Every two hours in Matthew Bennett’s Rice University lab, cyan and yellow lights flashed in synchronization. Bennett and his team had engineered 12 components to generate the coordinated oscillations. This circuit wasn’t electronic, however; it was biological. Two populations of E. coli, each carrying a synthetic gene circuit, cycled in synchronous pulses every 14 hours.
Bennett’s work, published last year in Science,1 is a key application of modern synthetic biology: taking biological components and linking them together to form novel functional circuits. Instead of a program coded in Java and executed by a computer’s working memory, commands were written in DNA and carried out by the microbes’ cellular machinery. LEDs were replaced with fluorescent proteins, and molecular signaling cascades served as the system’s wires.

Stripped back to its most basic components, a synthetic or natural biological network consists of a gene that either...

The first synthetic networks were created in 2000, when researchers built an oscillator and others constructed a bistable switch in E. coli. In an oscillator circuit, three genes form a cascade, in which each gene triggers the inactivation of the next gene. In the case of the landmark oscillator constructed by Rockefeller University’s Stanislas Leibler, then at Princeton, and his graduate student Michael Elowitz, one of the three repressor genes was also linked to green fluorescent protein (GFP), resulting in visible pulses of light.2 A bistable switch, on the other hand, consists of just two genes that inactivate each other. When one gene is on, the other is off. Due to variation in the expression of the active gene, the inactive gene occasionally gets the chance to switch on and suppress the expression of the first gene. Like Leibler and Elowitz, MIT bioengineer James Collins and his grad student Tim Gardner, then at Boston University, linked one of the genes with a sequence encoding GFP, and they were able to see the cells switch between states.3 (See “Tinkering With Life,” The Scientist, October 2011.)

Since these early studies, engineers, computer scientists, mathematicians, and physicists have been applying their expertise to engineer synthetic gene networks. In addition to supporting the creation of novel functions, synthetic networks can also give insight into how naturally occurring ones work. As physicist Richard Feynman once wrote on his office blackboard, “What I cannot create, I do not understand.” Studying gene circuits in their natural context is complicated by the complex cellular environment in which they function; reconstructing and tuning gene interactions in vitro can provide a simplified model for how equivalent networks behave in nature. (See illustration below.)

“Naturally occurring gene oscillators, especially the circadian oscillator that regulates our daily rhythms, are hard to study,” says Bennett. “We can easily make changes and fine-tune synthetic gene circuits in ways that are difficult in natural systems. Though our synthetic circuits are inherently different from their natural counterparts, we can use them to study some of the basic principles of how genes dynamically regulate each other.”

Stripped back to its most basic com­ponents, a synthetic or natural biologi­cal network consists of a gene that either switches another gene on or turns it off.

Researchers at the J. Craig Venter Institute (JCVI) in San Diego have even gone so far as to create the smallest functional genome to date, a mycoplasma bacterium consisting of just 473 genes.4 This stripped-down cell can now provide insights into what each of the genes and their respective proteins are doing to keep the organism alive.

Building man-made circuits can also lead to something entirely new, Bennett adds. “I try to find ways to engineer a new synthetic circuit that can mimic the unexplained phenomenon, even if my solution is not the same as nature’s. Sometimes this leads to new insights into the natural circuit and sometimes not. Either way it’s exciting.”

Genetic networking

In the early 2000s, Uri Alon of the Weizmann Institute of Science in Israel and colleagues studied the connections between genes in E. coli, discovering common motifs, or patterns of gene connectivity. Importantly, the researchers found that these motifs occurred more often than could be expected if you took the same number of genes and randomly connected them, suggesting that biological networks have evolved these patterns.5 After early studies demonstrated researchers’ ability to create novel gene circuits, many synthetic biologists began making synthetic replicas of these natural motifs.

DECIPHERING THE NETWORK: A naturally occurring gene network consists of many interacting genes that can activate or repress each other (top). But embedded within a larger network, their function can be hard to study. Synthetic biology can simplify the study of such gene interactions by engineering analogous circuits separate from the larger network (bottom).
See full infographic: WEB

By isolating a subset of genes and inserting them into a new cell, synthetic biologists can assemble a motif that has little interaction with the molecular machinery of that cell; the genes are considered orthogonal. For example, viral promoters—sequences of DNA that drive gene expression—can be used to express GFP, a gene taken from jellyfish, inside mammalian cells. Modifications to promoters can allow them to be controlled by signaling molecules, not only allowing novel genes to be expressed, but giving synthetic biologists the ability to switch them on and off.

One of the simplest signaling motifs involves a gene that either activates or represses itself. Positive autoregulation is when a protein triggers its own expression. At first, due to the absence or very low concentration of protein, its expression is very low. After a while, however, an intermediate level of the protein builds up, speeding up the rise in expression levels. The overall effect of positive autoregulation is thus a delay before the gene reaches normal expression rates.6 Conversely, negative regulation, when a gene inhibits its own expression, allows the fast activation of a gene upon exposure to a signaling molecule, but then slows its own production once it reaches a critical level, allowing it to rapidly reach a steady state.7 (See illustration below.)

Another common motif includes the interaction of several genes forming feed-forward loops, in which one gene activates or represses the expression of another only under certain conditions. Synthetic implementation of one particular feed-forward loop has been shown to produce a pulse of gene activation—a large peak of gene expression followed by steady state expression.8

Autoregulation and feed-forward loops highlight how synthetic biology can create direct replicas of naturally occurring circuits to understand their function. However, synthetic biologists also have engineered a number of novel behaviors in cells: for example, different types of computation. One of the choices a synthetic biologist might make when constructing a synthetic circuit is whether to make it digital (i.e., on/off) or analog (varying levels of output). Researchers have constructed digital circuits implementing Boolean computations such as AND/OR and NOT/NOR logic with up to 10 regulators and 55 component parts in E. coli.9 Of course, many genes are not expressed in a digital manner; neither completely on nor completely off, they are, rather, expressed dynamically over a range of levels. As a result, synthetic biologists are increasingly taking inspiration from nature and designing computational circuits in analog, implementing functions such as addition, subtraction, and division.10

More than 15 years of constructing such biological gene networks has made waves in a wide variety of scientific fields. For example, Mary Dunlop of the University of Vermont is taking advantage of feedback circuits in the design of biofuel-producing bacteria. Her group has modified E. coli to express biofuels such as alcohols, diesels, or jet fuels that are exported from the cell by efflux pumps. Too much biofuel accumulating in a cell is toxic, and expression of too many efflux pumps places a strain on the cell. Either of these problems can prevent cell growth and the production of more biofuel. Through mathematical simulation, Dunlop has demonstrated that a negative-feedback sensor could control the balance by delaying pump expression until it is needed, when there is enough biofuel inside the cell to necessitate pumping it out.11

DYNAMIC GENE EXPRESSION: A number of motifs that appear in naturally occurring networks have been reconstructed in synthetic circuits. Positive autoregulation (left) occurs when a gene is activated by its own product; this results in delayed activation. (The black dotted line provides a comparison to gene activation with no autoregulation.) Conversely, negative autoregulation occurs when a gene represses its own expression (middle), allowing its rapid activation until it reaches a steady state, and then preventing overexpression. Finally, a combination of several genes can form a motif known as a feed-forward loop (right). Depending on the way the genes are connected, activating a single gene triggers the simultaneous activation and repression of another gene, causing a pulse in expression followed by a lower steady state.
See full infographic: WEB

Synthetic circuits are also showing potential as valuable tools for diagnosing and treating disease. In one study, researchers created synthetic circuits designed to detect combinations of microRNAs associated with a particular case of cervical cancer, and inserted the circuits into cancer and noncancer cell lines. Using a combination of AND and OR logic allowed the detection of specific combinations of different microRNA species only present in HeLa cells. If the right microRNA combination was detected, the synthetic circuit expressed a gene that caused the cells to die.12

While many technical challenges stand in the way of applying synthetic biology techniques in treating patients, a more near-term application may come in the form of paper-based diagnostics. In 2014, Collins and his colleagues at Harvard and Boston Universities developed synthetic gene circuits that function outside of cells and can be embedded in paper, which changes color through the expression of fluorescent proteins if certain markers are present in the sample. In this proof-of-concept study, the researchers showed that such paper-based diagnostics could be designed for a diverse range of applications, from glucose detection to the identification of different strains of Ebola virus, with outputs that can be seen by eye or a cheap microscope.13 This year, the team updated the test to detect 24 RNA sequences found in the Zika genome; when a target RNA is present, a series of interactions turns the paper purple.14 Paper-based diagnostics are easy to store through freeze-drying and to move to low-resource settings out of the lab, and researchers are now working to design such diagnostics for use in the field.

Working in tandem

BACTERIAL MOSAIC: Two populations of E. coli fluoresce yellow and cyan in unison as they activate or repress the other’s expression as well as their own. (See illustration below.)SCIENCE, 349:986-89, 2015, COURTESY OF MATTHEW BENNETTThe examples described so far have been of genetic circuits operating in isolation inside many identical cells or outside cells altogether. In nature, however, cells don’t exist in a vacuum; rather, small signaling molecules that can be easily transmitted across cell membranes allow cells to communicate with their neighbors.

In the case of the oscillator created in 2000, each individual cell in a population of bacteria would act in isolation, with one cell oscillating out of phase from its neighbors. Ten years later, University of California, San Diego’s Jeff Hasty and his colleagues used small signaling molecules that could pass out of one cell and into another to regulate the gene network within that cell. As a result, the oscillations of entire populations of bacteria were linked together and cycled in unison.15

Bennett’s group at Rice University took this idea one step further when they made use of two interacting populations of E. coli carrying different genetic circuits to coordinate the long-term, stable oscillations of fluorescent protein expression. One population of bacteria acted as an activator strain while the other population acted as a repressor strain. The activator strain produced a signaling molecule that activated even more of its own signal production, triggering activation of the repressor strain. When activated, the repressor strain produced another signaling molecule that repressed both itself and the activator strain. Each strain also produced an enzyme that degraded the signaling molecules in the system, preventing the buildup of leftover signal.

“We took the circuitry of a single strain oscillator and reconfigured it so that two strains must work together to achieve the oscillations,” Bennett explains. “It’s similar to taking a book and giving the even pages to one person and the odd pages to another. To either individual, their portion of the book is useless. But if the two can communicate and work together, the book will make sense.”

COORDINATED OSCILLATIONS: Two populations of bacteria interact via signaling molecules to coordinate expression of fluorescent proteins. When using positive and negative autoregulation (top), the oscillations are robust as the two populations grow. The negative feedback loop of the repressor strain and the positive feedback loop of the activator strain thus reinforce oscillations; when feedback is removed from the circuit (bottom), oscillations are less coordinated and prone to failure. “You can think of feedback loops as self-correction mechanisms,” says Bennett. “They are constantly assessing the current performance of the circuit and make changes if necessary.”
See full infographic: WEB

As each strain is activated, a fluorescent molecule is produced: cyan in the activator strain and yellow in the repressor strain. When mixed together, both populations are activated and repressed in unison, causing fluorescent oscillations over the entire cell population. When one strain is grown in isolation, no oscillations are observed.

Applying principles of basic gene motifs such as feedback loops with cell population biology can thus expand the repertoire of synthetic biologists looking to create novel genetic circuits. Likewise, implementing synthetic biological circuits in mixed cell populations that have coordinated behavior might illustrate ways in which complex synthetic tissues and organs could be engineered.

The decreasing cost of DNA synthesis and sequencing, the ability to share plasmids, the creation of databases describing genetic components, and the development of novel techniques to easily assemble and edit genomes have greatly accelerated progress in this area. As researchers engineer new genetic components, the relatively new field of synthetic biology could soon begin to bear actionable fruit, with applications that include compound synthesis, diagnostics, and even medical treatments. In addition, the design and study of synthetic systems will continue to give us a deeper understanding of the biology that exists around us.

“I take a great deal of inspiration from nature,” says Bennett. “Sometimes I see a circuit that is well-characterized and wonder if we can build it just as well as nature. Other times, I look at a phenomenon in nature that is unexplained. Then I get really excited.” 

Richard A. Muscat works at the London-based Cancer Research UK, bringing together multidisciplinary teams of researchers using engineering and physical sciences to find new ways to tackle cancer.


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  2. M.B. Elowitz et al., “Synthetic oscillatory network of transcriptional regulators,” Nature, 403:335-38, 2000.
  3. T.S. Gardner et al., “Construction of a genetic toggle switch in Escherichia coli,” Nature, 403:339-42, 2000.
  4. C.A. Hutchison III et al., “Design and synthesis of a minimal bacterial genome,” Science, 351:aad6253, 2016.
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  10. R. Daniel et al., “Synthetic analog computation in living cells,” Nature, 497:619-23, 2013.
  11. M.E. Harrison, M.J. Dunlop, “Synthetic feedback loop model for increasing microbial biofuel production using a biosensor,” Front Microbiol, 3:360, 2012.
  12. Z. Xie et al., “Multi-input RNAi-based logic circuit for identification of specific cancer cells,” Science, 333:1307-11, 2011.
  13. K. Pardee et al., “Paper-based synthetic gene networks,” Cell, 159:940-54, 2014.
  14. K. Pardee et al., “Rapid, low-cost detection of Zika virus using programmable biomolecular components,” Cell, 165:1-12, 2016.
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