters, fatty alcohols and waxes) (Steen et al., 2010), methyl halide-derived fuels and chemicals (Bayer et al., 2009), polyketide synthases that make cholesterol-lowering drugs (Ma et al., 2009), and polyketides made from megaenzymes that are encoded by very large synthetic gene clusters (Kodumal et al., 2004).

Optimizing pathway flux. After biosynthetic pathways have been constructed, the expression levels of all of the components need to be orchestrated to optimize metabolic flux22 and achieve high product titres. A standard approach is to drive the expression of pathway components with strong and exogenously tunable promoters, such as the PLtet, PLlac, and PBAD promoters from the tet, lac and ara operons of E. coli, respectively. To this end, there are ongoing synthetic biology efforts to create and characterize more reusable, biological control elements based on promoters for predictably tuning expression levels (Alpert et al., 2005; Ellis et al., 2009). Further to this, synthetic biologists have devised a number of alternative methods for obtaining biological pathway balance, ranging from re-configuring network connectivity to fine-tuning individual components. A richer discussion of these topics, including the fine-tuning of parts, the application of model-guided approaches and the development of next-generation interoperable parts, is presented elsewhere (Lu et al., 2009). In Box A2-5 and Box A2-6 we detail several synthetic biology strategies that specifically pertain to the optimization of metabolic pathway flux. These strategies range from those driven by evolutionary techniques, to those driven by rational design and in silico models, to those that combine both approaches.

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BOX A2-5

Controlling Metabolic Flux: Evolutionary Strategies and Rational Design. In the production of artemisinin precursors, the native Escherichia coli isoprenoid pathway (the deoxyxylulose 5-phosphate (DXP)pathway) was eschewed in favour of a heterologous (more...)

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BOX A2-6

Controlling Metabolic Flux: Hybrid Approaches. In a hybrid rational–combinatorial approach, Dueber et al. (2009) suggested that metabolic flux could be controlled by spatially recruiting the enzymes of a desired biosynthetic pathway using synthetic (more...)

Programming novel functionality and materials. Beyond facilitating metabolic tasks, synthetic systems can infuse novel functionality into engineered organisms for production purposes or for building new materials. Early work in the field laid the groundwork for constructing basic circuits that could sense and process signals, perform logic operations and actuate biological responses (Voigt, 2006). Wiring these modules together to bring about reliable, higher-order functionality is one of the next major goals of synthetic biology (Lu et al., 2009), and an important application of this objective is the layering of ‘smart’ control mechanisms over metabolic engineering. For instance, circuits designed to sense the bioreactor environment and shift metabolic phases accordingly would further improve biofuel production. Alternatively, autonomous timing circuits could be used to shut down metabolic processes after a prescribed duration of time. Biological timers of this sort have been developed using genetic toggle switches that were deliberately rendered imbalanced through model-guided promoter engineering (Ellis et al., 2009). These genetic timers were used to program the time-dependent flocculation23 of yeast cells to facilitate the separation of cells from, for instance, the alcohol produced in industrial fermentation processes.

Synthetic control systems can also be used to extract and purify the synthesized product. This is particularly important in the production of recombinant proteins, bioplastics and other large biomaterials, which can accumulate inside cells, cause the formation of inclusion bodies and become toxic to cells if they are present at high titres. To export recombinant spider-silk monomers, Widmaier et al. (2009) searched for a secretion system that would enable efficient and indiscriminate secretion of proteins through both bacterial membranes. The Salmonella type III secretion system (T3SS) not only fulfils these criteria but also possesses a natural regulatory scheme that ties expression of the protein to be secreted to the secretion capacity of the cell; as a result, the desired protein is only expressed when sufficient secretion complexes have been built. To obtain superior secretion rates of recombinant silk protein, the researchers needed only to engineer a control circuit that hitches the heterologous silk-protein-producing genes to the innate genetic machinery for environmental sensing and secretion commitment.

Finally, there is an emerging branch of synthetic biology that seeks to program coordinated behaviour in populations of cells, which could lead to the fabrication of novel biomaterials for various applications. The engineering of synthetic multicellular systems is typically achieved with cell–cell communication and associated intracellular signal processing modules, as was elegantly used by Hasty and colleagues (Danino et al., 2010) to bring about synchronized oscillations in a population of bacterial cells. Weiss and colleagues (Basu et al., 2004, 2005) have similarly done pioneering work in building biomolecular signal-processing architectures that can filter communication signals originating from ‘sender’ cells. These systems, which can be programmed to form intricate multicellular patterns from a solid-phase cellular lawn, would aid the development of fabrication-free scaffolds for tissue engineering.

Future challenges and conclusions

The future of translational synthetic biology hinges on the development of reliable means for connecting smaller functional circuits to realize higher-order networks with predictable behaviours. In a previous article (Lu et al., 2009), we outlined four research efforts aimed at improving and accelerating the overall design cycle and allowing more seamless integration of biological circuitry (Box A2-7).

Box Icon

BOX A2-7

Recommendations for Improving the Synthetic Biology Design Cycle. Scaling up to larger and more complex biological systems while simultaneously minimizing interference among parts will require an expanded synthetic biology toolkit and, in particular, (more...)

Beyond the challenge of improving the design cycle, applied synthetic biology would benefit from once again summoning the original inspiration of bio-computing. The ability to program higher-level decision-making into synthetic networks would yield more robust and dynamic organisms, including ones that can accomplish many tasks simultaneously. Furthermore, as adaptive and predictive behaviours are naturally present in all organisms (including microbes) (Mitchell et al., 2009; Tagkopoulos et al., 2008), synthetic learning networks built from genetic and biological parts (Fernando et al., 2009; Fritz et al., 2007) would infuse engineered organisms with more sophisticated automation for biosensing and related applications.

Finally, the majority of synthetic biology is currently practiced in microbes. However, many of the most pressing problems, and in particular those of human health, are inherently problems with mammalian systems. Therefore, a more concerted effort towards advancing mammalian synthetic biology will be crucial for next-generation therapeutic solutions, including the engineering of synthetic gene networks for stem-cell generation and differentiation.

By addressing such challenges, we will be limited not by the technicalities of construction or the robustness of synthetic gene networks but only by the imagination of researchers and the number of societal problems and applications that synthetic biology can resolve.


We thank members of the Collins laboratory for helpful discussions and K. M. Flynn for help with artwork. We also thank the Howard Hughes Medical Institute and the US National Institutes of Health Director’s Pioneer Award Program for their financial support.

Competing Interests Statement

The authors declare competing financial interests: see Web version for details.


Entrez Gene: ccdB | lexA3 | traA

OMIM: agammaglobulinaemia

UniProtKB: CI | Cro | DspB | EnvZ | EthA | EthR

Further Information

James J. Collins’ homepage:

All links are active on the online PDF.


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Originally printed as Khalil, A. S., and J. J. Collins. 2010. Synthetic biology: Applications come of age. Nature Reviews Genetics 11:367–379. Reprinted with kind permission from Nature Publishing Group.


Memory elements – Devices used to store information about the current state of a system.


Pulse generators – Circuits or devices used to generate pulses. A biological pulse generator has been implemented in a multicellular bacterial system, in which receiver cells respond to a chemical signal with a transient burst of gene expression, the amplitude and duration of which depend on the distance from the sender cells.


Digital logic gates – A digital logic gate implements Boolean logic (such as AND, OR or NOT) on one or more logic inputs to produce a single logic output. Electronic logic gates are implemented using diodes and transistors and operate on input voltages or currents, whereas biological logic gates operate on cellular molecules (chemical or biological).


Filters – Algorithms or devices for removing or enhancing parts or frequency components from a signal.


Modularity – The capacity of a system or component to function independently of context.


Environment-responsive promoters – Promoters that directly transduce environmental signals (for example, heavy metal ions, hormones, chemicals or temperature) that are captured by their associated sensory transcription factors.


Aptamer – Oligonucleic acids that bind to a specific target molecule, such as a small molecule, protein or nucleic acid. Nucleic acid aptamers are typically developed through in vitro selection schemes but are also found naturally (for example, RNA aptamers in riboswitches).


Antisense RNAs – RNAs that bind segments of mRNA in trans to inhibit translation.


Riboregulators – Small regulatory RNAs that can activate or repress gene expression by binding segments of mRNA in trans. They are typically expressed in response to an environmental signalling event.


Two-component systems – Among the simplest types of signal transduction pathways. In bacteria, they consist of two domains: a membrane-bound histidine kinase (sensitive element) that senses a specific environmental stimulus, and a cognate response regulator (transducer domain) that triggers a cellular response.


Quorum sensing – A cell-to-cell communication mechanism in many species of bacteria, whereby cells measure their local density (by the accumulation of a signalling molecule) and subsequently coordinate gene expression.


Orthogonal environment – A cellular environment or host into which genetic material is transplanted to avoid undesired native host interference or regulation. Orthogonal hosts are often organisms with sufficient evolutionary distance from the native host.


DNA gyrase – A type II DNA topoisomerase that catalyses the ATP-dependent supercoiling of closed-circular dsDNA by strand breakage and rejoining reactions. Control of chromosomal topological transitions is essential for DNA replication and transcription in bacteria, making gyrase an effective target for antimicrobial agents (for example, the quinolone class of antibiotics).


Biofilms – Surface-associated communities of bacterial cells encapsulated in an extracellular polymeric substances (EPS) matrix. Biofilms are an antibiotic-resistant mode of microbial life found in natural and industrial settings.


Ultradian – Periods of cycles that are repeated throughout a 24-hour circadian day.


Glyoxylate shunt pathway – A two-enzyme metabolic pathway unique to bacteria and plants that is activated when sugars are not readily available. This pathway diverts the tricarboxylic acid (TCA) cycle so that fatty acids are not completely oxidized and are instead converted into carbon energy sources.


Metabolic flux – The rate of flow of metabolites through a metabolic pathway. The rate is regulated by the enzymes in the pathway.


Flocculation – A specific form of cell aggregation in yeast triggered by certain environmental conditions, such as the absence of sugars. For example, flocculation occurs once the sugar in a beer brew has been fermented into ethanol.


[These references] describe synthetic biology’s first devices — the genetic toggle switch and the repressilator — and establish the engineering-based methodology for constructing sophisticated, dynamic behaviours in biological systems from simple regulatory elements.


In [these references], engineered bacteriophages were used to deliver synthetic enzymes and perturb gene networks to combat antibiotic-resistant strains of bacteria.


[These references] provide a paradigm for the application of synthetic biology to the construction and optimization of biosynthetic pathways for cost-effective and high-yield microbial production. In these papers, the authors demonstrate industrial production of the direct precursor to the antimalarial drug artemisinin as part of a broader effort to address worldwide shortages of rare drugs.


Howard Hughes Medical Institute, Department of Biomedical Engineering, Center for BioDynamics and Center for Advanced Biotechnology, Boston University, Boston, Massachusetts 02215, USA.


Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, USA. Correspondence to J.J.C. e-mail: ude.ub@snillocj. doi:10.1038/nrg2775.

Copyright © 2011, National Academy of Sciences.
Bookshelf ID: NBK84446


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