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.

Uncovering hidden data structures from large scale experiments is a common problem across many areas of biology from the analysis of sequencing data to the recording of neuronal populations. Standard methods of dimensionality reduction and clustering based on abstract data representations are often insufficient to capture biological complexity and are difficult to validate with statistical confidence. We will discuss model-based approaches to data classification in three biological contexts: deregulation patterns in RNA sequencing data, analysis of neuronal subtypes and the detection of synchronous neuronal populations. In all these contexts Bayesian inference can be used to support data classification with rigorous statistical evidence.

Sperm-dependent parthenogenesis, also called pseudogamy, is a reproductive strategy in which all-female species use the sperm ofmales, usually from another species, to activate their oocytes. The sperm DNA does not participate to the development of the zygote, whichproduces only females. We discovered a novel and unique reproductive strategy in the pseudogamous nematode species "Mesorhabditis belari" , which produces its own males at low frequency. Here, females are produced asexually while males are produced sexually. Hence, the males never transmit their genes to their female offspring. We described the mechanisms that allow the production of sexual males. We also used game theory to explain the 9% sex ratio found in this auto-pseudogamous species. We have established a collection of auto-pseudogamous and sexual species of Mesorhabditis which will allow us to explore the origin and consequences of this partial loss of males and asexuality. 

Macromolecular interactions are central to most biological processes. Protein docking aims to predict the most likely structural binding modes of interacting protein partners. Understanding how binding partners coevolved can provide essential clues to improve the structural prediction of protein interfaces. In the past few years, our team has contributed to the improvement of protein docking methods by combining evolutionary information with more traditional approaches. I will present how we analyzed the way interface structure coevolved [1] and developed InterEvDock, a server for protein-protein docking designed to integrate evolutionary information in the docking process [2, 3]. InterEvDock uses one of the most successful interface sampling algorithms to date, which relies on fast Fourier transforms, followed by a consensus scoring scheme to discriminate correct from incorrect interfaces. InterEvDock was benchmarked on a large dataset of docking targets based on unbound homology models [4]. Finally, I will discuss how we successfully applied this pipeline to…