The goal of our group is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to novel therapeutics. This deregulation in how cell process and react to extracellular information is a hallmark of multiple pathological conditions. Our main application is cancer, but we also work on metabolic and auto-immune diseases.
Our research is hypothesis-driven and tailored towards producing mathematical models that integrate diverse data sources. Because of this, we collaborate closely with experimental groups. A key emphasis of our work is to build models that are both mechanistic (to provide understanding) and predictive (to generate novel hypotheses). To build these models, we combine the existing knowledge of the underlying biochemical processes with functional signalling data. In parallel, we study drugs’ modes of action by analysing genomic and phenotypic data collected in large-scale drug screenings. We then strive to combine this information with our prior knowledge of the underlying pathways to ultimately build integrated mechanistic models. Our premise is that these will have enhanced ability to discern the mode of action of existing therapies and provide avenues for the development of new drugs.
Productive integration of data and computation requires an effective workflow that pulls together all the steps that link experiments to mathematical models and analysis. We therefore strive to develop tools that facilitate this process and incorporate public standards. We are also involved in a community effort to advance the inference of mathematical models of cellular networks: DREAM (Dialogue for Reverse Engineering Assessments and Methods).
With these methods we hope to address questions such as:
- What are the origins of the profound differences in signal transduction between healthy and diseased cells and in particular, in the context of cancer, between normal and transformed cells?
- What are the differences in signal transduction among cancer types, and from patient to patient? Can we use these differences to predict disease progression?
- Do these differences reveal valuable targets for drug development? Can we study the side effects of drugs using these models?