Technological advances permit quantitative measurements of multiple biological characteristics at the genome-wide/system-wide level. Integrating, analysing and understanding these data is necessary to gain novel knowledge. Our research group embraces this data paradigm and develop system approaches to biological question. We work at the interface between molecular biology and computational biology.
We are particularly developing two research programs. The first program aims at understanding the lineage-specific signaling dependencies in cancer with a focus on transcriptional regulators. We are modeling the activity of transcription factor in cancer to stratify tumors and to identify genomic alterations specific of tumor lineage.
A second aspect of our researches focuses on system genetics. We aim at understanding the genetic and molecular mechanisms underlying inter-individual phenotypic differences. This question is central to the problem of personalized medicine. How can we predict based on DNA sequences the treatment that would be efficient at curing a specific patient?
To answer this question, we study model organisms (like budding yeast) to understand the fundamental relation between genotype and molecular phenotypes.
Valenzano DR, Benayoun BA, Singh PP, Zhang E, Etter PD, Hu CK, Clément-Ziza M, Willemsen D, Cui R, Harel I, Machado BE, Yee MC, Sharp SC, Bustamante CD, Beyer A, Johnson EA, Brunet A. (2015) The African Turquoise Killifish Genome Provides Insights into Evolution and Genetic Architecture of Lifespan. Cell 63(6):1539-54.
Clément-Ziza M, Marsellach FX, Codlin S, Papadakis MA, Reinhardt S, Rodriguez-Lopez M, Martin S, Marguerat S, Schmidt A, Lee E, Workman CT, Bähler J, and Beyer A. (2014) Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast. Molecular System Biology, 10,764
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