Cell-free synthetic biology for affordable, on-demand diagnostics

Detection of biomarkers, such as nucleic acids, performs critical roles in managing infectious disease outbreaks, point-of-care testing, and public health monitoring. However, many diseases and public health problems suffer from a lack of affordable, portable tests that can be used to sensitively detect nucleic acids and respond in a rapid manner. Current methods to nucleic acid testing are too expensive, slow, and complex to be routinely used outside of specialized lab settings. New diagnostic tools are needed that can work in resource-limited settings to help guide prompt treatment decisions, prevent spread of infectious diseases, and inform public health decisions. Cell-free synthetic biology has shown promise as a portable, affordable technology to detect biomolecules like nucleic acids. In this thesis, I present several advancements to cell-free synthetic biology diagnostics that enable new application areas. First, I present a paper-based cell-free synthetic biology platform using RNA toehold switch sensors to detect RNAs from human gut microbiome. We showed that this method could quantify bacterial and human RNA transcripts comparably to gold standard methods while reducing time and cost. Next, I used similar cell-free detection technology to create a set of fruit DNA-sensing demonstrations that can be used in high school biology classrooms. I then sought to engineer biomolecular circuits that can process multiple sensor inputs to reduce cost, improve specificity, and build classifier circuits. Finally, I present work to develop and use clustered regularly interspaced short palindromic repeats (CRISPR) enzyme-based diagnostics to achieve attomolar sensitivity and single-nucleotide mismatch specificity. Together, these projects demonstrate a set of advancements in cell-free synthetic biology diagnostics toward filling the gap of nucleic acid detection technologies that are low-cost, portable, sensitive, and easy to use.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Biological Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Dy, Aaron J.(Aaron James)

Advisor dc:contributor.advisor

• James J. Collins and Domitilla Del Vecchio.

• Biological Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124183

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124183