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How does DNA replication and repair shape genomes?

In the time it takes you to read this sentence, your body will have finished growing about 5 million new cells. It is critical that each of them copied, or replicated, the ~6 billion base pairs in your genome correctly, as errors along the way can cause genomic instability that leads to cancer and other genetic diseases. The purpose of our research is to determine how cells replicate and repair their DNA, the causes and consequences of any errors in DNA replication and repair, and how we can best exploit these errors with therapies.

We're working in...


Healthy human cells

Errors in DNA replication or repair can be a key driver of oncogenesis, or cancer onset. We are using a variety of model systems to track the steps from DNA replication stress, to genomic instability, to cancer.

We answer these questions as biologists that develop new computational and mathematical methods. Our lab is comprised of scientists from a broad range of fields, including mathematicians, biologists, physicists, engineers, and computer scientists. Meet our multidisciplinary, international team:

Our method development focuses on scalable, easy-to-use AI software to analyse genomics data as well as simulation methods for biological systems. We have a particular focus on high-performance computing and the analytics of big genomic data sets. Have a look at some ongoing projects below to see what we're working on.

Molecules Bio

We track the movement of DNA polymerases using AI software called DNAscent. Base analogues are pulsed into S-phase cells where they are incorporated into nascent DNA by replication forks, leaving a "footprint" of fork movement on single molecules. When these molecules are sequenced on the Oxford Nanopore platform, the base analogues create a subtle signal motif that our software can detect. We use this software to map the speed and stress of replication forks across the genomes of human cancer cells and parasites.


Artificial Intelligence

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Abstract Wavy Lines

We often have measurements of different attributes of a system such as replication fork speed, the location and efficiencies of replication origins, and the time it takes an organism to complete genome replication. But do all of these measurements add up? Can we predict new behaviour from these measurements, or pinpoint important information we might be missing? This is where mathematical modelling and simulation can help, and we employ a range of modelling techniques while also developing some of our own.


Mathematical Modelling

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