Global advances in synthetic biology


Lu Rahman selects some recent synthetic biology innovations and the potential they hold to benefit drug discovery. 

The rapidly growing area of synthetic biology – including molecular biology, biotechnology, biophysics, and genetic engineering – is having a marked impact on the drug discovery landscape. Applying the principles of engineering to biology – redesigning organisms and biological parts to create synthetic components with new abilities – fuels the progression of medicine and gives researchers additional ability to tackle scientific issues. 

SkyQuest Technology recently released a report: Global Synthetic Biology Market Insights, which highlights the sector’s value and potential. It shows that the synthetic biology market was valued at US$9.5 billion in 2020, and it is expected to reach a value of US$38.7 billion by 2027. 

According to SkyQuest Technology, the pharmaceutical industry is worth an estimated $2 trillion dollars and is expected to grow at 5% a year to 2025. However, the development of drugs takes decades which is too long for most biotech start- ups looking for fast-track drug development programs. Having the ability to use methods like synthetic biology to expedite drug development times could revolutionise the pharmaceutical industry and provide affordable healthcare to millions of people globally. These factors can significantly contribute to the growth of the synthetic biology market, it adds. 

It is believed that the term synthetic biology was created in 1970 by geneticist, Waclaw Szybalski1 as work was being carried out on the development of DNA sequencing and synthesis techniques. Advances in this field have progressed significantly in the last 50 years or so and show promise in the quest to tackle a range of diseases and illnesses. As a result, innovation is ongoing with many companies keen to take advantage of the opportunities this sector holds. 

An example of this is eureKARE a company focused on financing and building next generation biotechnology companies in synthetic biology which has launched its first synthetic biology studio located in Belgium. 

The company is developing a pan-European biotech studio network covering areas where synthetic biology can create disruptive solutions and high market value products. eureKARE’s synthetic biology studios will focus on biomedical applications – among others – in different European innovation hotspots. Its strategy is based on identifying, selecting, and nurturing high-quality European science and this model aims to bring together scientists from across Europe, combining tools and expertise from academia and industry. 

According to Georges Rawadi, Chief of Biotech Studio Development at eureKARE: “In just over a year, eureKARE has completed a number of key investments across synthetic biology and co-founded a European SPAC in biomanufacturing. Throughthe launch of the first synthetic biology studio in Belgium, eureKARE is championing a new model of start-up creation and development to build Europe’s future synthetic biology leaders and address the world’s most critical health and environmental challenges.” 

Drug-resistant synthetic antibiotics 

The Rockefeller University2 recently reported the development of a synthetic antibiotic that could potentially work against drug-resistant bacteria. A study published in Science3 outlines how the compound, named cilagicin, works well in mice and employs a novel mechanism to attack MRSA, C. diff, and several other deadly pathogens. It appears to neutralise even drug-resistant bacteria. 

The results suggest that a new generation of antibiotics could be derived from computational models. “This isn’t just a cool new molecule, it’s a validation of a novel approach to drug discovery,” said Rockefeller’s Sean F Brady. “This study is an example of computational biology, genetic sequencing, and synthetic chemistry coming together to unlock the secrets of bacterial evolution.” 

“Eons of evolution have given bacteria unique ways of engaging in warfare and killing other bacteria without their foes developing resistance,” said Brady, the Evnin Professor and head of the Laboratory of Genetically Encoded Small Molecules. Antibiotic drug discovery once largely consisted of scientists growing streptomyces or bacillus in the lab and bottling their secrets to treat human disease. 

But with the rise of antibiotic-resistant bacteria, there is an urgent need for new active compounds. The Brady lab has been finding antibacterial genes in soil and growing them within more lab-friendly bacteria. But even this strategy has its limitations. Most antibiotics are derived from genetic sequences locked within so-called biosynthetic gene clusters, which are genes that function as a unit to collectively code for a series of proteins. But those clusters are often inaccessible with current technologies. 

“Bacteria are complicated, and just because we can sequence a gene doesn’t mean we know how the bacteria would turn it on to produce proteins,” Brady added. “There are thousands and thousands of uncharacterised gene clusters, and we have only ever figured out how to activate a fraction of them.” 

Brady and colleagues turned to algorithms. By teasing apart the genetic instructions within a DNA sequence, modern algorithms can predict the structure of the antibiotic like compounds that a bacterium with these sequences would produce. Organic chemists can then take that data and synthesise the predicted structure in the lab. 

“The molecule that we end up with is presumably, but not necessarily, what those genes would produce in nature,” Brady said. “We aren’t concerned if it is not exactly right – we only need the synthetic molecule to be close enough that it acts similarly to the compound that evolved in nature.” 

Postdoctoral associates Zonggiang Wang and Bimal Koirala from the Brady lab began by searching through an enormous genetic-sequence database for promising bacterial genes that were predicted to be involved in killing other bacteria and hadn’t been examined previously. The “cil” gene cluster, which had not yet been explored in this context, stood out for its proximity to other genes involved in making antibiotics. The researchers duly fed its relevant sequences into an algorithm, which proposed a handful of compounds that cil likely produces. One compound, aptly dubbed cilagicin, turned out to be an active antibiotic. 

Cilagicin reliably killed Gram-positive bacteria in the lab, did not harm human cells, and (once chemically optimised for use in animals) successfully treated bacterial infections in mice. Interestingly, cilagicin was potent against several drug-resistant bacteria and, even when pitted against bacteria grown specifically to resist cilagicin, the synthetic compound prevailed. 

Cilagicin is still far from human trials. In follow-up studies, the Brady lab will perform further syntheses to optimise the compound and test it in animal models against more diverse pathogens to determine which diseases it may be most effective in treating. 

Beyond the clinical implications of cilagicin, however, the study demonstrates a scalable method that researchers could use to discover and develop new antibiotics. “This work is a prime example of what could be found hidden within a gene cluster,” Brady says. “We think that we can now unlock large numbers of novel natural compounds with this strategy, which we hope will provide an exciting new pool of drug candidates.” 

Designer cells 

In the UK, work being carried out at Imperial College4 has seen the engineering of designer cells which could help advance treatment for disease. 

Scientists from the Departments of Chemical Engineering and Chemistry have developed a way to engineer artificial cells that mimic how biological cells behave in response to environmental changes. This could have significant implications for our understanding of biology, in treating illness and in drug delivery. Producing such cellular architectures has been one of the ultimate goals of synthetic biology, as it would enable scientists to create designer cells with specific functions that are easier to control and predict than biological ones. 

A fundamental feature of biological cells across all forms of life is the compartmentalisation of cells, which can change in response to environmental stimuli. For example, when certain immune cells sense a virus, they release sub-compartments to their environment, which act as signal for other types of cells to destroy that virus. 

Previous efforts at replicating this dynamic feature of cells have only resulted in static compartmentalisation, which has hindered the biomimetic and technological potential of synthetic cells. 

A team of synthetic biologists has developed a method of mimicking the dynamic features of natural sub-compartments in artificial cells, which can exist either inside the cell or externally on its surface. This could pave the way for developments in treating illness and disease, and in targeted drug delivery. 

The team at Imperial used a ‘bottom-up assembly’ approach to develop artificial cells with sub-compartments, which can respond to chemical stimuli in their environment by changing their internal organisation. They can be engineered to disperse from the cell surface in response to chemical cues in the environment, or switch to a dispersed state within the cell lumen after sensing mechanical triggers. These structural rearrangements can be reversible and do not require complex biological machinery. 

Dr Yuval Elani, academic lead of this study, said: “Biological cells are highly dynamic and responsive, which is why they are so sophisticated. They constantly shift how materials inside are arranged, in response to their environment. 

“Taking inspiration from biology and building this feature into synthetic systems has great potential in biotechnology and therapeutics, something which we are now looking to exploit.”  

The understanding of how to build dynamic sub-compartments within cells in an essential first step in utilising this technology. Now researchers will need to focus on increasing its biological and technological relevance. For example, by engineering these synthetic cells to deliver medicines encapsulated in sub-compartments. 

Lead author Greta Zubaite added: “If a target of interest, for example a tumour, has a microenvironment that is different to that of healthy cells, the artificial cells could sense this and use it as cue to release drug-loaded sub-compartments. 

Drug carrying artificial cells could also be engineered to allow on site non-invasive treatment of disease or illness. The research we have carried out paves the way to this type of treatment.” 

Technology is also developing to optimise biological systems and a team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for this purpose, claiming it demonstrates its usability and versatility with a variety of biological examples5. 

Engineering of biological systems is indispensable in biotechnology and synthetic biology. Machine learning has also become useful. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labelled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems. 

The team at the Max-Planck-Institute for Terrestrial Microbiology has, it says, democratised machine learning. In its recent publication in Nature Communications, the team, along with partners from the INRAe Institute in Paris, presented their tool Machine- learning guided Experimental Trials for Improvement of Systems (METIS). The application is built so that it does not require computational skills and can be applied on different biological systems and with different lab equipment. 

Active learning, also known as optimal experimental design, uses machine learning algorithms to interactively suggest the next set of experiments after being trained on previous results, a valuable approach for wet-lab scientists, especially when working with a limited number of experimentally-labelled data. But one of the main bottlenecks is the experimentally-labelled data generated in the lab that are not always high enough to train machine learning models. 

“While active learning already reduces the need for experimental data, we went further and examined various machine learning algorithms. Encouragingly, we found a model that is even less dependent on data,” says Amir Pandi, one of the lead authors of the study. 

To show the versatility of METIS, the team used it for a variety of applications, including optimisation of protein production, genetic constructs, combinatorial engineering of the enzyme activity, and a complex CO2 fixation metabolic cycle. 

In application, the study provides novel tools to democratise and advance current efforts in biotechnology, synthetic biology, genetic circuit design, and metabolic engineering. 

“METIS allows researchers to either optimise their already discovered or synthesized biological systems,“ said Christoph Diehl, Co-lead author of the study. “But it is also a combinatorial guide for understanding complex interactions and hypothesis- driven optimisation. And what is probably the most exciting benefit: it can be a very helpful system for prototyping new-to-nature systems.” 

Synthetic biology has the potential to transform the way we develop drugs and tackle disease. As global advancements continue, the potential offered in this area of science is exciting and inspiring for both current and future scientists within this field. 

Included in the DDW Synthetic Biology eBook, sponsored by Evonetix


  1. the-origin-and-history-of-synthetic- biology 
  2. news/32306-a-synthetic-antibiotic- may-help-turn-the-tide-against-drug- resistant-pathogens/ 
  3. doi/10.1126/science.abn4213 
  4. news/237234/new-designer-cells- could-advance-treatments/ 
  5. https://www.mpi-marburg.mpg. de/1221347/2022-07-b?c=2511 


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