Rosalind Franklin’s pioneering work to establish the atomic structure of DNA has underpinned much of our understanding of the ‘molecule of life’. The compaction of genomic DNA into the nucleus results in significant topological stress and the formation of coiled, twisted and knotted DNA structures which impact cell viability, with ramifications from DNA replication to the activity of therapeutic agents in cancer and infection. The challenge of understanding how these complex DNA structures influence DNA processing has been fundamentally limited by the tools available.
High-resolution atomic force microscopy (AFM) is unique in its ability to provide quantitative information on DNA structure, function and kinetics in liquid with nanometre resolution without labelling or averaging [1], however the analysis of these datasets has until now relied on the eye of an experienced microscopist [2]. Despite the increasing size of datasets generated by AFM, automated analysis and/or machine learning techniques are not routinely applied.
Machine learning has driven step changes in our understanding of biological phenomena (e.g. AlphaFold). Deep learning using artificial neural networks has been applied to datasets produced with adjacent microscopies (notably cryo-EM in its resolution revolution), to solve previously inaccessible biological problems. Gaussian processes (GPs) are another important machine learning technique, useful in situations where data is less abundant and more is known about the behaviour of the system being modelled (e.g. DNA mechanics). We propose to use a combination of these and similar techniques, adapting and improving them in analysis of complex bio-AFM datasets.
This interdisciplinary project will apply cutting edge machine learning algorithms developed in the Machine Learning group at TUoS, to high-resolution AFM datasets obtained in the Henry Royce Nanocharacterisation Laboratory to derive quantitative descriptions of DNA structure, mechanics and topology. This will automate the detection of molecular interactions in complex DNA to improve our understanding of how complexity in DNA affects its interactions. This knowledge can be harnessed in the fight against disease as we develop future therapies.
You will be supervised by Dr Pyne from the department of Materials Science and Engineering (www.pyne-lab.uk), and Dr Álvarez from the Machine Learning group in the department of Computer Science (https://maalvarezl.github.io/). We welcome applicants from a diverse range of backgrounds across engineering and the physical sciences. Interested applicants should contact Dr Pyne & Dr Álvarez to discuss the project further ([Email Address Removed], [Email Address Removed]).
References:
[1] Pyne, A.L.B.*, et al. Base-pair resolution analysis of the effect of supercoiling on DNA flexibility and recognition. Nat Comms – in press. https://www.nature.com/articles/s41467-021-21243-y (2021)
[2] Beton, J.G et al. TopoStats - an automated tracing program for AFM images. Methods – in press, https://doi.org/10.1016/j.ymeth.2021.01.008 (2021)
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