Computational approaches are nowadays a full, self-standing branch of chemistry, both for their quantum-based (“ab initio”) accuracy, and for its multiscale extent. In prebiotic chemistry, however, due to the instrinsic complexity of the chemical problems, ab initio atomistic simulations have so far had a limited impact, with the exception of a few relevant studies, including the elucidation of the chemical interactions between biomolecules with surfaces, such as ice and minerals, or the simulation of the effect of the pressure/temperature shock waves induced by meteorite impacts in the early Earth. Surprisingly, even the celebrated Miller experiments, which historically reported on the spontaneous formation of amino-acids from a mixture of simple molecules reacting under an electric discharge, have never been studied at the quantum atomistic level. Here we set the general problem of chemical networks within new topology-based concepts, using search algorithms and social network data analysis. This allows a very efficient definition of reaction coordinates…

In our group at The Institute of Cancer Research, we use massive parallel sequencing to study genomic features of malignant tumours. Among other things, we work on detection of tumour DNA in liquid biopsies, analysis of patients’ human gut microbiomes and identification of tumour neoantigens for cancer immunotherapies. In this talk, I will first provide an introduction to cancer genomics and then I will focus on our in silico studies of cancer resistance mutations’ immunogenicity in the context of cancer vaccines.

The genetic component of the molecular phenotypes has been studied extensively in the last decade. Multiple results show that the donor identity accounts for a significant part of transcriptome variation and identified thousands of cis expression Quantitative Trait Loci (eQTLs). While gene expression is informative for understanding mechanisms of gene regulation, most cellular phenotypes are occuring at the protein level and variation in transcript levels may not be reflected at the protein level and vice versa. Yet, the degree in which the proteome is affected is still poorly understood. To examine the proteome variation and its coordination with gene expression, we generated matched proteomic (TMT Mass Spectrometry), transcriptomic (RNA-seq) and genomic datasets for 202 iPSC lines derived from 151 different donors as part of the Human Induced Pluripotent Stem Cells Initiative (HipSci). Our data revealed significant effects of the donor identity on abundance variation across lines for more than 900 proteins and several protein…

Living entities process information in very different ways than existinghuman-made object as the former excel at having a built-in capabilityfor self-reproduction, self-construction, evolvability, self-organization, scalability, adaptability and robustness. They can store information in DNA or epigenetically, although the former case is more tractable, and hence exploitable, by current technology. The machinery of living cells allows expressing functional biomolecules encoded in DNA. Such biomolecules can further switch the expression of selected genes, progressing asynchronously through successive reaction steps. We propose a strategy for the design of logic gates in livingcells that relies on the expression of exogenous RNA as biomolecules.The RNA has biophysical properties that allow engineering of complex logical devices by predicting the alternative conformations the RNA may adopt when interact with other RNA or small-molecules. Moreover, the devices can be connected once the RNA controls the expression of other RNAs. For this, we developed a methodology based on physical principles to…

Liquid-liquid phase separation (LLPS) of RNA-binding proteins plays a role in the formation of multiple membrane-less organelles involved in RNA metabolism, including stress granules [1,2]. Defects in stress granule homeostasis constitute a cornerstone of Amyotrophic lateral sclerosis (ALS)/ Frontotemporal lobar degeneration (FTLD) pathogenesis. Polar residues (tyrosine, glutamine) have been previously demonstrated to be critical for phase separation of ALS-linked stress granule proteins. We now identify an active role for arginine-rich domains in these phase separations. Moreover, arginine-rich dipeptide repeats (DPRs) derived from C9orf72 hexanucleotide repeat expansions similarly undergo LLPS, and induce phase separation of a large set of proteins involved in RNA and stress granule metabolism [3]. Expression of arginine-rich DPRs in cells induced spontaneous stress granule assembly that required both eIF2α phosphorylation and G3BP. Together with recent reports showing that DPRs affect nucleocytoplasmic transport, our results point at an important role for arginine-rich DPRs in the pathogenesis of C9orf72 ALS/FTLD.  …

The dynamical behavior of cells may be viewed as an emergent property of the underlying biological interaction network. This presentation focuses on the logical modeling of regulatory network and its analysis, allowing insight into the cell functioning. After a description of the logical formalism, we will discuss about the mathematical developments required to analyse the models. Then, biological applications, in the context of cancers, will be presented to illustrate the interest and possible contributions of this modeling approach. We will also discuss about the input of new single cell data in the modelisation.

The last 10 years have seen tremendous advances in our knowledge of genomic instability, which is a hallmark of cancer. We have been involved in a number of local and international efforts to better understand genomic instability across different cancers, which I will briefly describe.A particular focus of this talk will be the analysis and insights from a rapid autopsy study of patients with melanoma that has been undertaken at the Peter MacCallum Cancer Centre in wheich we sequenced about 80 cancers using WES and WGS from 13 patients. This has revealed the dominant mode of evolution in lethal and seems to have surprising clinical and biological implications.I will also describe the computational tools we have had to develop along the way to help make sense of genomic instability.