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Combining computational biology, computational chemistry, and machine learning techniques with biological big data to unravel the higher genomic code of life.

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Our research targets genomics through the development of highly quantitative methods for describing the structure and dynamics of (epi)genome, gene regulatory pathways, involved macromolecules and their interaction networks. We are interested in combining computational biology, computational chemistry (QM, MD), advanced machine learning and experimental biophysical and sequencing-based big data to reach a new level of precision in structural systems biology at all (genome, transcriptome and proteome) levels. We try to make computational biology maximally independent from empirical data, basing our models and predictions on mainly genomic sequences. We strive to achieve this by employing advanced machine learning (supervised and unsupervised) methodologies, coupled with the engineering of physics and structure “aware” features from those sequences. We apply our "ab intio" modelling approaches to better understand gene regulation, mutations, differential DNA damage and repair, and to spot driver DNA alterations in multigenic diseases (such as cancer, autism, cardiomyopathies). Besides the direct benefits, our approach also lets us understand part of the biology that we cannot predict, i.e. the reminder - cell-specific factors not tightly inter-linked to our genomic blueprint. This helps us better characterise the genome-invariant factors involved in cell differentiation. We heavily work on the full automatisation of the workflows for generating our models, creating a tailored AI platform suitable for automatic rule discovery and model development from biological sequence data.

Group members: We are happy to announce the growth of our group with two outstanding graduate students (William Biggs and Liezel Tamon, profiles coming soon) joining us since October 2018 to pursue a DPhil degree.

Past members: Claudia Feng (MPhil student, Cambridge University) and Nicholas Johnson (Visiting student, Princeton University).

Enquiries from prospective students and post-doctoral researchers are welcome.

Our team

Selected publications

Miscellaneous photos from the group

Group Photos