Growth dynamics of populations of interacting species in the aquatic environment are of great importance, both for understanding natural ecosystems and in efforts to cultivate these organisms for industrial purposes. Here we consider a simple two-species system wherein the soil bacterium /Mesorhizobium loti/ supplies vitamin B12 (cobalamin) to the freshwater green alga /Lobomonas rostrata/, which requires this organic micronutrient for growth. In return the bacterium receives photosynthate from the alga. Mathematical models are developed that describe minimally the interdependence between the two organisms, and that fit the experimental observations of the consortium. These models enable us to distinguish between different mechanisms of nutrient exchange between the organisms, and provide strong evidence that rather than undergoing simple lysis and release of nutrients into the medium, /M. loti/ regulates the levels of cobalamin it produces, resulting in a true mutualism with /L. rostrata/. Over half of all microalgae are dependent on an exogenous source…

In the past decade or so, new technologies in biotech have meant an explosion in the availability of high-dimensional genomic data (microarrays, SNP data, RNA-Seq...): their dimension and noise levels making it necessary to rely on machine learning techniques and statistical models to extract meaningful signal and narrow down the field for further experimental research. In this presentation, I will try to give a very general overview of some of the statistical techniques commonly used to treat high-dimensional data, as well as a more detailed illustration of the technique we developed to identify combinatorial interaction effects in such data. A crucial aspect of machine-learning when dealing with high-dimensional data, is the concept of sparsity: how much of the input’s variables find their way in the model. By using regularisation techniques (the addition of a tailored penalisation component), it is possible to ensure certain properties of the statistical model (size, elimination of collinear…

Gene expression plays a central role in the orchestration of cellular processes. The use of inducible promoters to change the expression level of a gene from its physiological level has significantly contributed to the understanding of the functioning of regulatory networks. However, from a quantitative point of view, their use is limited to short-term, population-scale studies to average out cell-to-cell variability and gene expression noise and limit the nonpredictable effects of internal feedback loops that may antagonize the inducer action. Here, we show that, by implementing an external feedback loop, one can tightly control the expression of a gene over many cell generations with quantitative accuracy. To reach this goal, we developed a platform for real-time, closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells? environment, and original software for automated imaging, quantification, and model predictive control. By…

Pairwise interactions between RNA molecules are fundamental to many biological processes, including translation, splicing, and gene regulation. Traditionally, RNA-RNA interactions have been mapped experimentally one at a time, or predicted computationally. To facilitate the study of RNA interactomes, we have recently developed CLASH (Cross-linking, Ligation and Sequencing of Hybrids), an experimental technique that captures RNA-RNA interactions by physically joining two RNA molecules into a single chimeric RNA molecule, followed by next-generation sequencing. I will discuss the applications of this approach in the study of ribosome biogenesis, gene regulation by microRNAs, and splicing. I will also present a new experimental approach that will allow comprehensive mapping of genetic interactions within and between genes, at a single nucleotide resolution.

Within the next few years, as sequencing technologies get faster and cheaper, tenths of thousands of newly sequenced genomes will become available (see for example [1, 2]). In contrast, experimental characterisation of genomes’ functional elements, which is expected to lead to breakthroughs in understanding molecular and cellular processes [3], remains in most cases an expensive and painstakingly slow process. In this scenario, computational biology appears as one scalable way to make sense of the vast amount of sequence data that are being deposited (daily) in the public domain. While computational predictions cannot substitute for experiments, they can generate hypotheses to guide targeted experimental validation. In this seminar, I will illustrate ongoing work that aims to shed some light on the “Dark Matter” of the protein sequence universe [4]. Additionally, I will discuss ideas to improve annotation of pathways, protein complexes and organelles in fully sequenced organisms.

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With the increasing availability of whole genome sequencing data, one has now access to a wealth of somatic mutation events specific to patients carrying a cancer. Classically, researchers focus on the events lying within protein-coding regions in order to extract the ones disruptive to protein structure and/or function. One can use whole-genome sequencing information to extract mutations lying within cis-regulatory regions with potential impact on gene regulation. We will present our study of 84 matched tumor-normal whole genomes of B-cell lymphomas where we specifically focused on mutations overlapping transcription factor binding sites (TFBSs) for their potential impact on gene expression regulation. We highlight the importance of moving beyond the protein-coding subset of the genome to analyze the cis-regulatory space. TFBSs representing the core of gene cis-regulation, we will finally introduce our transcription factor flexible models representing the next generation of TFBS prediction.