I am a postdoctoral research associate in the Theoretical Systems Biology group at Imperial College London, working on statistical models for errors and biases within protein interaction network data. I obtained my undergraduate degree in Mathematics and a Masters degree in bioinformatics at the Sciences faculty of Porto University in Portugal. In my Masters thesis I demonstrated the ability of logic programming to elegantly model, query and mine spatial databases. Afterwards I was accepted in a PhD Program in Computational Biology at Instituto Gulbenkian de Ciência (IGC) in Portugal. In the first year of this PhD program I was given an intense and extensive overview of what is being done in this field. After this, I had the opportunity to choose a Lab where to develop my thesis research work. I decided to work with John Pinney and Michael Stumpf on evolution of metabolic networks. During my PhD studies I worked on probabilistic modeling of metabolic network evolution. In order to understand biological systems we need to organize the wealth of interaction data into mathematical network models. These networks describe the sum of metabolic, physical and regulatory interactions in an organism. In this project we developed novel statistical and bioinformatics tools to infer such networks.
Supervisor: John Pinney
Cosupervisor: Michael Stumpf
Laboratory: Centre for Bioinformatics
University: Imperial College of London
Probabilistic modeling of metabolic network evolution. Imperial College London (2013) pp..
Liberal R, Pinney JW. Simple topological properties predict functional misannotations in a metabolic network.. Bioinformatics (2013) 29, i154-61.