Engineering Biology
Biomolecular Engineering Goal:

Integrated design of RNA-based regulatory systems for cellular control and information processing.

Current State-of-the-Art

There is a unique opportunity to construct information processing genetic circuitry out of RNA molecules versus proteins due to three factors: 1) a deep history of molecular computation with nucleic acids in vitro1Qian, L., & Winfree, E. (2011). Scaling up digital circuit computation with DNA strand displacement cascades. Science, 332(6034), 1196–1201. View publication.
Cherry, K. M., & Qian, L. (2018). Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature, 559(7714), 370–376.View publication.
; 2) the concept of nucleic acid strand displacement as a simple, yet highly modular and programmable mechanism to maintain and propagate changes of molecular state; and 3) the emergence of RNA secondary structure design tools that can implement new designs using these paradigms. Within the realm of in vitro nucleic acid ‘circuits’, there has been great progress in developing in vitro systems that can process information in much the same way as genetic regulatory circuits. In these systems, chemical state is defined as the concentration of specific nucleic acid species, and these states can be changed through designed interactions that can process information, such as logic evaluation2Seelig, G., Soloveichik, D., Zhang, D. Y., & Winfree, E. (2006). Enzyme-free nucleic acid logic circuits. Science, 314(5805), 1585–1588. View publication., or even perform complex computational tasks3Qian, L., & Winfree, E. (2011). Scaling up digital circuit computation with DNA strand displacement cascades. Science, 332(6034), 1196–1201.View publication.
Cherry, K. M., & Qian, L. (2018). Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature, 559(7714), 370–376. View publication.
. One specific paradigm for programming these interactions is via nucleic acid strand displacement – a method by which specific nucleic acid (DNA or RNA) hybrids can exchange strands with each other to change the abundance of specific hybrid species. They offer a powerful paradigm for molecular computation in that they leverage the existing DNA/RNA structure design tools and can be abstracted into high-level programming languages. In addition, RNA has additional advantages in being able to enhance functional properties through expanded non-natural nucleic acid chemistries, and by leveraging the wide range of RNA modifications present especially in eukaryotic systems. Overall, we know a great deal more about the fundamental principles of programming reaction cascades with RNAs than we do for proteins, due to the latter being governed by specific protein interactions that are harder to generalize. We therefore highlight RNA circuit design as its own goal while covering protein-based circuit design in Holistic, integrated design of multi-part genetic systems (i.e., circuits and pathways).

While there is great promise in RNA circuit design, and some progress made in porting the most basic elements of strand displacement into designed RNA regulators of gene expression (toehold switches4Green, A. A., Silver, P. A., Collins, J. J., & Yin, P. (2014). Toehold switches: de-novo-designed regulators of gene expression. Cell, 159(4), 925–939. View publication. and small transcription activating RNAs (STARs)5Chappell, J., Westbrook, A., Verosloff, M., & Lucks, J. B. (2017). Computational design of small transcription activating RNAs for versatile and dynamic gene regulation. Nature Communications, 8(1), 1051. View publication.), the full repertoire of strand displacement capability has so far not been fully ported to living cellular systems. This represents the major challenge of this goal, with the accordingly great opportunity to expand the programmable molecular control over cellular systems through porting strand displacement into cells.

Breakthrough Capabilities & Milestones

Porting nucleic acid strand displacement technology into cellular systems with RNA instantiations.

Porting successes in computationally designed bacterial RNA-based genetic regulators into eukaryotic and mammalian systems.

Footnotes

  1. Qian, L., & Winfree, E. (2011). Scaling up digital circuit computation with DNA strand displacement cascades. Science, 332(6034), 1196–1201. View publication.
    Cherry, K. M., & Qian, L. (2018). Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature, 559(7714), 370–376.View publication.
  2. Seelig, G., Soloveichik, D., Zhang, D. Y., & Winfree, E. (2006). Enzyme-free nucleic acid logic circuits. Science, 314(5805), 1585–1588. View publication.
  3. Qian, L., & Winfree, E. (2011). Scaling up digital circuit computation with DNA strand displacement cascades. Science, 332(6034), 1196–1201.View publication.
    Cherry, K. M., & Qian, L. (2018). Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature, 559(7714), 370–376. View publication.
  4. Green, A. A., Silver, P. A., Collins, J. J., & Yin, P. (2014). Toehold switches: de-novo-designed regulators of gene expression. Cell, 159(4), 925–939. View publication.
  5. Chappell, J., Westbrook, A., Verosloff, M., & Lucks, J. B. (2017). Computational design of small transcription activating RNAs for versatile and dynamic gene regulation. Nature Communications, 8(1), 1051. View publication.
Last updated: June 19, 2019 Back