Laboratoire de Physique Théorique
Toulouse - UMR 5152

Several decades of molecular biology research have delivered a wealth of detailed descriptions of molecular interactions in normal and tumour cells. This knowledge has been functionally organized and assembled into dedicated biological pathways resources that serve as an invaluable tool, not only for structuring the information about molecular interactions, but also for making it available for biological, clinical and computational studies. With the advent of high-throughput molecular profiling of tumours, close to complete molecular catalogues of mutations, gene expression and epigenetic modifications are available and require adequate interpretation. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular profiles of tumours. Mathematically speaking, this challenge requires developments of scalable mathematical and computational methods combining biological networks represented as graphs of various kinds and molecular profiles represented as finite sets of vectors in multidimensional space. I will review the major ideas used in this field for molecular data analysis and visualization, such as detecting subgraphs possessing certain properties in terms of data patterns and graph-based Fourier analysis of molecular data. I will focus on the approaches that we’ve developed in our group for representing biological networks using Google Maps (Atlas of Cancer Signaling Networks, http://acsn.curie.fr), methods for creating data-driven large network layouts (such as DeDaL, http://bioinfo.curie.fr/projects/dedal ) and methods for data smoothing for the tasks of machine learning (such as classification) using biological networks.

References :

Czerwinska U, Calzone L, Barillot E, Zinovyev A. DeDaL : Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts. 2015. BMC Syst Biol. 14 ;9:46.

Kuperstein I, Bonnet E, Nguyen HA, Cohen D, Viara E, Grieco L, Fourquet S, Calzone L, Russo C, Kondratova M, Dutreix M, Barillot E, Zinovyev A. Atlas of Cancer Signalling Network : a systems biology resource for integrative analysis of cancer data with Google Maps. 2015. Oncogenesis 4:e160.

Kuperstein I, Grieco L, Cohen DP, Thieffry D, Zinovyev A, Barillot E. The shortest path is not the one you know : application of biological network resources in precision oncology research. 2015. Mutagenesis 30(2):191-204.

Rapaport F., Zinovyev A., Dutreix M., Barillot E., Vert J.-P. Classification of microarray data using gene networks. 2007. BMC Bioinformatics 8:35.