Systems biology has produced a wealth of information from its detailed characterization of molecular components in living organisms. However, making sense of large data sets, and how to apply systems biology data to biological systems, poses challenges. One way in which data from systems biology can be applied is in the emerging field of synthetic biology.
The detailed quantitative characterizations of molecules and the behavior of their networks, gained from systems biology, provide a solid foundation for synthetic biology. Synthetic biology is a broad approach that uses tools such as rational design of genes, genetic systems and living systems for a specific purpose. For example, a synthetically designed oscillating biological clock and a bacterial ‘camera’ may seem like biological parlor tricks, but these pioneering experiments, along with others, have proved that biological systems can be engineered with a specific function and used to produce living ‘machines’ (Elowitz & Leibler, 2000; Gardner et al., 2000; Levskaya et al., 2005). A fundamental difference between synthetic biology and general genetic engineering is the collection, application and modeling of quantitative data. For example, because biological gene circuits have naturally widely varying kinetic characteristics, simply assembling a series of genes or genetic circuits to produce a desired function is unlikely to be successful. A general approach in synthetic biology is to design genetic circuits rationally, measure the steady-state and dynamic behavior of components quantitatively (e.g. mRNA synthesis and stability; protein synthesis and stability), model their behavior, and then assemble the characterized components into genetic circuits that exhibit predictable and reliable function (Andrianantoandro et al., 2006).
A key concept in synthetic biology is the application of long-proven approaches from engineering. For example, one of the easiest ways to produce a functional device in classical engineering is to use standardized parts (for example, a computer can be assembled from parts such as a processor, hard drive, memory and monitor). Standardization ensures that individual working components can be used together or exchanged. To begin standardization in biology, a group of Massachusetts Institute of Technology-based researchers have assembled Biobricks, a collection of mostly bacterial regulatory components and genes with some components from bacteriophage and yeast (http://www.biobricks.org). Biobrick components have been, and are being, used for annual student competitions to genetically engineer machines (International Genetically Engineered Machines, http://parts.mit.edu/igem07/index.php/Main_Page). In addition to standardization, decoupling and abstraction are central to designing complex synthetic biological systems from simple parts (Endy, 2005). Decoupling might be called ‘divide and conquer’. It allows researchers to break down a complex problem into a smaller problem that can be addressed experimentally. For example, to understand the complex process of photosynthetic electron transport, numerous studies have been carried out over many decades. Researchers first focused on questions that could be addressed experimentally, such as the effective wavelength of light, whereas studies today use detailed information about protein interactions in photosystems (Merchant & Sawaya, 2005). Collectively, such analyses could be called decoupling. A third concept that synthetic biology employs is abstraction. Organizing components of a system according to their complexity allows researchers to focus on one hierarchical level of complexity, independently of other levels (Endy, 2005). For example, to build an automobile, engineers have to design parts (pistons, tires), link these parts to form a device (engines, wheels), and assemble them together to produce an automobile. By focusing on making the best pistons, a better engine can be built and hence a better automobile. Likewise, plant synthetic engineers can optimize genes, genetic circuits and traits to produce plants with novel functions.
In addition to integrating engineering concepts, as already described, synthetic biology relies heavily on the mathematical modeling of components to provide insight into rational design of genetic circuits (Drubin, 2007). The modeling of selected aspects of genetic circuits through mathematical descriptions can be simple or complex, depending on the system components. Parameters that typically concern synthetic biologists can be grouped into two broad categories: those that define network topologies (how molecules control the concentration of other molecules), and those associated with kinetic interactions of molecules within devices (Kaznessis, 2007). Examples of gene circuit parameters that are often modeled include RNA polymerase binding, transcript elongation, repressor binding, random cellular noise, and polypeptide elongation (Elowitz & Leibler, 2000; Gardner et al., 2000; Li et al., 2007).
Regrettably, the information available for modeling gene expression is often limited, impeding the ability to design gene circuits rationally. A powerful means around rational design is offered by biological systems: the ability to evolve. By using directed evolution, synthetic biologists use the power of living systems to produce the best gene components based on selection of the desired behavior in vivo (Yokobayashi et al., 2002). In many cases, directed evolution has identified mutations that allow for subtle changes in the behavior of a gene or gene product that were not anticipated by modeling and rational design (Pattanaik et al., 2006; Zhou et al., 2006). For instance, researchers used directed evolution to identify mutations enhancing transcriptional activation of a basic helix−loop−helix transcription factor (Pattanaik et al., 2006). While most of the mutations were, as expected, in the acidic transactivation domain, one mutation was found in the N-terminal helix interaction domain. When tested, the synthetically evolved transcription factors showed significant enhancements in transcriptional activity and transactivation of an Arabidopsis promoter.
Synthetic biology accomplishments
In theory, any novel gene regulatory networks can be assembled from simple genetic components to produce behavior/activities designed by the biologist. However, one of the first productions of a functional gene regulatory network (a ‘toggle-switch’) from simple genetic components required quantitative mathematical modeling (Gardner et al., 2000). A genetic toggle-switch is a synthetic, bistable gene regulatory system. Multiple genetic designs are possible for a genetic toggle-switch; for example, Gardner et al. (2000) produced such a switch using two repressible promoters arranged in a mutually inhibitory network. A functional toggle-switch required the definition of multiple genetic parameters that were mathematically modeled, and the regulatory network was assembled based on this model. The genetic switch was used to produce or not produce green fluorescent protein (GFP). The expression showed a sharp sigmoidal curve indicating bistability or the ability to exist in two states (in this case with or without GFP production). A key difference between their work and traditional genetic engineering is that they manipulated the network architecture based on theoretical parameters (Gardner et al., 2000). Genetic toggle-switches may have many applications in plant biology, such as precise ‘on switches’ for plant pharmaceutical production, or regulation of biomass accumulation.
Timing is a key aspect of living systems that regulates processes such as circadian rhythms and periodicity. An oscillatory network showing that timing features can be designed synthetically was produced using a series of transcriptional repressors that controlled expression of GFP (Elowitz & Leibler, 2000). To produce the designed periodicity, Elowitz and Leibler developed a mathematical model for transcriptional/translational rates and decay rates of both mRNA and repressor proteins, and the GFP reporter. Understanding the mechanisms behind this artificial oscillatory network could reveal insight into the mechanisms of natural circadian clocks and the development of artificial clocks in living organisms. In plants, such an inducible timing mechanism could be engineered to coordinate flowering time in crop plants.
Remarkably, synthetic biologists have also been able to design systems where programmed multicellular pattern formation was produced (Basu et al., 2005). Natural pattern formation typically involves cell−cell communication that is then interpreted by an intracellular genetic network. Two sets of cells were engineered: ‘sender cells’ that produced a signaling molecule, acyl-homoserine lactone (AHL), and ‘receiver cells’ that produced a fluorescent protein in response to user-defined ranges of the AHL. By varying the spatial arrangement of sender cells and receiver cells, distinct ring-like patterns of GFP fluorescence were produced (Fig. 1). A key to accomplishing the ring pattern was the design of distinct types of receiver cells or ‘band-detect’ strains that had different genetic networks to detect and respond to a high, medium or low AHL concentration. Like previous work, the precise engineering of the genetic circuits used both theoretical and experimental analyses of various parameters (e.g. stability of proteins, strength of promoters). Mathematical models were able to define both individual cell behavior and spatiotemporal multicellular system behavior. In the receiver cells, three fluorescent proteins (GFP; red fluorescent protein, RFP; cyan fluorescent protein, CYP) were used as the output for the genetic network. From an undifferentiated lawn of receiver cells, a bullseye pattern was produced from CYP, RFP and GFP around a central sender colony (Basu et al., 2005). Synthetic pattern formation may provide quantitative understanding of natural processes, and opens doors to the possibility of engineering three-dimensional tissues (Basu et al., 2005). While pattern formation and its underlying genes have long been studied in plants, synthetic pattern formation could produce application-specific products. For example, by synthetically engineering the ability to control cell division planes, one could envision wood products that have dimensions for specific applications (e.g. a true block of wood rather than a block cut from an elongated tree).
Another application of synthetic biology is producing ‘biological machines’, or living organisms designed to perform a specific task. One example is a bacterial camera that was built to produce a chemical image corresponding to an applied light pattern (Levskaya et al., 2005). This biological camera uses a photosensitve phytochrome from cyanobacteria fused to the well characterized two-component signaling system, EnvZ−OmpR. The signaling system controls expression of LacZ that enzymatically produces a black compound in the presence of β-gal-like substrate. This work showed that a simple biological machine can be produced by interfacing different, naturally occurring molecular components.
A synthetic biosensor was produced in bacteria using computationally designed receptors (Looger et al., 2003). To delineate synthetic protein design, the Hellinga laboratory focused their efforts on the evolving zone, the region of ligand-receptor contact, and used periplasmic binding proteins that exhibit a hinge-binding mechanism. The hinge-binding mechanism allowed use of a fluorophore to screen computer-optimized synthetic receptors for functionality. Using these approaches, they demonstrated that a broad range of receptors can be designed: for example, receptors for an explosive, trinitrotoluene (TNT); a sugar, l-lactate; a neurotransmitter, serotonin; a nerve gas surrogate; and the metal zinc have all been designed (Dwyer et al., 2003; Looger et al., 2003; Allert et al., 2004). These receptors were shown to be highly specific, and often detected nanomolar concentrations of their ligands. To demonstrate that these receptors function in vivo, they used a histidine kinase-signaling system with synthetic feedback to reduce background (Looger et al., 2003). In response to nanomolar levels of a specific ligand, a conformational change is induced in the computer-designed receptor. The receptor−ligand complex then develops high affinity for the extracellular domain of transmembrane histidine kinase, activates the histidine kinase, and initiates signal transduction leading to the production of GFP. The system is extremely powerful because the receptors can be computationally designed to most small molecules. Moreover, because the receptors are the first part of the histidine kinase signal transduction system, they provide a modular function. By altering the receptor, bacterial biosensors can be produced to sense molecules such as explosives, chemical agents and environmental pollutants.
Developing phytodetectors with synthetic biology
Because plants naturally sense and respond to their environment, synthetic biology could be used to adapt these traits, which may lead to highly specific plant detectors or phytodetectors. A plant that could sense a substance of interest and provide a simple response could be useful in monitoring hazardous substances such as explosives, toxins/pollutants or pathogens. Plants would serve as ideal monitors because they are ubiquitous in most places and require little maintenance. The rational design of a phytodetector would require a sensing mechanism and a transmission mechanism that ideally activates a detectable response.
If the previously described computer-designed receptors could be made functional in plants, they would provide a means for simple and inexpensive detection of ligands of interest. In bacteria, the computer-designed receptors are localized in periplasmic space. While plants do not have a periplasmic space, the receptors themselves are small (e.g. the TNT receptor is 7 × 8 × 4 nm) and they could presumably diffuse freely in the apoplastic space of primary plant cell walls (Somerville et al., 2004). Moreover, the signal transduction system used in bacteria, histidine kinase, is conserved between bacteria and plants (Ferreira & Kieber, 2005; Fig. 2). Using synthetic biology approaches, it may be possible to forward engineer a system for eukaryotic synthetic signal transduction that links input from the computer-designed receptor to a response or read-out system.
One response system that might be useful for plant sentinels has already been developed. Our laboratory has described a synthetic ‘degreening circuit’ that allows chlorophyll levels to be placed under control of a specific input (Antunes et al., 2006). Chlorophyll represents one of the first ‘reporter genes’ appropriate for field-level measurement. Chlorophyll levels are typically under control of genetic and environmental input (Hortensteiner, 2006). To remove chlorophyll from environmental and genetic input, the synthetic degreening circuit was designed to stop synthesis and initiate breakdown simultaneously. Changes in chlorophyll fluorescence are detected in 2 h, with white plants resulting after 24–48 h (Fig. 3). The loss of chlorophyll, resulting in white plants, is a response that can be recognized by the general public. Because chlorophyll is the reporter molecule, the response can also be detected remotely with fluorescence or hyperspectral imaging (Shaw et al., 2007). When the ligand is removed, the plants regreen, allowing the plants to reset, an important aspect for any biologically based sensor. If a plant sensing system could be produced using the computer-designed receptors, some type of signal transduction, and a read-out such as provided by the synthetic degreening circuit, it could be extremely powerful, providing an inexpensive and widespread means to monitor for clean air, clean water and security.
Synthetic biology and biofuels
One area where synthetic biology could be particularly useful for plant biologists is biofuels. Synthetic biology could provide powerful tools for optimizing naturally occurring fuel production pathways or developing novel pathways in plants. For example, synthetic biology approaches could be used to optimize oil-producing pathways in oilseed crops or microalgae used for biodiesel production. One challenge to biodiesel production is the presence of polyunsaturated fatty acids in some vegetable oils (Chisti, 2007). Synthetic biology approaches could be useful in developing crops in which fatty acid production is limited to specific fatty acids optimal for biodiesel production. Like biodiesel production, development of cellulosic ethanol production could benefit from synthetic biology. For example, design of enzymes to efficiently break down cellulose into fermentable sugars or to remove lignin would significantly reduce the cost of processing. Cost could be further reduced if production of designer enzymes could be spatially and/or temporally controlled within the plant (Himmel et al., 2007). Perhaps a network could be designed that produces the enzymes for cellulose breakdown in the cell walls, and is controlled by an artificial clock that initiates enzyme production upon senescence.
Synthetic biology, the forward engineering of biological organisms for a specific purpose, is in its infancy. The enormous quantity of data from systems biology provides fertile ground to combine engineering concepts and mathematical modeling for useful purposes. Synthetic biology fundamentally describes the engineering of living systems and offers enormous potential in terms of novel materials, human health applications and energy resources. From a basic research perspective, designing synthetic systems will help us to better understand natural gene regulation mechanisms that underlie life. To date, most work on synthetic biology has been accomplished with microorganisms. However, we have described several ways in which synthetic biology may also find fruit in the green world of plants.