Vilar Research Group |
Publications  |
The traditional approach to study the functioning of cells has been remarkably successful at identifying cellular components and their interactions. Current automated technologies have brought the cartoon-like representations of cellular processes to exponentially growing webs of nodes and links that seem as close to completion as ever. The complexity of the emerging picture, however, makes it clear that all this information by itself is not enough to truly understand biological processes such as cancer. In order to piece back together all the genetic, biochemical, molecular, and structural information into a physiologically relevant description of the organism one needs "constructive" methods. Computational modeling has emerged as a promising tool for transforming molecular detail into higher order, more integrated understanding of complex biological behavior.
We use computational and mathematical modeling to study biological networks relevant to cancer. We are interested not only in the effects of the molecular details in the interactions between cellular components but also in the resulting cellular behavior and its integration into the physiological context of an organism. We study how mutations affect the molecular properties of the cellular components, how the mutated components affect different pathways, and how these modified pathways confer cell growth advantages during tumor progression and metastasis. Having a global view of all these processes and their propagation through all relevant levels of biological organization is crucial to identify and characterize key control elements of the system.
We are currently working on:- Gene regulation (RXR and other nuclear hormone receptors)
- Signal transduction networks (EGF and TGF-beta pathways)
- Control of cell growth and death (Bcl-2/Bax in metabolism and apoptosis)
We are also developing new computational approaches to determine, capture, and use the relevant biological information. We are especially interested in stochastic analyses and in multilevel and multiscale methods.
