I am currently a postdoctoral cross disciplinary fellow (XDF) at the MRC Institute of Genetics and Molecular Medicine. With a background in pure mathematics (completed PhD in Fourier Analysis in 2018 at the Universidad Autonoma de Madrid) and Machine Learning experience, I am interested in addressing biomedical questions from an analytical perspective.
Age-related Clonal Haematopoiesis
As we age we naturally acquire somatic mutations in haematopoietic stem cells (HSCs) that can disrupt the balanced production of blood cells and lead to the formation of large genetic clones. This condition termed 'Age-related clonal haematopoiesis' (ARCH) is considered to be a pre-malignant state, associated with a 10-fold increase in the later onset of haematological malignancies. However, while current sequencing technologies allow to easily detect the presence of large clones, detecting them before they grow disproportionately large and diagnosing their malignant potential is still an open challenge.
Early detection of ARCH
Accurately predicting the proliferation potential of genetic clones before they grow disproportionately large will allow for early detection and diagnosis of ARCH. Through mathematical models of cell populations, I am interested in inferring the proliferation speed or fitness of genetic clones to accurately detect those with a harmful proliferating potential. Some of the questions that drive my research are:
- How can we accurately measure the proliferation speed of a genetic clone?
- Can we distinguish between true clonal growth from stochastic fluctuations of cell populations?
- Can we determine a gene-intrinsic fitness to facilitate early detection of harmful variants?
- How many active haematopoietic stem cells do we have?
From clonal proliferation to malignancies
Mutations in genes DNMT3a, TET2 and ASXL1 account for the vast majority of mutation driven clonal haematopoiesis in humans. However, it is still not well understood through which mechanisms can these common mutations of ARCH facilitate the onset of haematological malignancies. Given that these genes are epigenetic regulators, understanding methylation and transcriptional changes caused by these mutations will be paramount to successfully bridg this gap. Using Machine Learning techinques I aim to uncover methylation patterns from longitudinal studies of ARCH and link their time evolution with transcriptional changes at the single cell level.
Past research interests
I have also collaborated and still work in projects involving: - Developing predictors of homologous recombination deficiency for high grade serous ovarian cancer. - Uncovering differentially expressed genes in the tumour stroma of invasive lobular breast cancer using deconvolution algorithms.
- Tamir Chandra (MRC Institute of Genetics and Molecular Medicine -University of Edinburgh)
- Linus Scumacher (Center for Regenerative Medicine - University of Edinburgh)
- Kristina Kirschner (Institute of Cancer Sciences - University of Glasgow)
- Arno Onken (School of Informatics - University of Edinburgh)
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