Using machine learning to improve DNA Replication via Polymerase Chain Reaction (PCR) performance

Project description

Many biology-based projects or professions require the examination of areas of DNA. In Forensic Biology the use of DNA leads to the generation of DNA profiles. The steps to generate a DNA profile are: extraction, quantification, PCR and electrophoresis. The focus of this project is the process of PCR (copying the fragments / section of DNA of interest) has three main phases: denaturing, annealing/priming and extension that are repeated multiple times. The temperatures, durations and repetitions of these stages vary slightly depending on the regions of DNA being targeted, but in general this process has changed very little since its invention in the 1980s. Preliminary work has identified approaches that improve the efficiency of DNA replication under traditionally challenging conditions. This work has two main angles for future development: 1) Modification and/or redevelopment of software to better control an existing quantifiable PCR (qPCR) machine. Best for someone with a software focus 2) Redesign of a PCR system to add sensors and feedback control loops to turn it in a smart qPCR machine, hence creating a potential new product. Best for someone with a hardware focus The successful completion of this project will have wide reaching implications and applications in forensic biology, medicine and beyond.

Co-supervisors

Professor Adrian Linacre Dr Richard Leibbrandt

Further information

Useful Publications: 1)    McDonald, C, Taylor, D, Masawi, GM, Khan, AKA, Leibbrandt, R, Linacre, A & Brinkworth, RSA (2024) Developing a Machine-Learning ‘Smart’ PCR Thermocycler, Part 1: Construction of a Theoretical Framework Genes doi: 10.3390/genes15091196 2)    McDonald, C, Taylor, D, Brinkworth, RSA & Linacre, A (2024) Developing a Machine-Learning ‘Smart’ PCR Thermocycler, Part 2: Putting the Theoretical Framework into Practice Genes doi: 10.3390/genes15091199

Supervisors research focus

My goal is to bring robotics out of the lab and into the real-world. To stop having to adapt our environments to suit artificial systems and build artificial systems with the ability to adapt to different environments. I will continue to contribute to the technological and scientific progress of Australia both directly and by helping to guide future generations of entrepreneurs and engineering professionals. My research focus is biologically inspired sensors and signal processing. Data is everywhere; information is the lifeblood of the modern age, but the refinement of raw data into usable information is a complex task. Traditional approaches have tended to be linear and time-invariant, or at least deviate minimally from this paradigm. Adaptation is the hallmark of biological systems, which, by definition, is non-linear and time varying. By studying how biology works, and putting that processing into action, we can open up a new world of possibilities in many areas of technology.

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