Synthetic biology-driven biosensors leverage engineered or chemically modified biomolecules to detect target analytes e.g., disease biomarkers. Perhaps the largest class of synthetic biology-driven biosensors are synthetic gene circuits (SGCs), i.e., sequences of DNA that can respond to inputs (e.g., target DNA/RNA) to produce a detectable output signal (e.g., a fluorescent protein). The major advantage of SGCs is their programmability; they can be rapidly engineered to respond to new disease inputs and even be programmed to engage in logic-gated sensing (e.g., AND/OR/IF). This makes them excellent tools for multiplexed sensing, i.e., analysing multiple disease biomarkers within a single assay. Unfortunately, contemporary SGCs suffer from several critical drawbacks, including poor stability in common sample types (e.g., blood, saliva) and high background signals. Furthermore, the most common classes of SGCs respond only to RNA biomarkers, limiting their potential disease scope.
Our group recently developed a new class of synthetic gene circuit that responds to DNA inputs, rather than RNA. These circuits are stable in various sample media and can provide an excellent signal-to-noise ratio (>1000). The aim of this project is to optimise the synthesis of this novel class of SGC and then apply them toward the detection of clinically relevant bacterial pathogens, e.g., methicillin-resistant Staphylococcus aureus (MRSA). The work will be split into three aims.
1) Optimise the synthesis of the SGCs, e.g., improving yields and decreasing synthesis times.
2) Develop a small library of SGCs (15 – 20) and characterise their capacity to express proteins in response to synthetic (randomised) triggers. We will focus on checking cross-reactivity and orthogonality between circuits and triggers.
3) Develop orthogonal gene circuits that can differentiate Staphylococcus aureus (SA) and methicillin-resistant Staphylococcus aureus (MRSA) in a single assay.
Contact person: Dr Daniel Richards, email@example.com, HCI F111