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Applications of Dynamic Nucleic Acid Nanotechnology in Closed-Loop Genetic Circuits and Detection of Viral Pathogens


Huang, Jining (2023) Applications of Dynamic Nucleic Acid Nanotechnology in Closed-Loop Genetic Circuits and Detection of Viral Pathogens. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/54tw-ym95.


Nucleic acid nanotechnologies have provided a platform where biologically relevant molecules can be engineered to perform programmable functions. Relative to proteins, complex nucleic acid-based systems can be designed more readily due to the countable nature of base-pairing interactions and readily available physical models. These features of nucleic acids enable us to design novel interaction pathways and functions by providing well-behaved molecular mechanisms. Two examples of these mechanisms are the conditional guide RNA (cgRNA) and the hybridization chain reaction (HCR). A cgRNA is a conditional programmable regulator where an expressed RNA trigger can conditionally turn on or off transcriptional regulation. HCR is a molecular mechanism for in vitro and in situ amplification of signals to spatially identify proteins, RNA, or DNA in a sample. This thesis will first demonstrate the use of these nucleic acid molecular mechanisms in closed-loop genetic circuits and infectious disease testing using cgRNAs and HCR, respectively, then provide updated tools for the nucleic acid design community to exploit the programmable nature of nucleic acids.

We begin by demonstrating the use of conditional programmable cgRNAs in closed-loop genetic circuits. Synthetic genetic circuits allow scientists to engineer arbitrary molecular interactions in living organisms. Feedback circuits in particular are recurrently found in nature and enable useful functionalities. However, protein components of genetic circuits cannot be designed scalably, are often mined from preexisting genomes, and present difficulties in being biologically orthogonal to themselves or the host organism. We are motivated to address these limitations by using orthogonal nucleic acid circuits created de novo. One potential component of these circuits are conditional guide RNAs (cgRNAs). cgRNAs are switchable transcriptional regulators, and this allows gene expression to be modulated through the expression of a small RNA trigger. Here we assess cgRNAs as a component for feedback genetic circuits. As an initial demonstration of cgRNA synthetic circuits, we built and validated a simple threshold circuit and demonstrated its orthogonality and scalability by showing independent circuit functions of two switches in a single cell. We also created a larger toggle switch that is made from the same components as the previous switches. These experiments show the orthogonality and feedback capabilities of cgRNAs will position them as a composable component for scalable synthetic biology.

We then used the hybridization chain reaction mechanism to develop an adaptable and sensitive test for the detection of SARS-CoV-2. The lateral flow assay format enables rapid, instrument-free, at-home testing for SARS-CoV-2. Due to the absence of signal amplification, this simplicity comes at a cost in sensitivity. Here, we enhance sensitivity by developing an amplified lateral flow assay that incorporates isothermal, enzyme-free signal amplification based on the mechanism of hybridization chain reaction (HCR). The simplicity of the user experience after the test begins is maintained by using a disposable 3-channel lateral flow device to automatically deliver reagents to the test region in three successive stages without user interaction. Prior to starting the test, a 15-minute heat step is required. Detecting gamma-irradiated SARS-CoV-2 virions in an extraction buffer, the current amplified HCR lateral flow assay achieves a limit of detection of 200 copies/µL using nucleic acid probes to target the SARS-CoV-2 RNA genome. By comparison, five commercial unamplified lateral flow assays that use proprietary antibodies to target the viral nucleocapsid protein exhibit limits of detection of 500 copies/µL, 1000 copies/µL, 2000 copies/µL, 2000 copies/µL, and 20,000 copies/µL. By swapping out nucleic acid probes to target different pathogens, amplified HCR lateral flow assays offer a platform for adaptable and sensitive at-home testing for emergent diseases.

Components for the previous two projects are designed and analyzed with NUPACK. NUPACK is a growing software suite for the analysis and design of nucleic acid structures, devices, and systems serving the needs of researchers in the fields of nucleic acid nanotechnology, molecular programming, synthetic biology, and across the life sciences. NUPACK algorithms are unique in treating complex and test tube ensembles containing arbitrary numbers of interacting strand species, providing crucial tools for capturing concentration effects essential to analyzing and designing the intermolecular interactions that are a hallmark of these fields. The all-new NUPACK web app ( has been re-architected for the cloud, leveraging a cluster that scales dynamically in response to user demand to enable rapid job submission and result inspection even at times of peak user demand. The web app exploits the all-new NUPACK 4 scientific code base as its backend, offering enhanced physical models (coaxial and dangle stacking sub-ensembles), dramatic speedups (20-120× for test tube analysis), and increased scalability for large complexes.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:DNA; RNA; dynamic RNA nanotechnology; CRISPR; genetic feedback circuits; synthetic genetic circuits; synthetic biology; small conditional RNAs; lateral flow assay; hybridization chain reaction (HCR); rapid antigen test; SARS-CoV-2 RNA genome; secondary structure; base-pairing; hybridization; complex ensemble; test tube ensemble; multi-tube ensemble; equilibrium; concentration; ensemble defect; analysis; design; reaction pathway engineering
Degree Grantor:California Institute of Technology
Division:Biology and Biological Engineering
Major Option:Bioengineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Pierce, Niles A.
Thesis Committee:
  • Winfree, Erik (chair)
  • Murray, Richard M.
  • Elowitz, Michael B.
  • Pierce, Niles A.
Defense Date:2 December 2022
Non-Caltech Author Email:jiningh (AT)
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)HR0011-17-2-0008
Center for Environmental Microbiology InteractionsUNSPECIFIED
Beckman Institute (Programmable Molecular Technology Center)UNSPECIFIED
Shurl and Kay Curci FoundationUNSPECIFIED
Richard N. Merkin Institute for Translational ResearchUNSPECIFIED
National Aeronautics and Space AdministrationNNX16AO69A
National Institutes of Health (NIH)R01 EB006192
National Science FoundationNSF-OAC-1835414
National Science FoundationNSF-CHE-1643606
National Science FoundationNSF-CCF-1317694
National Science FoundationNSF-ACI-1548562
National Institutes of Health (NIH)T32 GM007616
Amazon Web ServicesUNSPECIFIED
Record Number:CaltechTHESIS:01172023-195222304
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Chapter 3. adapted for Chapter 4.
Huang, Jining0000-0002-3798-4790
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15086
Deposited By: Jining Huang
Deposited On:20 Jan 2023 18:16
Last Modified:08 Nov 2023 00:43

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