The problem of mutation in synthetic biology

Science has come to a point where even the simplest and smallest organisms are now being used to benefit humanity. The advances both in knowledge and technique in the life sciences has allowed us to harness these living factories for our own needs. Everything from plastics to medicines and foodstuffs to construction material is being created by manipulating the biological machinery of bacteria.

However, our ability to engineer organisms to complete specified functions is jeopardized by the instability of the genetic instructions that we use to re-program life. Mutations can emerge that disrupt or change how a system acts, which poses a basic limit to how safe and effective genetically-modified systems can be.


Evolution in synthetic biology: promises and perils

The basic mechanism of Darwinian evolution which has allowed for the breathtaking diversity of life and organisms to develop their amazing abilities is also that which can frequently lead to their downfall. Random mutation is the driving force behind adaptation and has allowed life to survive for millennia but the same action can also lead to unwanted consequences. In bacteria, a genetic mistake can result in a malformed protein leading to severe dysfunction or apoptosis; in humans, similar mutations in the DNA can lead to cancer or other serious genetic pathologies. The goal of synthetic biology at this time is to harness the best facets of spontaneous mutation while preventing any deleterious effects.

Does a mutation in one cell make a difference?

The first and most obvious question that someone would ask is whether a single mutation in one region of one gene of one chromosome of one cell can possible make any sort of difference in a bioreactor where millions of billions of cells are all still functioning as normal. The problem is with basic selective pressures stemming from a differential metabolic load (Ellis et al. 2015). If the mutant cell has a mutation that no longer allows it to generate its product, that cell’s transcriptional/translational machinery is no longer occupied by the foreign DNA. This relieves the stress on the cell and its resources allowing it to focus on its fundamental needs, which are growth and division. The other cells in the population however still have this metabolic load and therefore are at an evolutionary disadvantage. Mathematical models (Vanderbilt iGEM 2015) have shown that even a minimal relief in metabolic load can allow a single mutant cell to take over an entire population in a relatively short time span.

Real-world consequences of mutation


The implications of reducing evolutionary potential are far-reaching. All of synthetic biology is impacted by the instability of genes, but there are several areas where it is of particular concern, namely manufacturing, medicine, and biosafety. In terms of production, bioreactors are especially vulnerable to gene mutation due to the metabolic stress exhibited by the cells in such an environment. The constant push for maximum production gives an evolutionary advantage to any cells that spontaneously mutated to produce less or no product. An entire incubator could be overtaken by these mutated cells leading to a loss in money, time, and resources.

In a medical context where pharmaceutical agents are produced, a mutation can have even worse consequences of creating a deleterious drug that would be harmful, such as antibody-producing strain of cells that undergoes a spontaneous mutation leading to unintended interactions. The advent of gene therapy which allows insertion of genes directly into living cells poses parallel difficulties in terms of ensuring continued function. The cell will not only try to get rid of the foreign DNA because of its associated metabolic load, the sequence will be prone to mutations that either cause a nonfunctional or dysfunctional protein product.

Similarly, genetically modified organisms that are introduced to the environment have the possibility to escape their intended area as well as transfer their genes to other organisms. To combat this issue, many have developed sophisticated killswitch circuits, but what has not been addressed is the degradation of this safety mechanism which is subject to more evolutionary pressure than other components due to its lethality. Both the circuit and the genes composing it are subject to mutation resulting in inactivation and the spread of GMO. (Moe-Behrens, Davis, and Haynes 2013).

A way to prevent mutation?

At the heart of our strategy is an advanced computational algorithm that integrates decade’s worth of scientific data in order to identify and correct the highly-mutation prone ‘hotspots’ that lurk in every gene. Our strategy has a strong foundation in a rich literature from the fields of cancer biology and others that have annotated and characterized mutation hotspots. This year we have focused on ultraviolet radiation and oxidation as we believe they are the two primary sources of mutation that are found in an external environment and bioreactors.



Ceroni, F., Algar, R., Stan, G.-B., and Ellis, T. (2015). Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat Meth 12, 415–418.

Moe-behrens GH, Davis R, Haynes KA. Preparing synthetic biology for the world. Front Microbiol. 2013;4:5.