Research Overview

Life is orchestrated by programmable biomolecules – DNA, RNA, and proteins – that execute complex self-assembly and disassembly processes to grow, regulate, and repair organisms. Inspired by these biological proofs of principle, we are working to establish principles, mechanisms, and algorithms for programming dynamic nucleic acid function, and to apply these principles to engineer programmable molecular technologies that overcome longstanding challenges to biological research and medicine.

Working at intersections of:

  • dynamic nucleic acid nanotechnology

  • synthetic biology

  • molecular programming

  • computational mathematics

  • chemical engineering

  • biological engineering

  • bioimaging technologies

  • translational medicine

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Programmable Conditional Regulators

Engineering small conditional RNAs (scRNAs) for cell-selective spatiotemporal control of regulation in living organisms.  

Examples: conditional guide RNAs (cgRNAs) for cell-selective control of CRISPR/Cas in bacteria and mammalian cells (Hanewich-Hollatz et al., 2019); high-performance allosteric cgRNAs in mammalian cells (Hochrein et al., 2021); conditional Dicer substrate formation in human cell lysate, full-length mRNA detection (Hochrein et al., 2018)

Imaging & Diagnostics

Developing dynamic nucleic acid nanotechnologies for multiplexed quantitative imaging of the programmable molecules of life (DNA, RNA, proteins) and for rapid at-home testing for SARS-CoV-2 and other pathogens.

 

Examples: HCR imaging for multiplexed, quantitative, high-resolution RNA imaging with automatic background suppression throughout the protocol (Choi et al., 2018); qHCR imaging: analog mRNA relative quantitation with subcellular resolution in an anatomical context (Trivedi et al., 2018); dHCR imaging: digital mRNA absolute quantitation with single-molecule resolution in an anatomical context (Shah et al., 2016); simultaneous multiplexed, quantitative, high-resolution protein and RNA imaging (Schwarzkopf et al., 2021); HCR lateral flow assays for amplified, instrument-free, at-home SARS-CoV-2 testing (Schulte et al., 2022)

 

Resource: Molecular Technologies

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Computational Algorithms

Developing physically sound, mathematically rigorous, computationally efficient algorithms for analysis and design of nucleic acid structures, devices, and systems.

Examples: all-new NUPACK cloud (Fornace et al., 2022); a unified dynamic programming framework for nucleic acid analysis (Fornace et al., 2020); analysis of complex and test tube ensembles (Dirks et al., 2007); complex design, complex ensemble defect, 4/3 optimality bound (Zadeh et al., 2011); test tube design, test tube ensemble defect, hierarchical ensemble decomposition (Wolfe et al., 2015); multi-tube design, reaction pathway engineering, sequence constraints (Wolfe et al., 2017)

Resource: NUPACK 

Molecular Choreography

Establishing principles and mechanisms for engineering small conditional DNAs and RNAs (scDNAs and scRNAs) that interact and change conformation via prescribed hybridization cascades in vitro, in situ, and in vivo. 

Examples: conditional self-assembly, hybridization chain reaction (HCR) (Dirks & Pierce 2004); pathway-controlled self-assembly and disassembly, catalytic hairpin assembly (CHA) (Yin at al., 2008); exquisite sequence selectivity with scavengers (Sternberg & Pierce, 2014); dynamic RNA nanotechnology, diverse modes of RNA assembly and disassembly, shape and sequence transduction with small conditional RNAs (scRNAs) (Hochrein et al., 2013)

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