Imagine detecting and instantly counteracting an overdose through a micro in vivo device. Or an early detection system that warns of an impending heart attack based on trace biomarkers. Or a pill that can diagnose disease. New synthetic biology research out of MIT paints these seemingly science fiction scenarios as realistic in the not-too-distant future with a new approach to protein switches. 

The burgeoning field of synthetic biology has already gifted us with the incredible. Active applications of design advances in biocatalysts, protein switches, gene switches, and genome engineering tools include intracellular biosensor devices. Proof of concept exists for micro engineered biosensors that can be swallowed and wirelessly relay real-time detection of internal bleeding. 

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To date, the field has primarily relied on lengthy and resource-taxing transcription and translation processes that take minutes, hours, or even days to respond to inputs. Applications requiring faster, near-instantaneous responses—like those relating to cell signaling or metabolism—require engineering fast protein-protein circuits with an eye to systems behaviors. This approach faces challenges relating to biological complexity and the unknown. How the field of synthetic biology advances from here depends on how these challenges are addressed.

Engineering a path through biological complexity

Biological complexity is a tricky beast—one that stymies progress in synthetic biology. Synthetic biology is a collision of biology, chemistry, and engineering that aims to engineer new biological functions and inform systems biology research. 

Fundamental to an engineering approach to programming biological functions and cell behaviors is introducing modularity—breaking down complex processes into manageable units that can be assembled, reorganized, and connected to existing endogenous functions. Engineering relies on predefined materials with predictable behavior, on-demand procurement, and foundational rules or models dictating how materials can be combined¹. Establishing this baseline within biological systems is fraught with challenges. 

Starting from nucleic acid design and regulation has resulted in impressive strides in the field. Genetic engineering was in full swing during synthetic biology’s formation and forms the base of early established abstraction levels: “DNA” manipulation, biological “parts” like proteins, “devices” assembled from parts, and “systems” from patterns of devices¹. It is—to an extent—modular in form and allows engineered functions to be considered separately from endogenous functions. The wealth of knowledge of regulation networks that has built up over the decades has allowed for the engineering of sophisticated networks. However, starting a function with DNA transcription and translation slows down any programmed biological response, in part due to competition for shared cellular resources, which limits the applications of the technology. 

Establishing networks composed solely of protein-protein interactions speeds biological response time, but is difficult, according to Ron Weiss, MIT professor of biological engineering, electrical engineering, and computer science. Understanding how to design appropriate chimeric proteins and reliably predict the upstream and downstream interactions is still developing and will prove a major challenge to the field moving forward. It highlights the need for thinking of engineered networks as part of a whole.

Weiss describes a current perspective shift, “I think a lot of our [early] thinking in synthetic biology, certainly mine, [was] ‘let's build a circuit and then put it into the cell, but without [fully considering] endogenous pathways.’” According to Weiss, improved understanding of the systems context around embedded engineered networks is critical to success in the field. While conversations on this need are as old as the field, Weiss draws a distinction between talking about it and investigating it deeply. Once synthetic biology and systems biology are better integrated, he believes that both areas will provide valuable insights into the other, “ultimately [paying] huge dividends.” 

While greater insights into systems biology will aid synthetic biology in managing complexity as research expands in scope, it’s possible that improved standardization will reduce it. The issue of biological complexity is frequently raised when assessing reproducibility of research^2–4. But how much variability in results is due to inadequate standardization of methods, protocols, and measurements? While in-depth conversations around the need for improved measurements and standardization in biology—and across the sciences—have been ongoing for years^3,5, correction in the research community has lagged^2,6,7. Measurements of biological activity, for example, are often relative. This increases ambiguity in results across the field but becomes more urgent when involving engineering efforts. 

Jacob Beal, a senior scientist at Raytheon BBN Technologies, is part of the wave of researchers pushing for improved consistency. He believes that a considerable portion of the variability and unpredictability in results would be eliminated with improved standardization. “There's a huge amount of time and energy wasted just trying to recreate missing information about units, protocols, and designs,” he explains. He’s observed researchers waste “months or even years debugging tricky biological issues” that turn out to be instrument or protocol related that—once fixed—demonstrates “more systematic and predictable” biology than previously believed. Beal expects that once enough labs commit to a particular minimum information quality, reduced time and effort costs will instigate “a vast acceleration of the field.”

A new approach

A new paper in Science describes an engineered protein circuit that uses reversible protein phosphorylation-driven interactions.⁸ As Deepak Mishra, lead author and MIT research associate in biological engineering, notes in a press release, this provides “a methodology for designing protein interactions that occur at a very fast timescale, which no one has been able to develop systematically.” 

The authors used a combination of endogenous and exogenous proteins to build complexity in a novel, reversible bistable toggle switch. Taking it a step further, they demonstrated its ability to control basic cell processes by tying the switch to cell division, successfully flipping on and off a yeast cell’s ability to bud by alternately exposing the yeast to two different chemical signals.

Using endogenous proteins in the network, the authors created a more complex toggle than most others to date, with more dependencies. Weiss, senior author, hopes this demonstration of incorporating existing biological systems in device design will contribute to pushing the boundaries of synthetic biology, particularly when it comes to building complexity and drawing inspiration from existing sophisticated networks.

The added layers and complexity in their toggle circuit prompted the authors to search the study organism for similar endogenous toggle circuits. So far, according to Weiss, regulatory network discovery has been limited by the streetlight effect, searching familiar areas for familiar topologies. The authors instead searched for a diverse array of—less optimal but potentially more evolutionarily probable—topologies that would achieve the same result. They found six. “We wouldn’t think to look for those because they’re not intuitive… This is a new, engineered-inspired approach to discovering regulatory networks in biological systems,” says Weiss.

This marks a considerable step forward in designing protein circuit devices. The authors see immediate use in developing biosensors for environmental pollutants, particularly given high sensitivity to triggers. Future development of similar custom protein networks opens the door to a wide range of diagnostic capabilities. 

Similar networks can add complexity to the type of micro engineered biosensor mentioned earlier, suggest the authors. This could allow for detection of multiple biomarkers, such as those associated with cancer, neurodegenerative disease, inflammation, or infection, and changes in concentration over time^9,10. There’s even a potential for immediate intervention. “You could have a situation where the cell reports that information to an electronic device that would alert the patient or the doctor, and the electronic device could also have reservoirs of chemicals that could counteract a shock to the system,” Weiss notes in the press release.

There is more work to be done before realizing these potentials, both regarding the findings and within the field. Understanding the modularity of fusion proteins and signal strength for this sensor is key, says Beal. He also notes the work that remains to build consistent systematic libraries. And, of course, a better understanding of circuitry integration is needed to drive smart designs. Still, in a field with immense progress year over year, it seems that when standardization leads to reliable reproducibility, anything’s possible.

References:

1. Endy, D. 2005. Foundations for engineering biology. Nature 438, 449–453. DOI: 10.1038/nature04342. https://www.nature.com/articles/nature04342.
2. Coxon, C. H., Longstaff, C. & Burns, C. 2019. Applying the science of measurement to biology: Why bother? PLoS Biol. 17, e3000338. DOI: 10.1371/journal.pbio.3000338. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6605671/.
3. Vilanova, C. et al. 2015. Standards not that standard. J. Biol. Eng. 9, 15–18. DOI: 10.1186/s13036-015-0017-9. https://jbioleng.biomedcentral.com/articles/10.1186/s13036-015-0017-9.
4. Sené, M., Gilmore, I. & Janssen, J. T. 2017. Metrology is key to reproducing results. Nature 547, 397–399. DOI: 10.1038/547397a. https://www.nature.com/articles/547397a.
5. Plant, A. L. et al. 2018. How measurement science can improve confidence in research results. PLoS Biol. 16, e2004299. DOI: 10.1371/journal.pbio.2004299. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2004299.
6. Stark, P. B. 2018. No reproducibility without preproducibility. Nature 557, 613. DOI: 10.1038/d41586-018-05256-0. https://pubmed.ncbi.nlm.nih.gov/29795524/.
7. Beal, J. et al. 2020. The long journey towards standards for engineering biosystems. EMBO Rep. 21, e50521. DOI: 10.15252/embr.202050521.
8. Mishra, D. et al. 2021. An engineered protein-phosphorylation toggle network with implications for endogenous network discovery. Science 373, eaav0780. DOI: 10.1126/science.aav0780. https://science.sciencemag.org/content/373/6550/eaav0780.
9. Gibson, D. G. et al. 2010. Creation of a bacterial cell controlled by a chemically synthesized genome. Science 329, 52–56. DOI: 10.1126/science.1190719. https://science.sciencemag.org/content/329/5987/52.
10. Duhkinova, M., Crina, C., Weiss, R. & Siciliano, V. 2020. Engineering intracellular protein sensors in mammalian cells. J. Vis. Exp. 185, e60878. DOI: 10.3791/60878. https://pubmed.ncbi.nlm.nih.gov/32420982/.