Synthetic biology: advancing biological frontiers by building synthetic systems
© BioMed Central Ltd. 2012
Published: 20 February 2012
Advances in synthetic biology are contributing to diverse research areas, from basic biology to biomanufacturing and disease therapy. We discuss the theoretical foundation, applications, and potential of this emerging field.
Synthetic biology is an emerging field of interdisciplinary research that seeks to transform our ability to probe, manipulate, and interface with living systems by combining the knowledge and techniques of biology, chemistry, computer science, and engineering. Its main aim is to increase the ease and efficiency with which biological systems can be designed, constructed, and characterized. Core efforts in the field have focused on the development of tools to support this goal, including new approaches to biological design and fabrication. Although the first generation of synthetic systems demonstrated genetic circuits that encode dynamic behavior, cellular computational operations, and biological communication channels, more recent research has focused on implementing synthetic biological devices and systems in diverse applications, including disease therapy, environmental remediation, and biosynthesis of commodity chemicals. As the field matures, synthetic biology is advancing biological frontiers by expanding biomanufacturing capabilities, developing next-generation therapeutic approaches, and providing new insights into natural biological systems. Here, we review the theoretical foundations, diverse tool kits, and engineered systems that have emerged from synthetic biology and discuss current as well as potential future applications, which include in-depth studies of basic biology (such as understanding endogenous signaling pathways and feedback circuits) and new frontiers in health and medicine (such as identification of diseased cells and targeted therapeutics).
Conceptual frameworks for biological design
A central aim of synthetic biology is to increase the ease and efficiency with which biological systems can be designed, constructed, and characterized. Although the manipulation of biological organisms and molecular pathways long preceded the emergence of synthetic biology, the engineering of biological systems has been a largely ad hoc exercise. A main reason is that biology is inherently diverse, mutable, and context specific. Natural biological substrates, including genetic elements such as promoters and genes, do not always behave predictably when implemented in different combinations, and details such as how individual parts are physically connected can vary widely across different construction methods. As a result, the components designed and assembled for one biological system often cannot be predictably reused in another system. Synthetic biology seeks to address this challenge by implementing a more 'engineering-ready' conceptual framework that emphasizes the need to generate and report biological constructs in a manner that is conducive to their understanding and utilization by a broad community of researchers.
The application of engineering tools such as abstraction, decoupling, and standardization was proposed early in the emergence of synthetic biology to support the efficiency and scaling of the biological system design process . An abstraction hierarchy that dissects the engineering process into several design levels - DNA, parts, devices, and systems - provides synthetic biologists with a means to manage complexity and distribute tasks. The design process at each abstraction level can be performed relatively independently of the other levels, and detailed information critical to one abstraction level need only be considered by designers operating at that level. This division of labor reduces the amount of information that each designer must be expert in to successfully design a part, device, or system.
Decoupling refers to the strategy of partitioning a complicated problem into simpler tasks that can be tackled separately and assembled into a complete solution. The separation of design and fabrication processes is an important example of decoupling supported by advances in design tools and fabrication platforms. The increasing efficiency and decreasing cost of DNA synthesis allow synthetic biologists to design novel systems with the confidence that DNA components can be readily synthesized by commercial sources. Furthermore, advances in DNA sequencing and synthesis provide researchers with access to biological components encoding functions of interest using sequence information deposited in databases, eliminating the need for physical exchange of genetic materials.
Standardization takes several forms, including standardization of physical assembly, functional assembly, and characterization/measurements. Early physical assembly standards used biological parts flanked by standardized sequences, enabling the interchangeable combination and sequential assembly of parts conforming to the specified standard through a constant restriction-enzyme/ligation-mediated cloning strategy [2, 3]. Significantly less progress has been made in the field on functional assembly standards, which focus on identifying sequence interfaces between two types of parts (for example, ribosome binding site (RBS) and gene) that allow functional coupling and predictable activity, independent of the specific sequence of each part. Several early physical-assembly strategies encountered obstacles because the proposed standards impaired the functional assembly of parts by requiring the insertion of standard sequences between each part. In response, the field is shifting to assembly methods that do not require restriction-enzyme-mediated cloning [4, 5]. Finally, technical measurement and reporting standards have been proposed to eliminate discrepancies that result from disparate experimental methods and to provide more reliable and thorough characterization data . Standardized characterization data will support reliable sharing and reuse of parts, devices, and systems such that new designs can build on the foundation of previous work and move beyond the ad hoc model of system development.
Advances in fabrication methods for genetic systems
As synthetic biological systems become increasingly sophisticated, fabrication methods with larger capacities, greater precision, higher speed, and lower cost have become increasingly important. Outpacing the development of novel parts and devices, a number of groundbreaking fabrication techniques have been demonstrated in recent years, allowing researchers to focus on system design while outsourcing or performing system fabrication with higher efficiencies than was previously possible. Advances in multiplex oligonucleotide synthesis and assembly with microfluidic arrays have allowed cheaper de novo synthesis of gene-length fragments [7–9]. Furthermore, several techniques have been developed for the assembly of large DNA fragments, moving the field beyond laborious and time-consuming molecular cloning.
For example, transformation-associated recombination (TAR) in the yeast Saccharomyces cerevisiae has been used to construct yeast artificial chromosomes encoding genes and pathways isolated from several different organisms [4, 10]. Yeast artificial chromosomes can be further modified with bacterial artificial chromosome sequences to transfer the constructs to bacteria and subsequently to mammalian cells . Enzymatic in vitro assembly methods, such as one-step isothermal DNA assembly, can allow DNA molecules of several hundred kilobases to be assembled without restriction-enzyme-mediated digestion [5, 12]. A combination of in vitro and TAR-based assembly methods was used to assemble and clone the first bacterial genome from chemically synthesized oligonucleotides . However, large sets of parts encoding similar functions with distinct sequences are needed to avoid undesired recombination events between components that share similar sequences when assembling large genetic systems with recombination-based strategies.
In addition to DNA synthesis and assembly, methods have been developed for high-throughput genome modification. Multiplex automated genome engineering (MAGE) uses the bacteriophage λ-Red single-stranded-DNA-binding protein β to achieve allelic replacement in Escherichia coli. This process can greatly accelerate the optimization of biological systems and metabolic pathways, provided that the target genes are known and that an efficient screening method is in place to identify the desired variants within the diverse libraries generated . An alternative method termed trackable multiplex recombineering (TRMR) has been developed to support applications in which a priori knowledge of which target gene to modify is lacking, enabling rapid mapping of genes and quantification of population dynamics . A complementary technology called hierarchical conjugative assembly genome engineering (CAGE), which has been used to combine portions of a genome that have been modified by MAGE, was also recently described . Although genome modification has been reported in yeast , most high-throughput methods have been limited to demonstrations in E. coli and the extension of these technologies to mammalian cells remains an important challenge.
Constructing the toolbox: synthetic biological parts and devices
The synthetic biology toolbox: common components used in synthetic biological systems
Provide continuously ON gene expression at pre-determined levels
Provide conditional and, in certain cases, titratable gene expression in response to inducer signal
Control protein production levels by regulating mRNA stability or translation initiation in response to molecular input
Alternative splicing modulators 
Control protein production levels or protein activity by regulating alternative splicing of mRNA in response to molecular input
RNase substrate libraries 
Control protein levels through tunable hairpin elements that direct transcript cleavage
Modulate protein levels by shortening protein half-lives
Provide biosensing and modulate protein activity by conditionally splicing inactive protein fragments together into functional wholes
Regulate signaling and metabolic pathway flux by controlling the localization and stoichiometry of pathway components and intermediate products
As the diversity of gene regulatory processes in natural biological systems comes to light, efforts have also been directed to developing control devices that act through posttranscriptional and posttranslational mechanisms. In addition to parts such as degradation tags [24, 25] and split inteins [26, 27], non-coding regulatory RNAs have been used to construct a number of control devices . In one example, microbial gene expression was regulated by engineered RNA-responsive regulators (termed 'riboregulators') that modulate translation initiation by either obstructing or releasing the RBS of a target gene in response to the presence of a separately transcribed RNA sequence . Researchers have demonstrated the utility of riboregulators in a variety of applications, including protein localization studies, perturbation of stress response networks, and programmable cell killing . RNA-based devices responsive to small-molecule and protein inputs have also been demonstrated, exerting control over both transgenic and endogenous protein expression in bacteria, yeast, and mammalian cells [31–33], leading to applications ranging from bacteria-mediated detection and breakdown of pesticides  to disease-marker detection and cell-fate regulation in mammalian cells . The unique properties of RNA-based control devices - including ease of design and construction, small genetic footprint, high energy efficiency, fast regulatory time scales, and the ability to tailor input responsiveness and regulatory stringency - have made RNA a versatile substrate for designing programmable control systems.
In addition to controlling protein levels, synthetic biologists have developed tools to modulate the spatial organization of protein molecules inside cells, resulting in new strategies for regulating or rewiring cellular activities encoded in metabolic and signaling pathways . In one example, researchers constructed synthetic feedback loops within the yeast mating mitogen-activated protein (MAP) kinase pathway by recruiting modulator proteins to the pathway scaffold protein Ste5 through fusing leucine zipper domains to each component, and demonstrated circuits with pulse generator, accelerator, delay, and ultrasensitive switch functions [37, 38]. In another example, synthetic protein scaffolds that spatially recruit metabolic enzymes were implemented in E. coli, enabling the stoichiometric optimization of three mevalonate biosynthetic enzymes and achieving a 77-fold increase in product titer while avoiding cellular toxicity caused by the accumulation of a pathway intermediate . As an alternative to protein-based scaffolds, rationally designed RNA strands have recently been shown to assemble into higher-order structures, including sheets and nanotubes, inside bacterial cells . An RNA scaffold was applied to a two-enzyme hydrogen biosynthesis pathway and shown to increase hydrogen production by up to 48-fold compared with an unscaffolded system . These examples highlight the utility of spatial engineering in enhancing and modifying biological pathways.
Synthetic gene circuits
One of the hallmarks of synthetic biology has been the drive to engineer biological systems from the bottom up. Model-driven design of synthetic gene circuits has demonstrated the ability to build circuits of specified function [41–43]; differences between models and realized circuits have illuminated important and unique aspects of biological system behavior, such as the effects of degradation processes, cooperativity, and noise [44–46]. In addition to inspiring the design of more robustly operating systems, the insights gained through synthetic approaches have contributed to our understanding of natural biological systems .
Genetic circuits encoding dynamic behaviors
Encoding cellular logic and computing functions
Genetic circuits that perform computations and logical evaluations of cellular information provide the ability to assess intracellular states and environmental signals. They transmit this information into changes in cellular function, such as production of easily assayed readouts, activation of metabolic pathways, or initiation of cell-fate decisions. Towards this goal, genetic circuits and devices capable of performing logical evaluations have been built to detect small molecules (using tandem promoter systems  and RNA devices ), and small RNAs such as small interfering (si)RNAs (using tandem RNA interference (RNAi) target sites ) (Figure 1b). These various schemes have demonstrated the classic NOT, OR, NOR, and AND gates that are used to build larger logic evaluators and computations.
Methods for counting and maintaining memory of system states will enable a broader spectrum of intracellular computing. A genetic circuit that can count up to three exposure events to a small-molecule inducer was built in bacteria by nesting polymerase-promoter pairs controlled by riboregulators responsive to an inducible transactivator . Although this system captured brief induction pulses, system performance was highly dependent on pulse duration and frequency. The incorporation of genetic memory offers an alternative strategy to increase the robustness of counting events over longer time frames. A three-event counter circuit was demonstrated by using DNA recombinase-based cascades that record each event as a permanent change to the DNA, where the output of each recombinase event would 'prime' the next promoter-recombinase pair in the circuit . Synthetic networks of feedback loops have been built as memory circuits that lock a system in one state through sustained production of proteins following a transient signal that initiates the state. For example, toggle switches engineered to show bistability in bacteria  and mammalian cells  use architectures of mutually inhibitory feedback loops to achieve reversible memory of small-molecule pulses. As another example, a positive feedback loop built from a synthetic transcriptional activator cascade demonstrated heritable memory over many generations in yeast .
One recurrent limitation in adapting biological systems to perform computation through the rules of binary logic is the analog nature of the responses. In particular, gene expression leakage in the OFF state can contribute to improper input processing and high basal output, diminishing an evaluator's signal-to-noise ratio [48, 53, 55]. In addition, control of highly lethal proteins and proteins that mediate irreversible genetic changes requires stringent OFF states. To address this issue, researchers have layered transcriptional and posttranscriptional control elements within genetic circuits to provide strategies for achieving stringent regulation of transgenes in mammalian [56, 57] and bacterial cells . In one example, an inducible promoter was layered with repressible expression of a small hairpin (sh)RNA to achieve undetectable expression levels of the highly lethal diphtheria toxin in the OFF state, thus enabling induced cell death only in the ON state . Although tight OFF states are desirable for binary computing, biological computing necessarily exploits the analog and tunable nature of gene expression. Connecting logical circuit outputs to changes in cellular state requires the ability to both identify thresholds of expression at which cellular behavior diverges and tune the output to cross that threshold when triggered. Combining the computational ability of logical evaluators with improved strategies for leakage minimization and output tuning should enable more robust computing. These tools can expand our ability to detect and treat diseases by increasing diagnostic certainty and improving precision in gene expression, and can also be used to probe previously inaccessible information sets, such as the temporal and spatial profiles of particular developmental genes, which will inform our fundamental understanding of biology.
Communication circuits supporting more complex behaviors
Communication systems are required to coordinate events and tasks between different cells in a population. Synthetic communication circuits have been engineered in bacteria using various bacterial quorum-sensing systems. In these systems, a lactone signal is broadcast with increasing strength as cell density increases. At a given threshold level, lactone binds and activates a transcriptional regulator, upregulating the expression of a target gene. Broadcasting and receiving can be incorporated within a single cell population or distributed between 'sender' and 'receiver' cells. Incorporating both functions in a single population programmed to regulate a killer gene resulted in population control and demonstrated how population heterogeneity can be exploited to achieve a robust population response  (Figure 1c). Segregating tasks and localizing the sender population established a radial gradient of signaling molecules. Coupling the quorum-sensing circuitry to a band-pass circuit, which detects a specified range of input concentrations, achieved formation of various radial patterns in the receiver cells . In addition, connecting bacterial quorum systems to synthetic circuits has demonstrated dual-population consensus response and symbiosis in biofilms [60, 61] and synchronized genetic clocks  (Figure 1d). Finally, coupling a light-responsive device  to logic-processing circuitry and a communication module resulted in a biological edge detector  (Figure 1e). These examples demonstrate how synthetic circuits can distribute and coordinate computational tasks across a population of cells to achieve complex responses similar to what is observed in natural pattern formation and development.
Beyond bacterial systems, mammalian receiver cells have been engineered to respond to volatile chemical signals  and metabolic conditions  using engineered synthetic promoters. These receivers can potentially be paired with various processing circuits and sender cells to generate synthetic hormone-signaling systems and synthetic ecosystems. The eventual coupling of metabolic functions and cell-fate circuitry to synthetic hormone-signaling systems will enable spatial patterning of cell differentiation and timing of coordinated cellular responses, a requisite for complex tissue formation and function.
Moving towards real-world applications
Despite remarkable advances in the design and construction of increasingly sophisticated genetic circuits over the past decade, the transition of these systems to real-world applications has been constrained by the limited availability of devices that can connect synthetic circuitry with information in living systems. However, synthetic biologists are developing new ways to connect natural and engineered systems. For example, exploiting existing connections between synthetic circuitry and intracellular information, researchers have used the natural correlation between DNA damage and proteolysis of the ON state inhibitor λ cI in a genetic toggle switch to record transiently induced DNA damage through the formation of a biofilm . Taking another approach, researchers have constructed synthetic sensor devices from natural components, such as promoter-repressor pairs [67, 68], signaling pathway components , and small RNAs and their target sites [52, 57], to extract information from biological systems. As the range of sensor devices, processing circuitry, and output modules expands, synthetic biology is poised to address a broad scope of biological, medical, and biotechnological challenges.
Understanding biology by building
Expanding biomanufacturing capabilities
Biomanufacturing is one of the more compelling and immediate applications of biotechnology that promises sustainable synthesis strategies for alternative energy sources, commodity chemicals, and high-value specialty chemicals such as therapeutic drugs. A major challenge of biosynthetic pathway engineering lies in balancing the levels and activities of the many heterologous pathway enzymes to achieve optimized productivity and yield of desired compounds in the microbial host. Synthetic biology is transforming biosynthesis capabilities by providing new tools that support pathway construction and optimization. For example, researchers have recently combined TAR-based assembly strategies with sets of biosynthetic pathway parts (including enzyme coding regions, promoters, and terminators) to demonstrate one-step, whole-pathway assembly for a variety of natural-product pathways [75–77]. In another example, combinatorial libraries of tunable intergenic regions (TIGRs) harboring a number of RNA regulatory elements, including terminators, RNase cleavage sites, and stabilizing hairpins, were assembled in the non-coding regions between three heterologous enzymes in the mevalonate biosynthetic pathway expressed from a polycistronic transcript in E. coli. Researchers screened library variants for the TIGR sequences that resulted in optimal relative expression levels of each enzyme to increase mevalonate production; the best mevalonate producers decreased accumulation of a toxic intermediate and increased growth rate . Libraries of modular control elements, including promoters and RNA regulatory elements, that have broad ranges of predictable activities have also been generated [19, 79]. Recently, a library of RNase cleavage elements was used in yeast to titrate a key enzyme and thus flux through the endogenous ergosterol pathway, which competes with synthetic terpenoid pathways for the common precursor farnesyl pyrophosphate . Finally, several new tools supporting colocalization of heterologous enzymes, such as protein- and RNA-based scaffolds, are being used to develop pathway optimization strategies based on spatial engineering [39, 40] (Figure 2b).
Advancing next-generation therapeutics and diagnostics
By developing new strategies to interface with and manipulate natural biological systems, synthetic biology holds exciting promise in developing new therapeutic approaches. For instance, synthetic biologists are developing genetic circuits that link therapeutic activities to the detection of molecular disease signals to develop targeted therapeutics with increased efficacy and safety. In one example, a layered microRNA (miRNA)- and transcription-factor-based logic circuit was used to distinguish a cervical cancer cell line (HeLa) from other cell lines based on the detection of a unique miRNA profile . Positive identification of HeLa cells through this logic circuit was subsequently linked to either expression of a reporter protein, as a model diagnostic device, or expression of a protein that led to cell death as a model therapeutic device. In another example, to restrict cell death to diseased cells showing hyperactive signaling, researchers developed protein-responsive RNA devices that could detect increased signaling through the NF-κB and Wnt pathways and transmit this information into changes in the expression of a clinically relevant suicide gene that sensitizes cells to an apoptosis-inducing prodrug  (Figure 2c). These types of autonomous sense-and-control circuits offer potential applications in the long-term surveillance and intervention of chronic diseases, such as gout and diabetes [68, 81]. Circuits currently under development that link genetic targets to clinician-modulated external inputs will provide an unprecedented level of temporal and spatial control over complex therapeutic activities. For example, systems have been described that support light-modulated glucose homeostasis  and drug-modulated control over in vivo gene expression  and T-cell proliferation .
Where will synthetic biology take us?
The biological parts, genetic circuits, and fabrication techniques that have been developed and continue to be improved on offer exciting potential in diverse applications, from environmental engineering to regenerative medicine. Synthetic biological systems capable of detecting, reporting, and/or removing hazardous substances have been reported [85–88], and their implementation in robust host organisms suitable for environmental release will provide a new paradigm for environmental remediation. In the area of health and medicine, synthetic intercellular communication systems that regulate spatial patterning, timing of coordinated cellular responses, and tissue homeostasis have the potential to make significant contributions to tissue engineering. Furthermore, synthetic control circuitry may reduce the inherent tumorigenicity of stem cells  and improve the efficiency of induced pluripotent stem cell reprogramming . Novel genetic circuits capable of guiding the ex vivo construction of complex tissues may be built in the foreseeable future as researchers continue to unravel the systems biology behind cell-fate decisions [91, 92].
Efforts in synthetic biology so far have covered a wide range of topics spanning broad conceptual frameworks and specific circuit designs, and the future direction of synthetic biology is by no means limited to the few areas highlighted here. However, a unifying driving force in the field has been the desire to efficiently build biological systems, whether to improve our fundamental understanding of biology or to provide solutions for pressing global challenges. By developing conceptual frameworks and technical tools for the design, construction, and characterization of novel biological systems that can perform autonomous functions and interact with natural biological systems, synthetic biology is poised to transform our ability to probe, understand, and manipulate biology.
CDS is supported by funds from the National Institutes of Health, National Science Foundation, and Defense Advanced Research Projects Agency. YYC is supported by the Harvard University Society of Fellows.
- Endy D: Foundations for engineering biology. Nature. 2005, 438: 449-453. 10.1038/nature04342.View ArticlePubMedGoogle Scholar
- Rebatchouk D, Daraselia N, Narita JO: NOMAD: a versatile strategy for in vitro DNA manipulation applied to promoter analysis and vector design. Proc Natl Acad Sci USA. 1996, 93: 10891-10896. 10.1073/pnas.93.20.10891.PubMed CentralView ArticlePubMedGoogle Scholar
- Knight T: Idempotent Vector Design for Standard Assembly of Biobricks. 2003, MIT Synthetic Biology Working Group Technical Reports. Cambridge, MA: MITGoogle Scholar
- Kouprina N, Larionov V: Selective isolation of genomic loci from complex genomes by transformation-associated recombination cloning in the yeast Saccharomyces cerevisiae. Nat Protoc. 2008, 3: 371-377.View ArticlePubMedGoogle Scholar
- Gibson DG, Young L, Chuang RY, Venter JC, Hutchison CA, Smith HO: Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat Methods. 2009, 6: 343-345. 10.1038/nmeth.1318.View ArticlePubMedGoogle Scholar
- Canton B, Labno A, Endy D: Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol. 2008, 26: 787-793. 10.1038/nbt1413.View ArticlePubMedGoogle Scholar
- Tian J, Gong H, Sheng N, Zhou X, Gulari E, Gao X, Church G: Accurate multiplex gene synthesis from programmable DNA microchips. Nature. 2004, 432: 1050-1054. 10.1038/nature03151.View ArticlePubMedGoogle Scholar
- LeProust EM, Peck BJ, Spirin K, McCuen HB, Moore B, Namsaraev E, Caruthers MH: Synthesis of high-quality libraries of long (150 mer) oligonucleotides by a novel depurination controlled process. Nucleic Acids Res. 2010, 38: 2522-2540. 10.1093/nar/gkq163.PubMed CentralView ArticlePubMedGoogle Scholar
- Hughes RA, Miklos AE, Ellington AD: Gene synthesis: methods and applications. Methods Enzymol. 2011, 498: 277-309.View ArticlePubMedGoogle Scholar
- Gibson DG, Benders GA, Andrews-Pfannkoch C, Denisova EA, Baden-Tillson H, Zaveri J, Stockwell TB, Brownley A, Thomas DW, Algire MA, Merryman C, Young L, Noskov VN, Glass JI, Venter JC, Hutchison CA, Smith HO: Complete chemical synthesis, assembly, and cloning of a Mycoplasma genitalium genome. Science. 2008, 319: 1215-1220. 10.1126/science.1151721.View ArticlePubMedGoogle Scholar
- Kouprina N, Annab L, Graves J, Afshari C, Barrett JC, Resnick MA, Larionov V: Functional copies of a human gene can be directly isolated by transformation-associated recombination cloning with a small 3' end target sequence. Proc Natl Acad Sci USA. 1998, 95: 4469-4474. 10.1073/pnas.95.8.4469.PubMed CentralView ArticlePubMedGoogle Scholar
- Gibson DG, Smith HO, Hutchison CA, Venter JC, Merryman C: Chemical synthesis of the mouse mitochondrial genome. Nat Methods. 2010, 7: 901-903. 10.1038/nmeth.1515.View ArticlePubMedGoogle Scholar
- Gibson DG, Glass JI, Lartigue C, Noskov VN, Chuang RY, Algire MA, Benders GA, Montague MG, Ma L, Moodie MM, Merryman C, Vashee S, Krishnakumar R, Assad-Garcia N, Andrews-Pfannkoch C, Denisova EA, Young L, Qi ZQ, Segall-Shapiro TH, Calvey CH, Parmar PP, Hutchison CA, Smith HO, Venter JC: Creation of a bacterial cell controlled by a chemically synthesized genome. Science. 2010, 329: 52-56. 10.1126/science.1190719.View ArticlePubMedGoogle Scholar
- Wang HH, Isaacs FJ, Carr PA, Sun ZZ, Xu G, Forest CR, Church GM: Programming cells by multiplex genome engineering and accelerated evolution. Nature. 2009, 460: 894-898. 10.1038/nature08187.PubMed CentralView ArticlePubMedGoogle Scholar
- Warner JR, Reeder PJ, Karimpour-Fard A, Woodruff LB, Gill RT: Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides. Nat Biotechnol. 2010, 28: 856-862. 10.1038/nbt.1653.View ArticlePubMedGoogle Scholar
- Isaacs FJ, Carr PA, Wang HH, Lajoie MJ, Sterling B, Kraal L, Tolonen AC, Gianoulis TA, Goodman DB, Reppas NB, Emig CJ, Bang D, Hwang SJ, Jewett MC, Jacobson JM, Church GM: Precise manipulation of chromosomes in vivo enables genome-wide codon replacement. Science. 2011, 333: 348-353. 10.1126/science.1205822.View ArticlePubMedGoogle Scholar
- Dymond JS, Richardson SM, Coombes CE, Babatz T, Muller H, Annaluru N, Blake WJ, Schwerzmann JW, Dai J, Lindstrom DL, Boeke AC, Gottschling DE, Chandrasegaran S, Bader JS, Boeke JD: Synthetic chromosome arms function in yeast and generate phenotypic diversity by design. Nature. 2011, 477: 471-476. 10.1038/nature10403.PubMed CentralView ArticlePubMedGoogle Scholar
- Purnick PE, Weiss R: The second wave of synthetic biology: from modules to systems. Nat Rev Mol Cell Biol. 2009, 10: 410-422. 10.1038/nrm2698.View ArticlePubMedGoogle Scholar
- Alper H, Fischer C, Nevoigt E, Stephanopoulos G: Tuning genetic control through promoter engineering. Proc Natl Acad Sci USA. 2005, 102: 12678-12683. 10.1073/pnas.0504604102.PubMed CentralView ArticlePubMedGoogle Scholar
- Ellis T, Wang X, Collins JJ: Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat Biotechnol. 2009, 27: 465-471. 10.1038/nbt.1536.PubMed CentralView ArticlePubMedGoogle Scholar
- Karlsson M, Weber W, Fussenegger M: De novo design and construction of an inducible gene expression system in mammalian cells. Methods Enzymol. 2011, 497: 239-253.View ArticlePubMedGoogle Scholar
- Weber W, Rimann M, Spielmann M, Keller B, Daoud-El Baba M, Aubel D, Weber CC, Fussenegger M: Gas-inducible transgene expression in mammalian cells and mice. Nat Biotechnol. 2004, 22: 1440-1444. 10.1038/nbt1021.View ArticlePubMedGoogle Scholar
- Levskaya A, Chevalier AA, Tabor JJ, Simpson ZB, Lavery LA, Levy M, Davidson EA, Scouras A, Ellington AD, Marcotte EM, Voigt CA: Synthetic biology: engineering Escherichia coli to see light. Nature. 2005, 438: 441-442. 10.1038/nature04405.View ArticlePubMedGoogle Scholar
- Grilly C, Stricker J, Pang WL, Bennett MR, Hasty J: A synthetic gene network for tuning protein degradation in Saccharomyces cerevisiae. Mol Syst Biol. 2007, 3: 127-PubMed CentralView ArticlePubMedGoogle Scholar
- Wong WW, Tsai TY, Liao JC: Single-cell zeroth-order protein degradation enhances the robustness of synthetic oscillator. Mol Syst Biol. 2007, 3: 130-PubMed CentralView ArticlePubMedGoogle Scholar
- Wu H, Hu Z, Liu XQ: Protein trans-splicing by a split intein encoded in a split DnaE gene of Synechocystis sp. PCC6803. Proc Natl Acad Sci USA. 1998, 95: 9226-9231. 10.1073/pnas.95.16.9226.PubMed CentralView ArticlePubMedGoogle Scholar
- Muller J, Johnsson N: Split-ubiquitin and the split-protein sensors: chessman for the endgame. Chembiochem. 2008, 9: 2029-2038. 10.1002/cbic.200800190.View ArticlePubMedGoogle Scholar
- Win MN, Liang JC, Smolke CD: Frameworks for programming biological function through RNA parts and devices. Chem Biol. 2009, 16: 298-310. 10.1016/j.chembiol.2009.02.011.PubMed CentralView ArticlePubMedGoogle Scholar
- Isaacs FJ, Dwyer DJ, Ding C, Pervouchine DD, Cantor CR, Collins JJ: Engineered riboregulators enable post-transcriptional control of gene expression. Nat Biotechnol. 2004, 22: 841-847. 10.1038/nbt986.View ArticlePubMedGoogle Scholar
- Callura JM, Dwyer DJ, Isaacs FJ, Cantor CR, Collins JJ: Tracking, tuning, and terminating microbial physiology using synthetic riboregulators. Proc Natl Acad Sci USA. 2010, 107: 15898-15903. 10.1073/pnas.1009747107.PubMed CentralView ArticlePubMedGoogle Scholar
- Win MN, Smolke CD: A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. Proc Natl Acad Sci USA. 2007, 104: 14283-14288. 10.1073/pnas.0703961104.PubMed CentralView ArticlePubMedGoogle Scholar
- An CI, Trinh VB, Yokobayashi Y: Artificial control of gene expression in mammalian cells by modulating RNA interference through aptamer-small molecule interaction. RNA. 2006, 12: 710-716. 10.1261/rna.2299306.PubMed CentralView ArticlePubMedGoogle Scholar
- Beisel CL, Chen YY, Culler SJ, Hoff KG, Smolke CD: Design of small molecule-responsive microRNAs based on structural requirements for Drosha processing. Nucleic Acids Res. 2010, 39: 2981-2994.PubMed CentralView ArticlePubMedGoogle Scholar
- Sinha J, Reyes SJ, Gallivan JP: Reprogramming bacteria to seek and destroy an herbicide. Nat Chem Biol. 2010, 6: 464-470. 10.1038/nchembio.369.PubMed CentralView ArticlePubMedGoogle Scholar
- Culler SJ, Hoff KG, Smolke CD: Reprogramming cellular behavior with RNA controllers responsive to endogenous proteins. Science. 2010, 330: 1251-1255. 10.1126/science.1192128.PubMed CentralView ArticlePubMedGoogle Scholar
- Good MC, Zalatan JG, Lim WA: Scaffold proteins: hubs for controlling the flow of cellular information. Science. 2011, 332: 680-686. 10.1126/science.1198701.PubMed CentralView ArticlePubMedGoogle Scholar
- Bhattacharyya RP, Remenyi A, Good MC, Bashor CJ, Falick AM, Lim WA: The Ste5 scaffold allosterically modulates signaling output of the yeast mating pathway. Science. 2006, 311: 822-826. 10.1126/science.1120941.View ArticlePubMedGoogle Scholar
- Bashor CJ, Helman NC, Yan S, Lim WA: Using engineered scaffold interactions to reshape MAP kinase pathway signaling dynamics. Science. 2008, 319: 1539-1543. 10.1126/science.1151153.View ArticlePubMedGoogle Scholar
- Dueber JE, Wu GC, Malmirchegini GR, Moon TS, Petzold CJ, Ullal AV, Prather KL, Keasling JD: Synthetic protein scaffolds provide modular control over metabolic flux. Nat Biotechnol. 2009, 27: 753-759. 10.1038/nbt.1557.View ArticlePubMedGoogle Scholar
- Delebecque CJ, Lindner AB, Silver PA, Aldaye FA: Organization of intracellular reactions with rationally designed RNA assemblies. Science. 2011, 333: 470-474. 10.1126/science.1206938.View ArticlePubMedGoogle Scholar
- Hasty J, Dolnik M, Rottschafer V, Collins JJ: Synthetic gene network for entraining and amplifying cellular oscillations. Phys Rev Lett. 2002, 88: 148101-View ArticlePubMedGoogle Scholar
- Stricker J, Cookson S, Bennett MR, Mather WH, Tsimring LS, Hasty J: A fast, robust and tunable synthetic gene oscillator. Nature. 2008, 456: 516-519. 10.1038/nature07389.View ArticlePubMedGoogle Scholar
- Kramer BP, Fussenegger M: Hysteresis in a synthetic mammalian gene network. Proc Natl Acad Sci USA. 2005, 102: 9517-9522. 10.1073/pnas.0500345102.PubMed CentralView ArticlePubMedGoogle Scholar
- Elowitz MB, Leibler S: A synthetic oscillatory network of transcriptional regulators. Nature. 2000, 403: 335-338. 10.1038/35002125.View ArticlePubMedGoogle Scholar
- Hasty J, McMillen D, Collins JJ: Engineered gene circuits. Nature. 2002, 420: 224-230. 10.1038/nature01257.View ArticlePubMedGoogle Scholar
- Rosenfeld N, Elowitz MB, Alon U: Negative autoregulation speeds the response times of transcription networks. J Mol Biol. 2002, 323: 785-793. 10.1016/S0022-2836(02)00994-4.View ArticlePubMedGoogle Scholar
- Eldar A, Elowitz MB: Functional roles for noise in genetic circuits. Nature. 2010, 467: 167-173. 10.1038/nature09326.PubMed CentralView ArticlePubMedGoogle Scholar
- Gardner TS, Cantor CR, Collins JJ: Construction of a genetic toggle switch in Escherichia coli. Nature. 2000, 403: 339-342. 10.1038/35002131.View ArticlePubMedGoogle Scholar
- Tigges M, Marquez-Lago TT, Stelling J, Fussenegger M: A tunable synthetic mammalian oscillator. Nature. 2009, 457: 309-312. 10.1038/nature07616.View ArticlePubMedGoogle Scholar
- Tamsir A, Tabor JJ, Voigt CA: Robust multicellular computing using genetically encoded NOR gates and chemical 'wires'. Nature. 2011, 469: 212-215. 10.1038/nature09565.PubMed CentralView ArticlePubMedGoogle Scholar
- Win MN, Smolke CD: Higher-order cellular information processing with synthetic RNA devices. Science. 2008, 322: 456-460. 10.1126/science.1160311.PubMed CentralView ArticlePubMedGoogle Scholar
- Rinaudo K, Bleris L, Maddamsetti R, Subramanian S, Weiss R, Benenson Y: A universal RNAi-based logic evaluator that operates in mammalian cells. Nat Biotechnol. 2007, 25: 795-801. 10.1038/nbt1307.View ArticlePubMedGoogle Scholar
- Friedland AE, Lu TK, Wang X, Shi D, Church G, Collins JJ: Synthetic gene networks that count. Science. 2009, 324: 1199-1202. 10.1126/science.1172005.PubMed CentralView ArticlePubMedGoogle Scholar
- Kramer BP, Viretta AU, Daoud-El-Baba M, Aubel D, Weber W, Fussenegger M: An engineered epigenetic transgene switch in mammalian cells. Nat Biotechnol. 2004, 22: 867-870. 10.1038/nbt980.View ArticlePubMedGoogle Scholar
- Ajo-Franklin CM, Drubin DA, Eskin JA, Gee EP, Landgraf D, Phillips I, Silver PA: Rational design of memory in eukaryotic cells. Genes Dev. 2007, 21: 2271-2276. 10.1101/gad.1586107.PubMed CentralView ArticlePubMedGoogle Scholar
- Deans TL, Cantor CR, Collins JJ: A tunable genetic switch based on RNAi and repressor proteins for regulating gene expression in mammalian cells. Cell. 2007, 130: 363-372. 10.1016/j.cell.2007.05.045.View ArticlePubMedGoogle Scholar
- Xie Z, Wroblewska L, Prochazka L, Weiss R, Benenson Y: Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science. 2011, 333: 1307-1311. 10.1126/science.1205527.View ArticlePubMedGoogle Scholar
- You L, Cox RS, Weiss R, Arnold FH: Programmed population control by cell-cell communication and regulated killing. Nature. 2004, 428: 868-871. 10.1038/nature02491.View ArticlePubMedGoogle Scholar
- Basu S, Gerchman Y, Collins CH, Arnold FH, Weiss R: A synthetic multicellular system for programmed pattern formation. Nature. 2005, 434: 1130-1134. 10.1038/nature03461.View ArticlePubMedGoogle Scholar
- Brenner K, Arnold FH: Self-organization, layered structure, and aggregation enhance persistence of a synthetic biofilm consortium. PLoS One. 2011, 6: e16791-10.1371/journal.pone.0016791.PubMed CentralView ArticlePubMedGoogle Scholar
- Brenner K, Karig DK, Weiss R, Arnold FH: Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium. Proc Natl Acad Sci USA. 2007, 104: 17300-17304. 10.1073/pnas.0704256104.PubMed CentralView ArticlePubMedGoogle Scholar
- Danino T, Mondragon-Palomino O, Tsimring L, Hasty J: A synchronized quorum of genetic clocks. Nature. 2010, 463: 326-330. 10.1038/nature08753.PubMed CentralView ArticlePubMedGoogle Scholar
- Tabor JJ, Salis HM, Simpson ZB, Chevalier AA, Levskaya A, Marcotte EM, Voigt CA, Ellington AD: A synthetic genetic edge detection program. Cell. 2009, 137: 1272-1281. 10.1016/j.cell.2009.04.048.PubMed CentralView ArticlePubMedGoogle Scholar
- Weber W, Daoud-El Baba M, Fussenegger M: Synthetic ecosystems based on airborne inter- and intrakingdom communication. Proc Natl Acad Sci USA. 2007, 104: 10435-10440. 10.1073/pnas.0701382104.PubMed CentralView ArticlePubMedGoogle Scholar
- Weber W, Schuetz M, Denervaud N, Fussenegger M: A synthetic metabolite-based mammalian inter-cell signaling system. Mol Biosyst. 2009, 5: 757-763. 10.1039/b902070p.View ArticlePubMedGoogle Scholar
- Kobayashi H, Kaern M, Araki M, Chung K, Gardner TS, Cantor CR, Collins JJ: Programmable cells: interfacing natural and engineered gene networks. Proc Natl Acad Sci USA. 2004, 101: 8414-8419. 10.1073/pnas.0402940101.PubMed CentralView ArticlePubMedGoogle Scholar
- Burrill DR, Silver PA: Synthetic circuit identifies subpopulations with sustained memory of DNA damage. Genes Dev. 2011, 25: 434-439. 10.1101/gad.1994911.PubMed CentralView ArticlePubMedGoogle Scholar
- Kemmer C, Gitzinger M, Daoud-El Baba M, Djonov V, Stelling J, Fussenegger M: Self-sufficient control of urate homeostasis in mice by a synthetic circuit. Nat Biotechnol. 2010, 28: 355-360. 10.1038/nbt.1617.View ArticlePubMedGoogle Scholar
- Kemmer C, Fluri DA, Witschi U, Passeraub A, Gutzwiller A, Fussenegger M: A designer network coordinating bovine artificial insemination by ovulation-triggered release of implanted sperms. J Control Release. 2011, 150: 23-29. 10.1016/j.jconrel.2010.11.016.View ArticlePubMedGoogle Scholar
- Nandagopal N, Elowitz MB: Synthetic biology: integrated gene circuits. Science. 2011, 333: 1244-1248. 10.1126/science.1207084.PubMed CentralView ArticlePubMedGoogle Scholar
- Cagatay T, Turcotte M, Elowitz MB, Garcia-Ojalvo J, Suel GM: Architecture-dependent noise discriminates functionally analogous differentiation circuits. Cell. 2009, 139: 512-522. 10.1016/j.cell.2009.07.046.View ArticlePubMedGoogle Scholar
- Mody A, Weiner J, Ramanathan S: Modularity of MAP kinases allows deformation of their signalling pathways. Nat Cell Biol. 2009, 11: 484-491. 10.1038/ncb1856.PubMed CentralView ArticlePubMedGoogle Scholar
- O'Shaughnessy EC, Palani S, Collins JJ, Sarkar CA: Tunable signal processing in synthetic MAP kinase cascades. Cell. 2011, 144: 119-131. 10.1016/j.cell.2010.12.014.PubMed CentralView ArticlePubMedGoogle Scholar
- Sprinzak D, Lakhanpal A, Lebon L, Santat LA, Fontes ME, Anderson GA, Garcia-Ojalvo J, Elowitz MB: Cis-interactions between Notch and Delta generate mutually exclusive signalling states. Nature. 2010, 465: 86-90. 10.1038/nature08959.PubMed CentralView ArticlePubMedGoogle Scholar
- Naesby M, Nielsen SV, Nielsen CA, Green T, Tange TO, Simón E, Knechtle P, Hansson A, Schwab MS, Titiz O, Folly C, Archila RE, Maver M, van Sint Fiet S, Boussemghoune T, Janes M, Kumar AS, Sonkar SP, Mitra PP, Benjamin VA, Korrapati N, Suman I, Hansen EH, Thybo T, Goldsmith N, Sorensen AS: Yeast artificial chromosomes employed for random assembly of biosynthetic pathways and production of diverse compounds in Saccharomyces cerevisiae. Microb Cell Fact. 2009, 8: 45-10.1186/1475-2859-8-45.PubMed CentralView ArticlePubMedGoogle Scholar
- Shao Z, Zhao H: DNA assembler, an in vivo genetic method for rapid construction of biochemical pathways. Nucleic Acids Res. 2009, 37: e16-10.1093/nar/gkn724.PubMed CentralView ArticlePubMedGoogle Scholar
- Shao Z, Luo Y, Zhao H: Rapid characterization and engineering of natural product biosynthetic pathways via DNA assembler. Mol Biosyst. 2011, 7: 1056-1059. 10.1039/c0mb00338g.PubMed CentralView ArticlePubMedGoogle Scholar
- Pfleger BF, Pitera DJ, Smolke CD, Keasling JD: Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat Biotechnol. 2006, 24: 1027-1032. 10.1038/nbt1226.View ArticlePubMedGoogle Scholar
- Babiskin AH, Smolke CD: Synthetic RNA modules for fine-tuning gene expression levels in yeast by modulating RNase III activity. Nucleic Acids Res. 2011, 39: 8651-8664. 10.1093/nar/gkr445.PubMed CentralView ArticlePubMedGoogle Scholar
- Babiskin AH, Smolke CD: A synthetic library of RNA control modules for predictable tuning of gene expression in yeast. Mol Syst Biol. 2011, 7: 471-PubMed CentralView ArticlePubMedGoogle Scholar
- Han J, McLane B, Kim EH, Yoon JW, Jun HS: Remission of diabetes by insulin gene therapy using a hepatocyte-specific and glucose-responsive synthetic promoter. Mol Ther. 2011, 19: 470-478. 10.1038/mt.2010.255.PubMed CentralView ArticlePubMedGoogle Scholar
- Ye H, Daoud-El Baba M, Peng RW, Fussenegger M: A synthetic optogenetic transcription device enhances blood-glucose homeostasis in mice. Science. 2011, 332: 1565-1568. 10.1126/science.1203535.View ArticlePubMedGoogle Scholar
- Gitzinger M, Kemmer C, El-Baba MD, Weber W, Fussenegger M: Controlling transgene expression in subcutaneous implants using a skin lotion containing the apple metabolite phloretin. Proc Natl Acad Sci USA. 2009, 106: 10638-10643. 10.1073/pnas.0901501106.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen YY, Jensen MC, Smolke CD: Genetic control of mammalian T-cell proliferation with synthetic RNA regulatory systems. Proc Natl Acad Sci USA. 2010, 107: 8531-8536. 10.1073/pnas.1001721107.PubMed CentralView ArticlePubMedGoogle Scholar
- Hannink N, Rosser SJ, French CE, Basran A, Murray JA, Nicklin S, Bruce NC: Phytodetoxification of TNT by transgenic plants expressing a bacterial nitroreductase. Nat Biotechnol. 2001, 19: 1168-1172. 10.1038/nbt1201-1168.View ArticlePubMedGoogle Scholar
- Singh BK, Walker A: Microbial degradation of organophosphorus compounds. FEMS Microbiol Rev. 2006, 30: 428-471. 10.1111/j.1574-6976.2006.00018.x.View ArticlePubMedGoogle Scholar
- Gadd GM: Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology. 2010, 156: 609-643. 10.1099/mic.0.037143-0.View ArticlePubMedGoogle Scholar
- Antunes MS, Morey KJ, Smith JJ, Albrecht KD, Bowen TA, Zdunek JK, Troupe JF, Cuneo MJ, Webb CT, Hellinga HW, Medford JI: Programmable ligand detection system in plants through a synthetic signal transduction pathway. PLoS One. 2011, 6: e16292-10.1371/journal.pone.0016292.PubMed CentralView ArticlePubMedGoogle Scholar
- Schuldiner M, Itskovitz-Eldor J, Benvenisty N: Selective ablation of human embryonic stem cells expressing a "suicide" gene. Stem Cells. 2003, 21: 257-265. 10.1634/stemcells.21-3-257.View ArticlePubMedGoogle Scholar
- Maherali N, Ahfeldt T, Rigamonti A, Utikal J, Cowan C, Hochedlinger K: A high-efficiency system for the generation and study of human induced pluripotent stem cells. Cell Stem Cell. 2008, 3: 340-345. 10.1016/j.stem.2008.08.003.PubMed CentralView ArticlePubMedGoogle Scholar
- Kueh HY, Rothenberg EV: Regulatory gene network circuits underlying T cell development from multipotent progenitors. Wiley Interdiscip Rev Syst Biol Med. 2012, 4: 79-102. 10.1002/wsbm.162.PubMed CentralView ArticlePubMedGoogle Scholar
- Thomson M, Liu SJ, Zou LN, Smith Z, Meissner A, Ramanathan S: Pluripotency factors in embryonic stem cells regulate differentiation into germ layers. Cell. 2011, 145: 875-889. 10.1016/j.cell.2011.05.017.View ArticlePubMedGoogle Scholar
- Conrado RJ, Wu GC, Boock JT, Xu H, Chen SY, Lebar T, Turnsek J, Tomsic N, Avbelj M, Gaber R, Koprivnjak T, Mori J, Glavnik V, Vovk I, Bencina M, Hodnik V, Anderluh G, Dueber JE, Jerala R, Delisa MP: DNA-guided assembly of biosynthetic pathways promotes improved catalytic efficiency. Nucleic Acids Res. 2011, doi: 10.1093/nar/gkr888Google Scholar