Re-wiring gene networks of a natural biological cell for customized behavior has promising applications in biomedicine and biotechnology, made possible via a broad field of science known as - synthetic biology.
The field has brought together scientists and engineers to synthesize genetic logic circuits and reprogram living cells, inspired by the design principles of electronic logic circuits. The concept was first appreciated more than 50 years ago in 1961, at a Cold Spring Harbor Symposia on Quantitative Biology.
The primary aim of gene circuit engineering is to understand how natural systems are built by re-constructing them in the lab. The ultimate vision is to enhance the natural gene construction blueprint of cells, to build futuristic ‘smart plants’, create therapeutic cells against super-bugs, therapeutic agents that correct genetic disease, and green fuel for cars, to name a few plausible innovations – still at their hypothetical stage.
Potential uses of gene-circuit engineering
Driven by recent advancements in genome engineering research, what has been achieved so far in synthetic gene circuit engineering, is remarkable.
Natural cells inherently respond to their own environment and co-ordinate tasks in space and time, based on a myriad of underlying computational operations. At the level of the gene and protein in eukaryotic cells and prokaryotic cells, complex biological processes take place, therefore cells behave like ‘living computers’ but with biological inputs and outputs.
Eukaryotes and Prokaryotes
Biologists and engineers harnessed this natural complexity to achieve logical forms of cellular control by engineering biological circuitry at the level of genes and proteins. In the early 2000s, the first successful design and construction of synthetic gene networks were achieved in the bacterial organism Escherichia coli (prokaryote), the research was published in two separate Nature Letters. The work detailed the construction of biological equivalents of electronic memory storage and time-keeping known as a ‘gene-toggle switch’ and a ‘repressilator’, which also occur naturally in certain biological organisms.
Initial gene circuits designed in bacteria
Similar to the design of silicon-based electronic circuits, the inherently modular (compartmentalized) nature of cell signalling and gene regulatory networks can be designed for synthetic reconstruction. This is accomplished by engineering three synthetic compartments representing sensors >> internal logic circuits >> and actuators. Inspired by electronic engineering, the basic gene engineering equivalents include the following components: sensors = biological receptors of input detection, internal logic circuits = genetic logic gates to determine a response for inputs, and the actuators = cellular mechanisms to produce desired output. Typically a fluorescent reporter gene will be used to measure the output of interest.
Basic modular design for gene-circuit engineering
Re-construction of gene circuits will be achieved with well-characterized genes and proteins, using different methods in synthetic biology. As with silicon-based electric circuit design, customized genetic circuit modules too can have logic gates (AND/NAND/NOR gates) for specifically programmed biological control. Similar to the principles of Boolean logic gates, synthetic internal regulatory networks can be constructed to integrate multiple signals, activate genes and produce the desired/programmed output. This circuit isn't electronic, however, it is entirely biological.
A closer look at genetic logic gates inspired by electronics
The challenge in biology, at least for now is that you can't assemble billions of transistors (genetic analogs), as you do on a piece of silicon.
Synthetic systems model native biology
Living cells respond to environmental signals via a complex collection of reactions that can be computationally classified as digital (a definite ON/OFF response) or analogue processing (adjustments to gradual changes in concentration, temperature etc). Biological systems are inherently analogue in nature, but re-constructing such complexities is very challenging, therefore most work in synthetic biology is focused on the digital approach as it's easier to program. Hence the resemblance of gene circuit engineering to silicon-based electronic circuits with logic gates - in short, if this were electronics, DNA is the wire.
A fundamental challenge during design is the cross-talk between native and synthetic parts, which should be prevented by carefully ‘decoupling’ synthetic compartments from the native system. Although nature has achieved precise engineering of cellular function through evolution, we are only beginning to understand how to utilize this level of complexity for predictable, human-designed function. Understanding how natural systems overcome such limitations will lead to better design principles.
Despite its potential, gene circuit design based engineering invariably remains one of the most challenging techniques of genetic engineering.
Pioneering proof-of-principle gene circuits engineered
A few examples of proof-of-principle gene-circuits engineered by pioneers in this field include; synthetic mammalian and bacterial biosensors capable of sensing changes in the environment, drug design based on drug target identification, understanding disease mechanisms, development of pharmaceuticals and chemicals. For example, synthetic biosensors can be used in industrial bioreactors to communicate the state of a micro-environment for which it was specifically programmed. Notable examples include; reconstruction of the SARS (severe acute respiratory syndrome) coronavirus and the Spanish Influenza virus to understand genetic mutations that led to increased virulence in humans. The design of a drug discovery framework to understand multi-drug resistant tuberculosis and the use of synthetic biology devices themselves as therapies to engineer entire viruses to target specific disease causing agents and mechanisms.
Primary shortcomings: amidst a number of limitations coupled to technological setbacks in the past few decades, the manual re-construction of gene circuits has been time consuming and sometimes unreliable. Furthermore, applications of these proof-of-principle cell-based synthetic gene networks outside the laboratory has been restricted by concerns of biosafety and practicality of the cellular host.
The present timeline is best described as the 'rebirth of synthetic biology' - in light of rapid, efficient advancements in gene editing and machine learning technologies. Specifically, the emergence of CRISPR (clustered regularly interspaced short palindromic repeat)-Cas9 genome editing system, can improve gene-circuit engineering technically by preventing cellular cross-talk and expanding the number of logical operations a cell can perform with increased efficiency/precision.
The design algorithm used for silicon circuit development can also be applied to automatically create gene circuits by using a computing language called Verilog (that electrical engineers use to design silicon circuits). The gene circuit function can be typed in as commands to specify the kind of cell used, intended function and explain how inputs and outputs should be logically connected. This information will be translated to a DNA sequence via the software program Cello, which will simulate function of the gene circuit and its impact on the cell.
Cello-based automation for gene circuit design
In this way, any circuit predicted to impose an unacceptable burden on cell growth will be flagged to the user prior to actual design. This will prevent resource-limiting and time consuming, trial and error analysis – for rapid design, construction and troubleshooting of complex gene circuitry.
In practice, these technical advances will help accelerate the manifestation of synthetic cells for efficient engineering of programmed gene-circuits, to understand cellular mechanisms and carryout human-designed functions.
This article is primarily based on a review by Ahmad S. Khalil and James J. Collins published in Nature Reviews Genetics May 2010, available via | doi:10.1038/nrg2775