Freeing genetic circuits from the messy cellular context

Cells have limited resources available for gene expression. To make sure engineered genetic circuits are insulated from changes in resource availability, we designed and built a gene expression controller that works inside of mammalian cells.

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Engineered genetic circuits have the potential to open up a world of therapeutic possibilities. They can help program living cells with capabilities such as recognizing and killing cancer cells or repairing damaged tissue as in heart disease. In synthetic biology, there have been substantial efforts to make these circuits sufficiently robust and reliable for real-life application.

Despite these efforts, there are still many hurdles impeding the predictable behavior of engineered genetic circuits. One culprit is limited cellular resources. The cell has a finite pool of resources and did not evolve to supply resources to synthetic circuits in addition to its own natural components. When a genetic module draws too many resources, other modules are unable to keep the prescribed protein production, causing circuits to malfunction. In our recent Nature Communication paper1, we propose a solution to this issue by developing a feedforward controller that can adapt the expression level of a gene to significant resource loading in mammalian cells.

Our group’s first realization of the severity of resource competition in synthetic genetic circuits came in 2012 while testing a routine bacterial genetic circuit composed of two genes. Surprisingly, upon inducing expression of one gene, the expression of another independently regulated gene would dramatically decrease due to competition for translational resources2. Two years later in 2014, the pressing need for solving this problem became apparent to us when characterizing a simple bacterial two-gene activation cascade, ubiquitous in most genetic circuits.

We observed a rather shocking failure mode: the level of the output protein was decreased, instead of increased, by the input activator. How was it possible that two cascaded activators could lead to overall repression? We demonstrated that the cause was resource competition3. At the time, it struck me that researchers talk about building complex programs that cure cancer, but due to cellular context, we couldn’t even build a simple genetic circuit composed of two genes. Our team decided to develop solutions for resource competition, which would decouple genetic modules from their cellular context.  

The first such solution was a quasi-integral feedback controller that we implemented in bacterial circuits to decouple the expression of a gene of interest from the expression of any other gene, despite resource sharing4.  Meanwhile, Ron Weiss alerted me that resource competition was also likely playing a role in the mammalian circuits being built in his lab. Inspired by this, Ron and I joined forces to investigate mammalian resource sharing.

In 2015, graduate students Ross Jones and Yili Qian took over as leaders of the project and worked as a team to characterize resource competition across common promoters, transcriptional activators and cell lines used in mammalian synthetic biology, pinpointing resource competition as a pervasive problem that required an immediate solution. 

Using mathematical models and the endoribonuclease (endoRNase) library developed by Breanna DiAndreth5, the team designed a feedforward controller that could make the expression of any gene of interest decoupled from expression of other genes. Testing this controller in the lab, we were indeed able to successfully insulate gene expression from resource loading across mammalian cell lines. This indicated that we had found a successful solution to resource competition that will be widely applicable for engineering mammalian cells.

With this feedforward controller, we are moving one step closer to creating sophisticated programs in mammalian cells that are sufficiently isolated from the messy cellular context and hence more reliable for real-life application. ​


  1. Jones, R. D. et al. An endoribonuclease-based incoherent feedforward loop for decoupling resource-limited genetic modules. Nat. Commun. (2020). doi:10.1038/s41467-020-19126-9
  2. Gyorgy, A. et al. Isocost Lines Describe the Cellular Economy of Genetic Circuits. Biophys. J. 109, 639–646 (2015).
  3. Qian, Y., Huang, H. H., Jiménez, J. I. & Del Vecchio, D. Resource Competition Shapes the Response of Genetic Circuits. ACS Synth. Biol. 6, 1263–1272 (2017).
  4. Huang, H.-H., Qian, Y. & Del Vecchio, D. A quasi-integral controller for adaptation of genetic modules to variable ribosome demand. Nat. Commun. 9, 5415 (2018).
  5. DiAndreth, B., Wauford, N., Hu, E., Palacios, S. & Weiss, R. PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. BioRxiv (2019). doi:10.1101/2019.12.15.867150
  6. Frei, T. et al. Characterization and mitigation of gene expression burden in mammalian cells. Nat. Commun. 11, 4641 (2020).


Domitilla Del Vecchio

Professor, Massachusetts Institute of Technology

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