engineering gene circuits: foundations and applications

20Engineering Gene

Circuits: Foundationsand Applications

Dennis TuJiwon LeeTaylan OzdereTae Jun LeeLingchong YouDuke University, Durham

20.1 Introduction................................................................. 20-363

20.2 Designing Gene Circuits ............................................. 20-364Modeling-Guided Rational Design .

Refining a Circuit through Evolutionary Techniques

20.3 Programming Robust Circuit Behavior..................... 20-366Cellular Noise . Basic Regulatory

Motifs to Resist Noise . Cell–Cell Communication

20.4 Applications ................................................................. 20-370Gene Therapy . Drug Development .

Biotechnology Applications

20.5 Outlook ........................................................................ 20-372

Abstract

Synthetic biology has emerged as a useful approach to decoding fundamental laws underlying biological

control. Recent efforts have produced many exciting systems and generated substantial insights. These

progresses highlight the potential of synthetic biology to impact diverse areas including biology,

computation, engineering, and medicine.

20.1 Introduction

Biological systems often function reliably in diverse environments despite internal or external perturba-

tions. This behavior is often characterized as ‘‘robustness.’’ Based on extensive studies over the last

several decades, much of this robustness can be attributed to the control of gene expression through

complex cellular networks [1–4]. These networks are known to consist of various regulatory modules,

including feedback [5] and feed-forward [6] regulation and cell–cell communication [7]. With these

basic regulatory modules and motifs, researchers are now constructing artificial networks that mimic

nature to gain fundamental biological insight and understanding [8]. In addition, other artificial net-

works that are engineered with novel functions will serve as building blocks for future practical appli-

cations. These efforts form the foundation of the recent emergence of synthetic biology [3,9,10]. These

artificial networks are interchangeably called ‘‘synthetic gene circuits’’ or ‘‘engineered gene circuits.’’

Recent accomplishments in synthetic biology include engineered switches [11–14], oscillators [15,16],

logic gates [17–19], metabolic control [20], reengineered translational machinery [21], population control

[22] and pattern formation [23] using natural or synthetic [24] cell–cell communication, reengineered

viral genome [25], and hierarchically complex circuits built upon smaller, well-characterized

Tuan Vo-Dinh / Nanotechnology in Biology and Medicine 2949_C020 Page Proof page 363 7.8.2006 8:49am

20-363

functional modules [26] In addition to programming cellular dynamics, efforts have also been made

toward the construction of in vitro gene circuits using cell extracts [27,28].

As evidenced in the term ‘‘circuit,’’ gene circuits are often compared to electrical circuits. For example,

one can characterize the state of a gene as ON or OFF depending on whether it is expressed or repressed.

Construction of electrical circuits benefits from a large collection of well-characterized parts and

modules, including resistors, capacitors, and inductors, which can be connected to generate a complex

circuit with a useful function. The rapid progress in high-throughput technologies is now generating

similar parts-lists, as evidenced by the establishment of numerous databases [29–32]. In particular,

efforts along this line have led to the creation of a database of biological parts (http:==parts.mit.edu=) —

the Registry of Standard Biological Parts—that is specifically established to facilitate gene-circuit design

and implementation. This registry contains details on basic parts (mostly for bacteria) such as ribosome

binding sites, transcriptional terminators, and reporter genes. As of this writing, the registry contains

details on over 1500 parts. The ultimate goal is to be able to order standardized parts ‘‘off the shelf ’’

from the registry and assemble them together to engineer a functional circuit. The current registry

documents mostly components from bacteria, especially E. coli. We anticipate that this or similar

registries will also be expanded to include yeast, eukaryotes, and other organisms. In addition, we

note that the recent development of high throughput, low-cost DNA synthesis technology [33] provides

a unique opportunity to explore parts not found in nature.

20.2 Designing Gene Circuits

20.2.1 Modeling-Guided Rational Design

The first term that comes to mind when considering the creation of a circuit is ‘‘engineering’’ or

‘‘programming.’’ With this mindset, many novel synthetic gene circuits have been created through

iterations of careful design, modeling, and implementation. A well-known synthetic gene circuit is the

genetic toggle switch, whose name is derived from its ability to program bistable behavior (Figure 20.1a)

[12]. The toggle switch circuit consists of two repressors, TetR and LacI, which mutually repress each

other (Figure 20.1a). The circuit can be flipped between ‘‘high’’ and ‘‘low’’ states using chemical inducers

isopropyl-b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc). For example, when aTc is

added to the system to inhibit tetR, lacI will be highly expressed and the reporter will be OFF.

Conversely, when IPTG is added to inhibit lacI, tetR and the reporter will be highly expressed.

(a) (b)IPTG

Lacl

P trc-2

aTc

TetR Reporter

Lacl

Lacl

TetR

PLtetO-1Ptet

Plac

TetRλcl

λP

λcl

FIGURE 20.1 (a) The genetic toggle switch gene circuit layout. TetR represses PLtetO-1and is inhibited by inducer

aTc; LacI represses Ptrc-2 and is inhibited by inducer IPTG. (b) Repressilator gene circuit layout. TetR represses

expression of lcI which in turn represses expression of LacI. Finally, LacI represses TetR expression, completing the

repressilator cycle.


20-364 Nanotechnology in Biology and Medicine

Mathematical modeling suggested that the circuit could demonstrate bistability for appropriate

circuit parameter values. That is, for particular parameter sets, the system can reach either the stable

high state or the stable low state, depending on the prior history of its dynamics. For example, if a

sufficiently strong pulse of IPTG is applied to the circuit (previously at the low state), the circuit will be

turned ON. Importantly, it will stay ON even if the IPTG is reduced to a low level or is even completely

absent. Modeling also predicted the conditions where bistability would be favored, which included

higher cooperativity in transcription repression and faster synthesis rates of the repressors. Guided by

these modeling predictions, the authors used commonly available, well-characterized elements (e.g., tet

and lac repressors) to successfully implement a library of functional bistable toggles.

This relatively simple circuit demonstrates a dynamic property with broad implications in biology

[34]. For example, bistable behavior can ensure that a gene is fully ON or OFF without intermediary

states, which can serve as the basis for cell fate decision [35] and cell differentiation [36]. Because an

induction with sufficient strength and duration is required for switching, a bistable circuit may act as a

noise filter, which is discussed in detail later.

Another well-known circuit is the repressilator [15], which consists of three repressors sequentially

repressing each other. As shown in Figure 20.1b, the first repressor protein (TetR) inhibits the second

repressor gene whose protein product (lcI) in turn inhibits the third repressor gene (lacI). Finally, the

third repressor protein (LacI) inhibits the first repressor gene, completing a negative feedback loop with

a long cascade.

Similar to the design process of the toggle switch [12], mathematical modeling was used to analyze the

circuit design and guide its implementation. Modeling suggested that the circuit could generate

sustained oscillations with concentrations of the three repressors within biologically feasible ranges.

Through detailed analysis, it was found that oscillations would be favored by strong promoters, tight

transcriptional repression (low ‘‘leakiness’’), cooperative repression characteristics, and comparable

protein and mRNA decay rates. With this knowledge, the stability of each of the repressor proteins

was modulated by using degradation tags [37]. This careful engineering eventually resulted in a circuit

that generated oscillatory behavior in individual cells.

These pioneering studies illustrate that we have acquired enough biological understanding to begin

programming cellular dynamics in a rational way. Nevertheless, there are many challenges ahead. For

example, compared with natural biological oscillators, such as circadian oscillators [38–40], the repres-

silator is unreliable in that oscillations only occur in a subpopulation of cells and these oscillations are

often noisy.

20.2.2 Refining a Circuit through Evolutionary Techniques

Despite careful design and computer simulation, building a gene circuit in vivo may still be challenging

due to a lack of detailed understanding of the cellular components and how they interact with one

another. The result of this knowledge gap is a circuit that may require fine tuning of circuit components

or even rearranging the entire circuit topology. This process may benefit from evolutionary techniques.

For example, in silico evolution is a technique using computer simulated evolution to discover the

best parameter sets. This technique entails simulating a library of networks with varying parameter

settings and topologies in a computer. Then, these networks are screened for the best performing ones

according to a predefined objective function [41] For instance, one could select circuits that generate

oscillations with a certain period. Winners from the first round of screening are ‘‘mutated’’ further by

varying their parameters or topologies and computationally screened again for variants that display

improved performance. Several rounds of in silico mutagenesis and screening often leads to circuits with

topologies and parameter settings that ensure greatly improved performance according to a predefined

objective. The optimal parameters discovered through this computer simulated evolution are useful in

guiding the circuit designer to select appropriate promoters and genes. An advantage of this strategy is

that it can potentially identify alternative designs that are not obvious to a ‘‘rational designer.’’ These

could include circuits with the same architecture but with nonobvious parameter settings, or circuits


Engineering Gene Circuits: Foundations and Applications 20-365

with completely different network architecture and different parameter settings. However, the extent to

which this strategy can speed up circuit design and implementation is unclear. In fact, synthetic gene

circuits are yet to be experimentally implemented based on a design resulting from this approach.

Laboratory evolution can be realized by combining random mutagenesis and subsequent high-

throughput screening or selection for a desired function [42,43]. This ‘‘directed evolution’’ technique

has been used successfully for years to optimize enzymes by improving or altering their efficiency and

specificity [44]. In gene circuit design, directed evolution has been used to optimize metabolic pathways

to produce useful metabolites [45]. This optimization is accomplished by generating components such

as proteins and DNA regulatory elements with diverse kinetic properties that may not be naturally

available. In other words, it allows researchers to take advantage of a broader parameter space when

designing a gene circuit. In recent work, an evolved transcriptional activator (LuxR) [46] was used in a

synthetic pattern formation circuit [23] to optimize the final circuit function.

Another application of directed evolution is to optimize a partially functional or nonfunctional circuit

to be fully functional [47]. For example, this technique was used to evolve an initially nonfunctional

genetic inverter into a fully functional one [19]. Briefly, several rounds of mutagenesis by error-prone

PCR and subsequent high-throughput screening were able to achieve proper matching of the different

components that were assembled to make up the inverter, allowing for rapid optimization of the circuit

[19]. It is interesting to note that the functional circuit, created from directed evolution, ended up with

parameters tuned completely different from what was attempted by a rational approach [17]. This aspect

again highlights the advantage of evolution methods to explore a parameter space not readily accessible

by rational design.

More recently, this technique was further revised by combining mutagenesis with selection instead of

screening [48]. That is, the ability of a circuit to function properly is tied to the survival or death of the

host cell. This selection strategy, if properly established, can drastically improve the throughput of circuit

evolution. In this proof-of-concept work, Yokobayashi and Arnold were able to isolate a functional

inverter from a pool with more than 3 million fold background of nonfunctional circuits. Although this

work demonstrated a technique that was limited to optimization of a circuit with two output states (e.g.,

logic gates and toggle switches), more complex circuits may be broken down into such submodules for

individual optimization.

Random mutagenesis is unlikely to alter the connectivity of a circuit; rather, it largely results in

changes in one or more circuit parameters. Therefore, for directed evolution techniques to succeed,

they require a starting circuit with a topology that can in principle display the desired function with

proper parameter settings. Complementary to random mutagenesis, a combinatorial method will

enable the generation of a large number of circuits with diverse topologies from a small number of

elements. As a proof-of-concept, Guet and colleagues created 125 logic circuits using only three genes

and five promoters [18]. The resulting circuits displayed diverse phenotypes including NOR, NOT IF,

and NAND. Potentially, the same strategy can be applied to a larger number of elements and an even

greater diversity of circuit connectivity can be created. When coupled with efficient screening or

selection methods, this strategy can allow evolution of a circuit in both the topological space and the

parametric space.

20.3 Programming Robust Circuit Behavior

20.3.1 Cellular Noise

Another fundamental challenge in constructing gene circuits with reliable function is to deal with

cellular ‘‘noise’’ or stochastic fluctuations in cellular processes. Such noise can be due to a number of

factors, including small numbers of interacting molecules, heterogeneity of the cellular environment,

perturbations from the extracellular environment, and extended gene cascades [49–58].

Noise may benefit cells by providing phenotypic variations through which they adapt to changing

environments [59–62]. In most cases, however, noise is detrimental because it poses a significant



challenge for creating circuits with robust function. For example, the repressilator displays noisy

behavior. Only 40% of the cells in the population exhibited oscillatory behavior and these oscillations

are often out of phase with each other, even between sibling cells [15]. To a lesser degree, cellular noise

also cause variations in the oscillatory dynamics in a more sophisticated oscillator (metabolator)

recently implemented [16]. In contrast, natural oscillators function much more reliably. For example,

single-cell measurements in cyanobacteria revealed highly robust oscillations for days [63]. An intri-

guing question is how natural systems are able to function with such robustness despite cellular noise.

20.3.2 Basic Regulatory Motifs to Resist Noise

Extensive theoretical and experimental studies have been conducted to elucidate the underlying mech-

anisms by which biological systems achieve robustness [50,64–66]. One such mechanism is negative

feedback, where the output from a system reduces its own output. In a biological context, this occurs

when a protein limits its own accumulation by repressing its own synthesis [14,67] or by facilitating its

own turnover [68,69].

A well-studied example is bacterial chemotaxis, a phenomenon where bacteria move toward an

attractant by biasing their swimming motion [2]. This response is controlled by receptors that detect

the attractant gradient. When the bacterium reaches a steady concentration of attractant, adaptation

occurs. This ‘‘zeroes’’ the detector so that it can react to new gradients. This adaptation was found to be

resistant to internal and external perturbations [1,2]. In-depth theoretical analysis indicates that the

robust behavior of the circuit can be attributed to an integral negative feedback [70].

Over three decades ago, Savageau pioneered systems-level analysis of various regulatory motifs [71],

and found that negative feedback can confer stability in system output and improve system response

speed [67]. These predictions were recently verified experimentally. Besckei and Serrano constructed a

simple synthetic circuit where transcription repressor TetR represses transcription of its own gene

(Figure 20.2). Comparison of this autoregulated gene circuit with an unregulated counterpart by

negative feedback indicated that the autoregulation indeed reduced noise, as measured in the distribu-

tion of gene expression across the population (Figure 20.2) [5]. That is, the autoregulated circuit

(a) (b)Unregulated Negative feedbackregulated

Gene X Gene X

Normalized concentration Normalized concentration

Cel

l cou

nt

Cel

l cou

nt

0

20

40

60

80

100

0

100

200

300

400

0.9 1 1.1 1.2 0.9 1 1.1 1.2 1.3

FIGURE 20.2 (a) An unregulated circuit as compared with (b) an autoregulated circuit by negative feedback

results in a narrower distribution of gene expression as compared with an unregulated circuit with no feedback.



resulted in gene expression with a much narrower distribution compared to the unregulated circuit. To

further illustrate the importance of negative feedback, the autoregulatory loop was gradually removed by

adding an inducer. As the inducer concentration increased, the negative feedback circuit behavior

transitioned to that of the unregulated circuit, resulting in a wide distribution of gene expression [5].

Part of the mechanism behind this behavior may be an increase in the noise frequency. This frequency

shift is thought to enable the cell to more effectively filter the noise [72]. Using the same circuit, Alon

and colleagues demonstrated that negative feedback also significantly improved system response speed

[73].

An alternative approach to resisting noise and enhancing stability is increasing the rise-time in gene

expression. Rise-time is the time it takes for a gene circuit to reach a steady-state level of protein

expression [73]. An increased rise-time requires a longer-lasting input to fully induce a gene. This

behavior can be implemented through a coherent feed-forward loop, where transcription factor A

regulates B, and both jointly control the expression of C [6]. The latter stage of the feed-forward loop

causes an increase in rise-time, allowing the cell to filter noise from the input by withstanding transient

perturbations.

Another regulatory feature used to increase cellular robustness is positive feedback. Positive feedback

occurs when the output of a network causes an increase in the same output. It can be realized by

positive autoregulation [13,14], mutual activation [36,74,75] or mutual inhibition [12] (as detailed in

Figure 20.1a). Positive feedback loops often amplify noise [13,64]. On the other hand, when these

modules display bistable switching behavior with appropriate parameter sets, they may serve as a

noise-resistant device. This is because a sufficiently strong, long-lasting input signal is required to

change the state of the switch [76]. Using this feature as a noise filter to stabilize oscillations was

proposed on the basis of computational analysis [77]. Subsequent experimental efforts successfully

generated population-level oscillations, although these were damped oscillations [78]. In another

example of noise-resistant behavior, cellular ‘‘memory’’ is controlled by a positive feedback loop.

This memory allows the cell to maintain a state that depends on the initial input. Once the memory

is established, the state of the cell does not change with varying input as long as the memory is

sustained by the positive feedback loop [79].

20.3.3 Cell–Cell Communication

In recent years, scientists have discovered that bacteria do not exist merely as individuals, but can

coordinate their behavior as a group through quorum sensing [7,80,81]. Quorum sensing is a technique

by which bacteria use cell–cell communication to sense changes in cell population density. This ability

has been extensively studied for its role in regulating diverse physiological functions such as biolumin-

escence [80], virulence [82,83], biofilm formation [84], and programmed death [85–87].

In quorum sensing, signaling molecules are expressed by the cells and can freely diffuse through the

cell membrane. At a low population density, these molecules diffuse out of the cells into the extracellular

space with little accumulation of the signal within the cells. Thus, communication is weak and the cells

act independent of one another. As the population increases, the signaling molecule concentration

increases both inside and outside the cells. Upon reaching a threshold concentration, the signal will lead

to coordinated expression of genes responsible for various functions [7,81].

Intuitively, the large number of signaling molecules outside the cells can act as a buffer and temper the

fluctuations of the signaling molecule in each cell. That is, the fluctuation in the concentration of the

signaling molecule is likely much smaller compared with molecules of the same concentration, but

confined in individual cells. Consider a molecule with a concentration of 10 nM. If it is confined in the

cell, there will be about 10 molecules=cell (assuming a cell volume of 10�15 L). The fluctuation in its

concentration will be about 101=2, or a relative fluctuation of �0.3. However, for a freely diffusible

signaling molecule with the same concentration in a 1 mL culture, the molecular number is 1013, and the

relative fluctuation is around 10�6.5, which is considerably less noisy. Therefore, if a gene is under

control of such a signaling molecule, there will likely be less noise in its expression across the population.



Confirming this point, our simulations suggest that cell–cell communication can indeed reduce noise in

gene expression across a population of cells. The distribution of gene expression controlled by quorum

sensing is much tighter than the distribution found in the system without cell–cell communication

(unpublished data). Our simulations also suggest that this effect depends only on the cell density and

diffusion rate and not on the number of cells. With this evidence, quorum sensing may be a side effect of

batch culture cells detecting their environmental conditions through diffusion [88].

It has been proposed that cell–cell communication may be used to facilitate more robust oscillations.

Recent computer simulations have demonstrated that the repressilator oscillations can be synchronized

across an entire population of cells through cell–cell communication [89,90]. The same strategy was also

proposed to synchronize relaxation-type oscillators [91,92]. Although this synchronization strategy

remains to be demonstrated experimentally, there is evidence that suggests that interlocked oscillators

provide more robust behavior in nature [93].

In addition to synchronizing oscillators, cell–cell communication can serve as an effective mechanism

to achieve integrated and coordinated behavior among a population of cells. In a synthetic ‘‘population

control circuit,’’ quorum sensing was coupled with a negative-feedback loop to regulate the cell density

by controlling the death rate [22]. As the size of the population increases, the signaling molecule (AHL)

builds up in the environment and inside the cells. When a particular population density is reached, the

production of a killer gene (ccdB) is activated and the death rate increases. Gradually, the population

density steady state is achieved when the growth rate equals the death rate (Figure 20.3). Simple

mathematical models predict that the circuit can generate stable steady states [22] or sustained

oscillations [94].

0 15 30 45 60

0 15 30 45 60

Cel

ls/m

L

109

107

105

104

106

108

H

[ccd

B]

AHL

(a) (b)

(c)R I

LuxILuxR

ccdB

ccdB

R*

PluxI

FIGURE 20.3 A population control circuit that detects the size of a population and activates a cell killing gene

when the cell density is too high (a) Acyl-homoserine lactone (AHL) is produced by LuxI (I) and freely diffuses

through the cell. At high enough concentration, AHL binds to and activates LuxR (R). Activated LuxR (R*) binds to

the PluxI promoter causing expression of the killer gene (ccdB) and cell death. Both luxR and luxI are the under

control of an inducible promoter Plac promoter. (b) Experimentally measured growth curves with the circuit ON

(open circles) and OFF (filled circles). The model predictions are in solid (ON) and dotted (OFF) lines. (c) A time

course showing the concentration of killer gene (ccdB) produced in the ON and OFF cultures. This was measured by

assaying the LacZ activity because the ccdB was fused to lacZa. There is no ccdB concentration line for model

prediction of the OFF culture because it is always zero. (From You, L., Cox, R.S. III, Weiss, R., and Arnold, F.H.,

Nature 428, 868, 2004.)



Experimentally, this population control circuit can be turned ON by inducing the quorum sensing

module. As expected, the uninduced (OFF) culture grew exponentially until reaching a stationary phase

at the maximum density the culture medium could sustain (Figure 20.3b). The ON culture initially grew

similarly as the OFF culture until a threshold was reached at 7 h after circuit activation. The culture then

underwent damped oscillations that were likely the result of a delay in the negative-feedback loop, which

can be caused by the synthesis and transport of the signal, activation of LuxR, expression of ccdB, and the

cascade leading to cell death by ccdB. Even though negative feedback is usually associated with increased

output stability, such long cascades can cause oscillations. In particular, recent measurements using a

microchemostat demonstrated that the circuit was able to generate sustained oscillations for up to

several hundred hours [94].

Importantly, this circuit also demonstrated that a certain degree of noise can be useful for program-

ming robust dynamics via cell–cell communication. Specifically, cell-to-cell variations are essential for

generating robust dynamics in the population control circuit. If cells performed identically, they would

all die upon reaching the critical density that leads to a sufficient level of the killer protein. Precisely

because of cell-to-cell variations, some cells died while others were left to survive and the overall

population dynamics could be robustly regulated.

In addition to auto-communication within a single population, cell–cell communication has also

been used to program communication between different populations and to coordinate pattern forma-

tion. Basu and colleagues created a pulse generator that is activated by a signal from nearby ‘‘sender’’

cells. An interesting aspect of this interaction is the spatiotemporal behavior. Specifically, the pulse-

generating cells were able to differentiate between communication from nearby and far-away sender cells

[95]. This spatiotemporal behavior led to the development of a pattern formation circuit. Similar to the

previous circuit, sender cells were used to produce an intercellular signal but this time the receiver cells

responded to the gradient by producing a bulls-eye pattern around the chemical source [23].

20.4 Applications

In addition to decoding the ‘‘design principles’’ of biological systems, synthetic gene circuits can also

find wide applications in diverse areas. These applications include innovative gene regulation systems

[11,20,24,26,96–99], gene therapy [100], drug development [101], cellular computation [102], stem cell

reprogramming [103], and bioremediation [104]. Here we discuss a few examples in more detail.

20.4.1 Gene Therapy

An important application area for synthetic biology is the development of innovative gene circuits for

therapeutic purposes. Recent years have witnessed encouraging advancements in the field of cancer gene

therapy in the creation of novel adenoviral vectors. For example, an oncolytic adenovirus dl1520, first

coined by its creators [105], has shown potential use in specific targeting of tumor cells. Pioneered by

McCormick and colleagues [106], ONYX-015 selectively propagates in cells with inactivated p53,

a common defect in tumor cells (Figure 20.4b). On the other hand, normal cells have active p53 and

ONYX-015 causes apoptosis (Figure 20.4a). Both pathways force the cell to enter the S-phase by increasing

the amount of free E2F. In normal cells, the combination of entering the S-phase and a viral infection results

in p53 inducing a cell cycle arrest or an apoptosis to prevent proliferation. Cancer cells with nonfunctional

p53, however, will allow replication of viral particles, leading to selective killing of these cells.

The concept of selective targeting is further expanded upon by Johnson and colleagues [107]. Taking

advantage of the mutations in the pRb pathway, common to most human cancers, they restrict

adenoviral infection to tumor cells with high efficiency and specificity. Their strategy is based on

identical dependence of both human cells and human adenovirus on the loss or mutation of Rb for

uncontrolled proliferation. The E2F promoter is known to be abnormally active in tumor cells. Thus, the

engineered human adenovirus ONYX-411, whose promoter of E1A or E1B has been replaced by the E2F

promoter, only replicates in human tumor cells with Rb-pathway defects [107].



Although ONYX-015 is in its phase III of clinical development and the applicability of ONYX-411 is

being explored with promising results, there is much room for improvement. First, effective killing of

tumor cells through highly specific and efficient viral integration is most desirable. In fact, the issues of

efficiency in gene integration, specificity, and effectiveness are common to many fields of gene therapy.

Another limitation is variability in vector interactions with the hosts. In optimizing therapeutic benefits

in various disease treatments, synthetic biology plays a key role by providing ways to design robust gene

circuits that will function with increased reliability.

So far, specificity has been improved by various promoter design techniques [108–111] and increased

efficiency can be achieved by the better understanding of vector integrations into cells [112–114].

Furthermore, interdisciplinary efforts and the adoption of gene delivery techniques, such as electropor-

ation and ultrasound, contribute to the growth of gene therapy [115]. These technologies and method-

ologies will provide ways to design more sophisticated genetic circuits that will confer improved

efficiency, specificity, and effectiveness.

20.4.2 Drug Development

Many metabolites such as drugs are of natural origin but are difficult to extract from their native hosts.

This rarity makes them expensive to produce in the quantities necessary to treat patients. In an effort to

produce these rare drugs cheaply and in large quantities, microbes have been engineered by inserting the

genes necessary to produce the drug or its precursor [20,101,116]. Bacteria are suitable hosts because

they grow rapidly to produce large quantities of the drug, and it is easier to extract the product from

bacteria as compared with plants [117]. In these efforts, the major challenge is to balance the expression

of the drug with the host’s natural metabolic processes such that the host is not adversely affected [101].

For example, a successful attempt to produce the precursor of antimalarial drugs in bacteria was

accomplished by transferring the genes from Artimisia annua, the native plant, to E. coli. However,

(a)

(b)

Normal cells

Cancer cells

Viralproliferation

Cell cyclearrest

Apoptosis

ViralproliferationE1a

E1a

Rb

Rb

E2F

E2F

p53

FIGURE 20.4 Onyx-015 viral vector circuit design for cancer gene therapy. In normal cells, (a) expression of viral

vector gene E1a leads to increased E2F. An increase in E2F results in viral proliferation and increased p53. Excess p53

leads to cell cycle arrest and apoptosis, destroying the virus. On the other hand, in cancerous cells (b) p53 is inactive

(marked out with an X). Viral proliferation occurs selectively in cancerous cells.



initial yields were limited by the synthase, a key enzyme in the production of the precursor. Conse-

quently, the production of the synthase and other upstream molecules were increased by optimizing

existing genes and introducing biosynthetic pathways from S. cerevisiae. Using a specialized pathway

from S. cerevisiae, rather than the existing pathway in E. coli, circumvented the inhibitory elements

found in yeast while also bypassing those in E. coli. In the final engineered E. coli strain that incorporated

genes from different organisms, final yields of the artemisinin precursor amorphadiene were increased

>10,000-fold with room for further optimization [117]. The benefits of being able to produce large

quantities of rare drugs that are in high demand are quite obvious. The success of this work has recently

attracted major investment from the industry [118], which will encourage broader efforts to engineer

bacteria for drug and metabolite production.

20.4.3 Biotechnology Applications

By bringing discovery and utilization of novel genetic components together with a rational design

approach, synthetic biology opens up tremendous possibilities for environmental engineering and

biomedical discovery [119] However, use of recombinant DNA techniques invites risks commonly

associated with conventional genetic engineering such as horizontal gene transfer to other species and

introduction of new allergens and toxins to the environment [120,121]. Furthermore, the potential to

realize desired behavior in organisms through rational design might translate into greater risks that are

malevolent or inadvertent in nature [122]. A reasonable way to deal with these risks would be to

make the containment of the proposed genetic circuitry an essential part of the design process. This way,

the effectiveness of the containment could be made to match the estimated risk [123]. For example,

containment in bacteria can be achieved through a built-in safeguard mechanism that can be imple-

mented using the quorum sensing based population control discussed earlier [22].

One practical application area for synthetic biology, where aforementioned risks should be minim-

ized, is environmental biotechnology. Microorganisms are commonly used for pollution reduction

(bioremediation) and can provide safe and cost-effective alternatives to existing physicochemical

methods [124]. However, risks and concerns associated with the release of recombinant microorganisms

into the environment [125] currently limit their widespread use. Therefore, researchers are designing

containment systems that will minimize horizontal gene transfer and uncontrolled proliferation of

recombinant microorganisms (for a good review see Ref. [104]). In order to realize the full potential

of synthetic biology in this area and answer safety concerns, microorganisms should be equipped with

not only biodegradation pathways but also conditional lethality systems that will ensure control over the

genetic material being released. One striking example of coupling of a biodegradation pathway with a

containment system is achieved by engineering a streptavidin based lethality system that is controlled by

TOL plasmid for aromatic hydrocarbon metabolism [126] In this system, the TOL pathway, which is

capable of breaking down toxic aromatic compounds, is used to control the expression of a streptavidin

gene, which binds to biotin, an essential coenzyme for microorganisms. Depletion of the environmental

inducer of the TOL pathway, m-methylbenzoate, led to streptavidin expression and eventually cell death

with remarkable efficiencies around 10�7–10�8 mutant escape rates [126].

Containment is an important issue for synthetic constructs; moreover, synthetic biology can address

this issue and tackle environmental pollution problems by introducing new perspectives and techniques.

Although the use of genetic engineering for biodegradation is not novel [127], design ideas from basic

circuits to improve robustness against mutations, and attain control at the level of populations rather

than at the level of individuals [22,128] can improve killing efficiencies of containment systems.

20.5 Outlook

We have presented recent progress in the nascent field of synthetic biology and highlighted a few

application areas. We believe that synthetic biology has great potential for numerous applications that

go well beyond what we can envision today. Nevertheless, many challenges still remain. A major hurdle is



the elucidation of control mechanisms that can ensure robust function of synthetic gene circuits. To this

end, synthetic biologists will benefit from rapid advances in systems biology [65,129–132]. On one hand,

efforts in systems biology will continue to expand the list of building blocks that serve as the foundation

of synthetic gene circuits. On the other, they will likely generate substantial lessons for optimal circuit

design from naturally biological systems [133–135].

Another challenge lies in the measurement and monitoring of intracellular and intercellular dynamics

with high precision. For de novo engineering of gene circuits, it has become clear that precise

characterization of circuit dynamics critically depends on well-controlled experimental conditions and

distinct cellular markers. To address this issue, the marriage between biology and nanotechnology can be

particularly promising [34].

Fine control of growth environments can be achieved using novel microfluidics devices [136,137]. For

instance, Balagadde and colleagues recently devised a microchemostat [94], an automated and continu-

ously operated microfluidic device with a reactor volume of 16 nL, which is an order of magnitude

smaller than conventional culturing systems. When applied to the population control circuit described

above, the microchemostat significantly delayed the process of mutants arising and enabled measure-

ments of complex population dynamics for more than 500 h with single-cell resolution. This work has

focused on observation of cell morphology in addition to cell density, but it can be readily adapted to

measure gene expression dynamics reported by fluorescence or luminescence.

Complementary to culturing systems, a wide array of nanosensors has been developed to facilitate

analysis of single cells and protein dynamics inside the cell [138–141]. We anticipate that these diverse

micro- and nanoscale devices can be integrated for elucidating temporal–spatial dynamics of cellular

processes with ever-increasing resolution.

Acknowledgments

We thank Chee Meng Tan, Jun Ozaki, and Guang Yao for their comments and suggestions on the

chapter.

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[AQ1] Kindly update reference 119.

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