The ability to measure genome-wide gene expression or mutations from a biological sample made of thousands or millions of cells has revolutionized biology in the late 1990’s, allowing for example to characterize subtypes of cancers from their molecular profile or to identify comprehensive lists of genes expressed or inhibited in particular conditions. Cells within a sample are however never all the same, and measuring an average over thousands of cells may mask or even misrepresent signals of interest that vary between individual cells. Fortunately, recent technological advance in massively parallel sequencing and high-throughput cell biology technologies now give us the ability to measure, at the level of individual cells, genome-wide measurements based on DNA, RNA, chromatin states or proteins. The use of these techniques, which we collectively refer to as single-cell genomics, allows us to study cell-to-cell variability within a biological sample and investigate new questions out of reach for classical bulk genomics. For example, intra-tissue heterogeneity is now clearly established in many cell types including T cells, lung cells, or myeloid progenitors. The construction of a comprehensive atlas of human cell types is now within our reach. Cell-to-cell variability is also central in many biological processes such as gene regulation or cell differentiation, as it reflects the intrinsic stochastic molecular processes and provides information on the underlying molecular networks. This variability has been shown to play an important functional role in the cell decision-making process and beyond. Consequently, the measurement of gene expression in single cells has the promise of revolutionizing our understanding of gene regulation and resolving many longstanding debates in biology. Besides technological aspects, single-cell genomics raises new mathematical and computational challenges. The nature of data produced by single-cell genomics techniques, as well as the questions we need to answer, differ indeed a lot from standard bulk genomics. For example, due to the extremely small amount of biological material present in a single cell, it is common to have 90% of missing values in a single-cell experiment, and the observed values can themselves be strongly distorted by particular experimental artifacts, calling for new statistical modelling of these data. In addition, the quantity of cells that are investigated simultaneously by the latest (and future) single-cell technologies goes easily in the millions, orders of magnitude more than the number of samples in standard bulk genomics, raising new computational challenges for scalability. Finally, new biological questions are raised, such as modelling a differentiation process or integrating genetic and epigenetic data at the single-cell level, which calls for new mathematical models and algorithms. In short, new dedicated analytical tools are crucially needed to unleash the full power of single cell genomics. The goal of this project is to attack some of these pressing challenges, by developing new mathematical models and computational tools for three biological problems: (i) investigating sample heterogeneity and cell identity, (ii) modelling the dynamics of cell differentiation and gene regulation, and (iii) exploring single cell epigenomics. For that purpose, we have gathered a consortium with a unique combined experience in high dimensional statistics, machine learning, bioinformatics, computational and systems biology, and an extended network of collaborators on single-cell genomics in France and abroad.

In a break with current approaches, each based on an analysis paradigm, the HERELLES scientific program aims to define an operational theoretical framework that allows the combination and collaboration of different analysis paradigms and allows strong interaction with the expert for the extraction of knowledge from heterogeneous multi-temporal data. We aim for an approach that will allow collaboration between supervised or unsupervised methods through information transfers (clusters will be used to create learning data or to increase their size, learning data will be used to help validate and thematize clusters...) extracted from different but complementary time data. This collaboration will be controlled by the expert, who will intervene to incrementally add new knowledge. Thematic validation in remote sensing.

The project is devoted to the development of statistical methods specifically designed for analysing different types of ecological networks: trophic, mutualistic, competition or antagonistic and host-parasite systems. We propose to create a unique consortium of researchers combining applied statisticians with long-standing experience on life-science modeling and ecologists at the forefront of their domain to tackle the challenges posed by the advanced statistical modeling of ecological networks. Our proposal includes 1) integrating space and time dimensions in ecological networks modeling and developing tools for the comparison of networks along environmental gradients; 2) integrating multiple types of interactions, taking advantage of covariate information (such as species traits, distributions, phylogenies and environmental variables) when available; 3) incorporating sampling effects in our analyses and 4) providing predictions on ecosystem response to environmental changes.

Agriculture has currently to tackle new challenges, largely due to the need to increase global food supply under the declining availability of soil and water resources and increasing threats from climate change. It has to face main changes and to adapt to new conditions, in particular environmental ones. To better handle this adaptation, it is necessary to better understand several key notions such as genetic variability and interactions between the plant and its environment. In this context, predictive approaches relying on ecophysiology and genetic knowledge, as well as mathematical modeling are very promising. The Stat4Plant project aims at developing new statistical methodologies and new algorithmic tools for modeling and analyzing genotypic variability and interaction between plant and its environment in a context of climate change. The project consortium gathers scientists in modeling and applied statistics with large experience in interdisciplinary collaborations in plant sciences and biologists with strong expertise in phenotype-genotype relations. The project is structured in four main research axes, supported by strong collaborations between statisticians and biologists and motivated by practical questions linked with biological dataset. The first axis aims at developing new methods for identifying key biological processes driving plant development lying behind the observed genotypic variability. These works combine mechanistic ecophysiologic modeling highly nonlinear of plant development, statistical mixed effects modeling for genotypic variability and statistical testing procedures, in particular adapted to small data samples, to identify genotype-dependent parameters. These approaches will allow to better understand genotype by environment interactions and to identify new tools for varietal selection. The second axis is dedicated to joint modeling of a time of interest such as flowering time or harvest time and of a phenotypic dynamical trait depending on time such as biomass or pest presence. The considered joint models combine survival models with random effects and covariates of high dimension and nonlinear mixed effects models for the dynamical trait. The objective is to identify the relevant covariates, to estimate the parameters and to predict the time of interest. These methods will allow for example to better predict flowering time or optimal harvest time. The third axis aims at developing new methods for identifying among a high number of covariates those who are the most influent for a phenotypic trait of interest, solely or jointly with a time of interest. Nonlinear mixed effects models combining mechanistic models of plant development and genetic models integrating a high number of genetic covariates will be used to model genotypic variability of the trait of interest. New covariates selection methods adapted to the nonlinear context will be developed. These methods will allow to identify the main genetic factors influencing the trait. Finally, the last axis aims at building new criteria for varietal selection, integrating randomness of environmental conditions and targeting simultaneously several objectives, such as maximizing yield and minimizing yield variability. New methodologies for optimizing these criteria will be developed. Such criteria will be new tools for decision support system in agriculture.

In perennial species, yield and production quality are impacted by water stress with marked interannual effects. The knowledge of the physiological and genetic mechanisms regulating grapevine responses to WD remain largely insufficient to adapt the viticulture to climatic challenges. Most often, responses to water stress have been studied during a single vegetative cycle, considering traits independently and using a limited range of genetic diversity. The G2WAS project aims to study the responses of grapevine to water deficit on intra- and inter-annual scales, by integrating the dynamics of production, storage and utilization of carbon resources in both vegetative and reproductive systems. This study will be performed with a diversity panel designed to maximize the genetic diversity of the cultivated species (V. vinifera). In order to decipher the genetic and physiological bases of adaptation to drought, and to incorporate them into breeding programs, several innovative approaches will be run: i) advanced phenotyping of vegetative and reproductive organs targeted at several critical developmental stages, with a focus on carbon allocation, ii) identification by exhaustive transcriptomics (RNAseq) of co-regulated gene networks; iii) genotype-phenotype whole genome association (GWAS) analysis applied to a panel of 279 varieties iv) development of a multi-trait and multi-year statistical model to improve prediction accuracy. Performed for the first time in perennials, such a combination of methods will improve the detection of QTL and the prediction of individual genetic values. This multidisciplinary approach will be supported by the G2WAS consortium which brings together specialists in eco-physiology, physiology, quantitative and functional genetics, statistics and breeders. In addition to the coordination (WP1), the project is based on 4 WP: a physiological study (WP2) of 16 contrasting genotypes confronted to a gradient of 10 hydric conditions under tightly controlled environment (PhenoDyn platform) to provide a precise description of the responses to water deficit and to parametrize the conditions to apply to the GWAS panel (WP3) in the semi-automated phenotyping platform (PhenoArch platform); these data will be used for QTL detection and genomic prediction (WP4), using a statistical model specifically developed for this study; finally, the results will be used in ongoing selection programs (WP5). This last step will be one of the first attempts to combine properties of tolerance to water stress and resistance to fungi, in agreement with the 2 major challenges facing viticulture. In addition to an expected breakthrough on the characterization of critical genetic resources which are fundamental for grapevine improvement, this study will provide new clues on the interaction between carbon limitation and hydraulic functioning at the plant scale. This progress will be essential to develop improvement strategies to anticipate some of the drawbacks linked to climate changes, in particular the increase of evaporative demand and the limitation of water resources. This knowledge and methodologies will be potentially transferable to other models of perennial fruit species.