Dr Michael Boemo

University of Cambridge

University departments
Department of Pathology

Position: Research Associate
Personal home page: https://www.boemogroup.org/
Email:   Public email address:  mb915@cam.ac.uk

PubMed journal articles - click here

Dr Michael Boemo is pleased to consider applications from prospective PhD students.

Research description

My group is interested in developing new computational methods to study how dysregulation or faults in cell cycle mechanisms lead to genome instability and cancer. We use and develop cutting-edge methodologies from machine learning, bioinformatics, software engineering, and computational modelling to realise this goal. The following describes one example of our ongoing research.

Each time one of our cells divide, 6 billion base pairs of DNA have to be replicated quickly and accurately. Any unresolved errors in this process can have severe consequences for genome integrity, potentially resulting in cancer and other genetic diseases. How DNA replication initiates and coordinates to complete replication and maintain genome integrity is of central importance for understanding the onset of cancer. To address this issue, we have developed machine learning software called DNAscent that infers replication initiation and fork movement by detecting the modified base BrdU in ultra-long Oxford Nanopore reads. When BrdU is pulsed into replicating DNA, the DNAscent software is able to determine how and where replication forks were moving during the pulse. We partner with a range of experimental collaborators to address central questions pertaining to the regulation, performance, and behaviour DNA replication to understand how it affects genome integrity.

Research Programme
Cell and Molecular Biology
Secondary Programme
Not applicable
Methods and technologies
Bioinformatics
Computational modelling
DNA sequencing
Genomics
Keywords

Computational Biology
Machine Learning
Bioinformatics
Systems Biology
Mathematical Modelling
DNA Replication
Genome Stability


Key publications

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.

Aydogan, M.G.*†, Steinacker, T.L.*, Mofatteh, M., Gartenmann, L., Wainman, A., Saurya, S., Conduit, P.T., Zhou, F.Y., Boemo, M.A.†, Raff, J.W.† (2019) A free-running oscillator times and executes centriole biogenesis. [bioRxiv]
​​
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.

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.

Boemo, M.A.†, Turberfield, A.J., Cardelli, L. (2015) Automated design and verification of localized DNA computation circuits. In: Phillips, A., Yin, P. (eds.) DNA 2015. LNCS, vol. 9211, p. 1-13. Springer, Heidelberg.

Wagh, K.*, Bhatia, A.*, Alexe, G., Reddy, A., Ravikumar, V., Seiler, M., Boemo, M., Yao, M., Cronk, L., Naqvi, A., Ganesan, S., Levine, A.J.†, Bhanot, G.† (2012) Lactase persistence and lipid pathway selection in the Maasai. PLoS ONE 7: e44751.