Beyond the role the intestinal metagenome plays in regulating multiple physBeyond its role in regulating multiple physiological functions that impact health, the intestinal metagenome is implicated in cancer initiation, progression and responses to therapies, even for extraintestinal neoplasia. Hence, there is an urgent need to fully identify and functionally characterize minimalist commensal ecosystems relevant to cancer, with reliable and robust methods, to validate cancer-associated gut microbiome fingerprints of high clinical relevance, and to develop diagnosis tools that will become part of the oncological arsenal for the optimization and personalization of therapy. Based on retro-and pro-spective studies, with large discovery and validation cohorts enrolling >9,000 cancer patients across 10 countries, ancillary to ongoing innovative clinical trials or FDA/EMA approvals across 4 frequent cancer types, ONCOBIOME will pursue the following aims: 1/ identify and validate core or cancer-specific Gut OncoMicrobiome Signatures (GOMS) associated with cancer occurrence, prognosis, response to, or progression on, therapy (polychemotherapy, immune checkpoint inhibitors, dendritic cell vaccines) or adverse effects, 2/ decipher the functional relevance of these cancer-associated gut commensal ecosystems in the regulation of host metabolism, immunity and oncogenesis, 3/ integrate these GOMS with other oncology hallmarks (clinics, genomics, immunomics, metabolomics) 4/ design optimal companion tests, based on those integrated signatures to predict cancer occurrence and progression. With high carat interdisciplinary experts, ONCOBIOME expects to validate cancer or therapy-specific Gut OncoMicrobiome Signatures (GOMS) across breast, colorectal, melanoma and lung cancers adjusting for covariates, to unravel the mode of action of these GOMS in innovative platforms, thus lending support to the design of cancer preventive campaigns using well characterized pre-and pro-biotics.

The tremendous technological advance underlying the ongoing genomic revolution in the life sciences has profoundly transformed biological research over the last 10-15 years. The grand challenge ahead is to leverage experimental progress like high-throughput sequencing by developing effective tools for large scale data-based inference and optimization. The goals of INFERNET rely on the transfer of ideas and methods developed in recent years at the boundary between statistical physics and information theory into the world of quantitative biology. We aim at setting up a consortium characterized by a proven track-record of high-quality research in order to implement a highly integrated program leading from the design of new algorithms to concrete biological applications. The consortium will provide the optimal environment to nurture a generation of researchers that will drive new developments at the forefront of these challenging fields. The perimeter of each individual research activity will be delimited by: (a) the research themes characterized by the toolbox and methods developed and shared within INFERNET, (b) the choice of the application domains. Principal research themes covered by the consortium will be: (i) the inference of interaction networks from data, (ii) the analysis of static and dynamical processes on networks. Application domains can be broken down into four main areas: (i) the inference and modeling of multi-scale biological networks, (ii) the rational design of biological molecules, (iii) the quantitative study of cell energetics in proliferative regimes, (iv) the characterization of functional states of large-scale regulatory networks. Each individual research project will be a puzzle piece of the wider research project, as well as tailored to suit each researcher's scientific and professional development.

The last two decades have witnessed giant experimental breakthroughs in different areas of the life sciences, from genomics to epidemiology. Thanks to modern high-throughput techniques, biological systems across multiple scales –from single molecules up to entire populations– can now be probed quantitatively at high spatial and temporal resolutions. Besides enhancing our basic knowledge of a system’s constituents, these data potentially encode a plethora of information about the functional constraints that govern its evolution and the physical constraints that limit its performance, as well as about levels of organization, dynamical constraints or design principles that would be hard to identify from low-throughput data. Extracting this information is also crucial for applications ranging from the design of proteins with a desired functionality to the reconstruction of contacts during an epidemics. Inverse statistical mechanics attempts to do it by inferring generative models (Boltzmann distributions) from data using methods from the physics of disordered and random systems. Specific characteristics of biological data however, like strong undersampling and heterogeneity, limit the effectiveness of these tools. SIMBAD aims at developing a class of statistical inference techniques capable of overcoming these issues. In SIMBAD, theoretical work will supply concepts and methods to address four pressing problems (learning protein sequence landscapes, inverse modeling metabolic networks, inferring contact networks from epidemiological data, and improving survival analysis models), which in turn will guide the theory towards integration with the existing standards of each field. This effort promises to open new pathways for basic research to impact economic, technological and societal issues; the high- profile cross-disciplinary expertise represented in SIMBAD ensures instead for measurable and achievable objectives, placing SIMBAD in an ideal position to achieve its goals

The dramatic differentials in healthy ageing, quality of life and life expectancy between individuals of different socioeconomic groups, is a major societal challenge facing Europe. The overarching aim of the LIFEPATH project is to understand the determinants of diverging ageing pathways among individuals belonging to different socio-economic groups. This will be achieved via an original study design that integrates social science approaches with biology (including molecular epidemiology), using existing population cohorts and omics measurements (particularly epigenomics). The specific objectives of the project are: (a) To show that healthy ageing is an achievable goal for society, as it is already experienced by individuals of high socio-economic status (SES); (b) To improve the understanding of the mechanisms through which healthy ageing pathways diverge by SES, by investigating lifecourse biological pathways using omic technologies; (c) To examine the consequences of the current economic recession on health and the biology of ageing (and the consequent increase in social inequalities); (d) To provide updated, relevant and innovative evidence for healthy ageing policies (particularly “health in all policies”) that address social disparities in ageing and the social determinants of health, using both observational studies as well as an experimental approach based on the existing "conditional cash transfer" experiment in New York. To achieve these objectives we will use data from three categories of studies: 1. Europe-wide or national surveys combined with population registry data; 2. Cohorts with intense phenotyping and repeat biological samples (total population >33,000); 3. Large cohorts with biological samples (total population >202,000). The cohorts will provide information on healthy ageing at different stages of life, based on the concepts of life-course epidemiology ("build-up and decline") and multimorbidity.