Computational systems biology

Head of the team

Prof. Ovidiu Radulescu

Research interests

We are  developing mathematical approaches for learning and analysing  mechanistic models of  biological systems at various levels of  organisation, with a focus on the host-pathogen interactions in several  infectious diseases (malaria, HIV) and on cancer.

Mathematical  modelling of biological systems, in their full details, is a daunting  challenge. In order to cope realistically with the dynamics of molecular  pathways and gene networks in the cell, bottom-up models use thousands  of variables. Furthermore, in models of tissues, populations of cells  with complex single cell dynamics must be described collectively within a  spatially heterogeneous framework. In order to cope with this  complexity, we develop rigorous and automated methods for generating  hierarchies of simplified models that keep, at each scale, only  essential processes and components. Our modelling approaches provide  solutions to many problems in fundamental biology and medicine.

We  also develop novel AI methods for extracting information from  biological data. Our vision in this field is to combine data driven  “black box” models with knowledge driven “white box” models within  hybrid AI approaches.

For more information, check out the LPHI Computational Systems Biology website.