Pathways and biological systems modelling
Biology was dominated until recently by the reductionist approach, consisting in the thorough study of individual biochemical compounds. However, new technologies allow experimentalists to gather rapidly increasing amounts of data at the scale of the entire cell, and a key challenge for biology is now to integrate and exploit these data in order to advance towards a global understanding of biological functions. The new field of Systems Biology seeks to explain macroscopic phenotypes by the use of chemical and physical models embedding detailed knowledge of the quantities, dynamics and interactions between intracellular compounds. New algorithms and solutions are required for data integration, network analysis, model construction and simulation. In this framework, our research projects encompass on the following topics:
Constraint-based analysis of metabolic networks
The metabolism of biological cells consists in several hundreds of chemical reactions, which build a complex and highly interconnected network. The analysis of metabolic networks by constraint-based methods, such as flux balance analysis and elementary modes, has led to valuable new knowledge and applications in recent years. We are developing new methodologies to integrate these tools with high-throughput experimental data and to extend their use in whole-cell models.
Integrative and dynamical modelling
Biological processes are highly dynamic, as living cells constantly need to adapt to specific functional requirements or changes in their environment. Our projects aim at the construction of dynamic models integrating metabolism with gene regulatory and signalling pathways. We interact with experimental biologists to model pathways of particular interest, while at the same time developing methodologies that will be of general interest to biological systems modelling.
Systems pharmacology and drug metabolism
Although investments by pharmaceutical companies have been growing continuously in the last decades, the number of newly approved drugs has remained almost constant. The traditional approach in drug development generally targets a single gene or gene product. However, many diseases are multifactorial and systemic effects of drug action need to be taken into account. Our work aims to characterise and predict the interactions between drugs and cellular systems by the use of network analysis and the construction of stoichiometric and dynamic models.