Research is centred on modelling biological networks. In particular, we focus on two main areas of research:

(i) Numerical simulation and mathematical modelling of cancer.

Both development of computational tools and experiment-oriented simulations are carried out to understand and predict the emergence of cancer resistance to drugs. We mainly focus on the metastasis promoter protein S100A4 as well as the ROS1 and ALK tyrosine kinases.

Our modelling framework relies on principles of physical biochemistry and enzyme kinetics to represent activation/inhibition networks mathematically. The simplifying assumptions in our models are aimed at facilitating integration and delivery of experimentally accessible quantities (i.e., being close to experimental reality) on one hand, and on the other hand to take into consideration complexity and tractability according to state-of-the-art computational tools. 

Control theory and sensitivity analysis are used to identify control points in the biological networks that underlie the resistance mechanisms of a tumour to therapies taking into account the heterogeneity of tumour cells. Our methodology facilitates access to high-performance computing (HPC) for experimental biologists. In silico studies of metastasis allow to optimize the information retrieved experimentally and to improve the design of experiments also supporting novel directions for the development of alternatives to animal testing.

(ii) Modelling of (mixed) microbial communities: stress adaptation and gene regulation of extremophile microorganisms in biotechnological applications.

In this research area we perform wet lab experiments using next-generation sequencing (NGS) and analyse data with standard bioinformatics tools as well as with some of our in-house developed software. Due to the general assumptions of the modelling framework described above, we apply similar methodology to simulate and analyse data from both areas (i) & (ii).
After extracting nucleic acids and proteins from microorganisms living under conditions of interest, we retrieve raw information about gene regulatory processes using NGS that we reverse-engineer into biological networks. This allows us to access information about microbial life in extreme conditions, on gene regulation and mechanisms of adaptation of individual and mixed microbial communities. We subsequently analyse network models by means of simulation aiming at optimizing key processes for biotechnological applications.

Research is in association with the following groups:


Article in journal (Refereed)

Conference paper (Refereed)

Doctoral thesis, monograph (Other academic)