Synthetic Biology: Engineering Life To Examine It

Two scientific papers that were published in the journal Nature in the year 2000 marked the beginning of engineering biological circuits in cells. The paper “Construction of a genetic toggle switch in Escherichia coli” by Timothy Gardner, Charles Cantor and James Collins created a genetic toggle switch by simultaneously introducing an artificial DNA plasmid into a bacterial cell. This DNA plasmid contained two promoters (DNA sequences which regulate the expression of genes) and two repressors (genes that encode for proteins which suppress the expression of genes) as well as a gene encoding for green fluorescent protein that served as a read-out for the system. The repressors used were sensitive to either selected chemicals or temperature. In one of the experiments, the system was turned ON by adding the chemical IPTG (a modified sugar) and nearly all the cells became green fluorescent within five to six hours. Upon raising the temperature to activate the temperature-sensitive repressor, the cells began losing their green fluorescence within an hour and returned to the OFF state. Many labs had used chemical or temperature switches to turn gene expression on in the past, but this paper was the first to assemble multiple genes together and construct a switch which allowed switching cells back and forth between stable ON and OFF states.


The same issue of Nature contained a second land-mark paper which also described the engineering of gene circuits. The researchers Michael Elowitz and Stanislas Leibler describe the generation of an engineered gene oscillator in their article “A synthetic oscillatory network of transcriptional regulators“. By introducing three repressor genes which constituted a negative feedback loop and a green fluorescent protein as a marker of the oscillation, the researchers created a molecular clock in bacteria with an oscillation period of roughly 150 minutes. The genes and proteins encoded by the genes were not part of any natural biological clock and none of them would have oscillated if they had been introduced into the bacteria on their own. The beauty of the design lay in the combination of three serially repressing genes and the periodicity of this engineered clock reflected the half-life of the protein encoded by each gene as well as the time it took for the protein to act on the subsequent member of the gene loop.

Both papers described the introduction of plasmids encoding for multiple genes into bacteria but this itself was not novel. In fact, this has been a routine practice since the 1970s for many molecular biology laboratories. The panache of the work lay in the construction of functional biological modules consisting of multiple genes which interacted with each other in a controlled and predictable manner. Since the publication of these two articles, hundreds of scientific papers have been published which describe even more intricate engineered gene circuits. These newer studies take advantage of the large number of molecular tools that have become available to query the genome as well as newer DNA plasmids which encode for novel biosensors and regulators.

Synthetic biology is an area of science devoted to engineering novel biological circuits, devices, systems, genomes or even whole organisms. This rather broad description of what “synthetic biology” encompasses reflects the multidisciplinary nature of this field which integrates ideas derived from biology, engineering, chemistry and mathematical modeling as well as a vast arsenal of experimental tools developed in each of these disciplines. Specific examples of “synthetic biology” include the engineering of microbial organisms that are able to mass produce fuels or other valuable raw materials, synthesizing large chunks of DNA to replace whole chromosomes or even the complete genome in certain cells, assembling synthetic cells or introducing groups of genes into cells so that these genes can form functional circuits by interacting with each other. Synthesis in the context of synthetic biology can signify the engineering of artificial genes or biological systems that do not exist in nature (i.e. synthetic = artificial or unnatural), but synthesis can also stand for integration and composition, a meaning which is closer to the Greek origin of the word.  It is this latter aspect of synthetic biology which makes it an attractive area for basic scientists who are trying to understand the complexity of biological organisms. Instead of the traditional molecular biology focus on studying just one single gene and its function, synthetic biology is engineering biological composites that consist of multiple genes and regulatory elements of each gene. This enables scientists to interrogate the interactions of these genes, their regulatory elements and the proteins encoded by the genes with each other. Synthesis serves as a path to analysis.

One goal of synthetic biologists is to create complex circuits in cells to facilitate biocomputing, building biological computers that are as powerful or even more powerful that traditional computers. While such gene circuits and cells that have been engineered have some degree of memory and computing power, they are no match for the comparatively gigantic computing power of even small digital computers. Nevertheless, we have to keep in mind that the field is very young and advances are progressing at a rapid pace.

One of the major recent advances in synthetic biology occurred in 2013 when an MIT research team led by Rahul Sarpeshkar and Timothy Lu at MIT created analog computing circuits in cells. Most synthetic biology groups that engineer gene circuits in cells to create biological computers have taken their cues from contemporary computer technology. Nearly all of the computers we use are digital computers, which process data using discrete values such as 0’s and 1’s. Analog data processing on the other hand uses a continuous range of values instead of 0’s and 1’s. Digital computers have supplanted analog computing in nearly all areas of life because they are easy to program, highly efficient and process analog signals by converting them into digital data. Nature, on the other hand, processes data and information using both analog and digital approaches. Some biological states are indeed discrete, such as heart cells which are electrically depolarized and then repolarized in periodical intervals in order to keep the heart beating. Such discrete states of cells (polarized / depolarized) can be modeled using the ON and OFF states in the biological circuit described earlier. However, many biological processes, such as inflammation, occur on a continuous scale. Cells do not just exist in uninflamed and inflamed states; instead there is a continuum of inflammation from minimal inflammatory activation of cells to massive inflammation. Environmental signals that are critical for cell behavior such as temperature, tension or shear stress occur on a continuous scale and there is little evidence to indicate that cells convert these analog signals into digital data.

Most of the attempts to create synthetic gene circuits and study information processing in cells have been based on a digital computing paradigm. Sarpeshkar and Lu instead wondered whether one could construct analog computation circuits and take advantage of the analog information processing systems that may be intrinsic to cells. The researchers created an analog synthetic gene circuit using only three proteins that regulate gene expression and the fluorescent protein mCherry as a read-out. This synthetic circuit was able to perform additions or ratiometric calculations in which the cumulative fluorescence of the mCherry was either the sum or the ratio of selected chemical input concentrations. Constructing a digital circuit with similar computational power would have required a much larger number of components.

The design of analog gene circuits represents a major turning point in synthetic biology and will likely spark a wave of new research which combines analog and digital computing when trying to engineer biological computers. In our day-to-day lives, analog computers have become more-or-less obsolete. However, the recent call for unconventional computing research by the US Defense Advanced Research Projects Agency (DARPA) is seen by some as one indicator of a possible paradigm shift towards re-examining the value of analog computing. If other synthetic biology groups can replicate the work of Sarpeshkar and Lu and construct even more powerful analog or analog-digital hybrid circuits, then the renaissance of analog computing could be driven by biology.  It is difficult to make any predictions regarding the construction of biological computing machines which rival or surpass the computing power of contemporary digital computers. What we can say is that synthetic biology is becoming one of the most exciting areas of research that will provide amazing insights into the complexity of biological systems and may provide a path to revolutionize biotechnology.

ResearchBlogging.orgDaniel R, Rubens JR, Sarpeshkar R, & Lu TK (2013). Synthetic analog computation in living cells. Nature, 497 (7451), 619-23 PMID: 23676681





An earlier version of this article was first published here on the 3Quarksdaily blog.

Beautiful Animations of Cellular Processes

The professional animator and molecular biologist Janet Iwasa at Harvard Medical School is generating beautiful animations of cellular processes such as proteasome structure and function or endocytosis. Importantly, she has published these on her website with a Creative Commons license so that everyone has access to them. She has been interviewed by EarthSky, where she explains why she became a molecular animator.

Movie about the proteasome structure:

Movie about chromosome segregation:

Movie about protein translocation (movement of proteins across membranes):

There are plenty of other beautiful animations and illustrations on her website and I highly recommend that anyone with an interest in cell biology should explore those.

Is the Analysis of Gene Expression Based on an Erroneous Assumption?

The MIT-based researcher Rick Young is one of the world’s top molecular biologists. His laboratory at the Whitehead Institute for Biomedical Research has helped define many of the key principles of how gene expression is regulated, especially in stem cells and cancer cells. At a symposium organized by the International Society for Stem Cell Research (ISSCR), Rick presented some very provocative data today, which is bound to result in controversial discussions about how researchers should assess gene expression.

Ptolemey’s world map from Harmonica Macrocosmica

It has become very common for molecular biology laboratories to use global gene expression analyses to understand the molecular signature of a cell. These global analyses can measure the gene expression of thousands of genes in a single experiment. By comparing the gene expression profiles of different groups of cells, such as cancer cells and their healthy counterparts, many important new genes or new roles for known genes have been uncovered. The Gene Expression Omnibus is a public repository for the huge amount of molecular information that is generated. So far, more than 800,000 samples have been analyzed, covering the gene expression in a vast array of organisms and disease states.

Rick himself has extensively used such expression analyses to characterize cancer cells and stem cells, but at the ISSCR symposium, he showed that most of these analyses are based on the erroneous assumption that the total RNA content in cells remains constant. When the gene expression in cancer cells is compared to that of healthy non-cancer cells, the analysis is routinely performed by normalizing or standardizing the RNA content. The same amount of RNA from cancer cells and non-cancer cells is obtained and the global analyses are able to detect relative differences in gene expression. However, a problem arises when one cell type is generating far more RNA than the cell type it is being compared to.

In a paper that was published today in the journal Cell entitled “Revisiting Global Gene Expression Analysis”, Rick Young and his colleagues discuss their recent discovery that the cancer-linked gene regulator c-Myc increases total gene expression by two to three-fold. Cells expressing the c-Myc gene therefore contain far more total RNA than cells that don’t express it. This means that most genes will be expressed at substantially higher levels in the c-Myc cells. However, if one were to perform a traditional gene expression analysis comparing c-Myc cells versus cells without c-Myc, one would “control” for these differences in RNA amount by using the same amount of RNA for both cell types. This traditional standardization makes a lot of sense; after all, how would one be able to compare the gene expression profile in the two samples, if we loaded different amounts of RNA? The problem with this common-sense standardization is that it misses out on global shifts of gene expression, such as those initiated by potent regulators such as c-Myc. According to Rick Young, one answer to the problem is to include an additional control by “spiking” the samples with defined amounts of known RNA. This additional control would allow us to then analyze if there is also an absolute change in gene expression, in addition to the relative changes that current gene analyses can detect.

In some ways, this seems like a minor technical point, but I think that it actually points to a very central problem in how we perform gene expression analysis, as well as many other assays in cell biology and molecular biology. One is easily tempted to use exciting large scale analyses to study the genome, epigenome, proteome or phenome of cells. These high-tech analyses generate mountains of data and we spend an inordinate amount of time trying to make sense of the data. However, we sometimes forget to question the very basic assumptions that we have made. My mentor Till Roenneberg taught me how important it was to use the right controls in every experiment. The key word here is “right” controls, because merely including controls without thinking about their appropriateness is not sufficient. I think that Rick Young’s work is an important reminder for all of us to continuously re-evaluate the assumptions we make, because such a re-evaluation is a pre-requisite for good research practice.