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Department of Pathology


I am interested in genomic instability, which is characterised by the rapid accumulation of mutations commonly observed in cancer cells. My particular focus lies in understanding how defective DNA replication and repair mechanisms instigate genomic instability. Having completed a BA in Mathematics at Rutgers University and a PhD in Physics at the University of Oxford, I approach these problems through mathematics and computer science lenses by developing machine learning and high-performance simulation techniques to unravel the underlying causes of DNA replication and repair defects. This approach allows for exploring novel avenues for targeting these defects with therapeutic interventions in human cancer cells and pathogens.

Currently, I am an Assistant Professor of AI in Disease with a joint appointment in the Department of Pathology and the Department of Genetics. I am also a fellow of St. John's College where I am Sub-Director of Studies in Mathematics for Biological Natural Sciences.


Before a cell divides, it must copy (or “replicate”) its genome exactly once, but errors along the way can lead to cancer or other genetic diseases. An important error occurs when DNA replication forks stall. This may happen due to nucleotide shortages from abnormal cell cycle entry or when the fork encounters an obstacle that it cannot pass such as actively transcribing genes, DNA lesions, or difficult-to-replicate sequences. The frequent slowing or stalling of replication forks, termed “replication stress”, can lead to the rapid acquisition of mutations that results in genomic instability, but replication stress also presents an attractive therapeutic target for both human cancer cells and parasites. 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 are a computational biology lab that studies DNA replication and repair by developing high-performance mathematical modelling methods and AI models that analyse large genomic sequencing datasets. We then engineer these methods into scalable, easy-to-use software that is deployed by our lab, our collaborating labs, and a broad user base around the world. While our work is computational, our lab members have a wide range of academic backgrounds including medicine, mathematics, biochemistry, computer science, theoretical physics, and engineering.

We are always interested in enquiries from prospective students and postdocs; candidates are encouraged to visit our lab webpage at for further information.


Key publications: 

Totanes, F.I.G., Gockel, J., Chapman, S.E., Bartfai, R., Boemo, M.A.†, Merrick, C.J.† (2023) A genome-wide map of DNA replication at single-molecule resolution in the malaria parasite Plasmodium falciparum. Nucleic Acids Research.

Aydogan, M.G.*†, Steinacker, T.L.*, Mofatteh, M., Wilmott, Z.M., Zhou, F.Y., Gartenmann, L., Wainman, A., Saurya, S., Novak, Z.A., Wong, S., Goriely, A., Boemo, M.A., Raff, J.W. (2020) A free-running oscillator times and executes centriole biogenesis. Cell 181:1-16.

Boemo, M.A.†, Cardelli, L., Nieduszynski, C.A. (2020) The Beacon Calculus: A formal method for the flexible and concise modelling of biological systems. PLoS Computational Biology 16:e1007651. 

Mueller, C.A.*, Boemo, M.A.*, Spingardi, P., Kessler, B. Kriaucionis, S. Simpson, J.T., Nieduszynski, C.A.† (2019) Capturing the dynamics of genome replication on individual ultra-long nanopore sequencing reads.  Nature Methods 16:429-436. [bioRxivDOI:10.1038/s41592-019-0394-y

Boemo, M.A.†, Byrne, H.M.† (2018) Mathematical modelling of a hypoxia-regulated oncolytic virus delivered by tumour-associated macrophages. Journal of Theoretical Biology 461:102-116. 

Boemo, M.A., Lucas, A.E., Turberfield, A.J.†, Cardelli, L.† (2016) The formal language and design principles of autonomous DNA walker circuits. ACS Synthetic Biology 5:878-884. 

Assistant Professor of AI in Disease
Fellow of St John's College

Contact Details

Takes PhD students
Available for consultancy