Metabolism

Tumour Microenvironment

Artificial Intelligence and Machine Learning

Francesca is a computational biologist and mathematician whose research focuses on system medicine approaches that help us understand cancer as a complex system.

Key to this work is the development and application of advanced statistical and mathematical modelling approaches,  and machine learning of large multi-omic and functional datasets.

Francesca is funded by Cancer Reseach UK and the European Research Council to investigate how cancer cells re-wire their molecular networks to adapt to a heterogeneous and dynamic tumour microenvironment.

Her lab develops multi-omic cancer classifiers and executable cancer models that enable in-silico experimentation to identify likely resistance mechanisms, predict effective drug combinations, and identify strategies for cancers that are currently incurable.

After a Masters Degree in Theoretical Physics from the University of Turin, Francesca completed her PhD in Physics and Mathematical Modelling at the Institute of Cancer Research, University of London. She then undertook a postdoctoral research fellowship in Mathematical Modelling and Biostatistics at the Gray Cancer Institute, London, before joining the Medical Sciences Division, University of Oxford, first as a postdoctoral research fellow in Bioinformatics / Biostatistics, then as Group Leader of Computational Biology and Integrative Genomics.

In addition to her research work, she teaches at national and international Masters Courses and Advanced Schools, is a member of national and international panels, including REF-2021, and acts as bioinformatics and biostatistics advisor for genomics clinical research studies.

She authored over 130 publications, several in high impact journals, and she is the recipient of a Cancer Research UK programme and a European Research Council consolidator award.

Group Members