How do we target genome replication and repair with therapeutics?
In the time it takes you to read this sentence, your body will have produced millions of new cells. Every one of those cells must accurately copy approximately six billion DNA base pairs, and even rare mistakes can have profound consequences, driving genomic instability, cancer, inherited disorders, and the evolution of infectious pathogens.
Our research seeks to uncover the fundamental principles that govern DNA replication and DNA repair. We investigate how cells faithfully duplicate and maintain their genomes, why these systems sometimes fail, and how the resulting vulnerabilities can be exploited to develop better therapies. By understanding the origins and consequences of genomic instability, we aim to reveal new strategies for diagnosing, preventing, and treating disease. The biological systems we study span a broad spectrum of human health and disease, including cancer, malaria, and other parasitic infections. Although these diseases differ enormously in their biology, they share a common challenge: the accurate replication, maintenance, and transmission of genetic information. By studying genome stability across diverse organisms, we seek to uncover both universal biological principles and disease-specific therapeutic opportunities.
Human cancer cells
DNA replication is an important therapeutic target in cancers. We are interested in what cells show defects in replication, why they show these defects, and how these defects affect different parts of the genome.
Malaria parasites
DNA replication are repair are targets for front-line antimalarials, but we have a poor understanding of how replication works in malaria parasites. We want to understand these mechanisms to better target DNA replication with therapies.
We approach these challenges 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. We've found that by spanning these disciplines, we can strengthen our research by learning from each other.
Our computational lab space sits in the heart of historic Cambridge, a five-minute walk from where the structure of DNA was discovered. Our location in the Cambridge Department of Pathology combines our university’s history with access to cutting-edge high-performance computing facilities, clinical expertise, and collaboration with Cambridge’s rich biotechnology sector.
A major focus of the lab is the development of next-generation computational and artificial intelligence methods for analysing genomic data at scale. We create easy-to-use, scalable software that enables researchers to extract biological insights about genome replication and repair from increasingly complex datasets. Alongside this, we develop mathematical models and large-scale simulations that allow us to explore how faulty genome replication gives rise to disease.
We are particularly passionate about helping researchers without prior computational experience develop the quantitative and programming skills needed to develop AI for genomics. Through mentorship, collaborative projects, open-source software, and accessible training, we strive to lower barriers to entry and create an inclusive environment where biologists, clinicians, and quantitative scientists can learn from one another. Our goal is not only to develop new methods, but also to empower the next generation of researchers to use them confidently and creatively.
Check out our ongoing projects to see what we're working on and how you can join us.
AI Engineering
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.
Mathematical modelling
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.