Agronomy and biodiversity shall address several major societal, economical, and environmental challenges. However, data are being produced in such big volume and at such high pace, it questions our ability to transform them into knowledge and enable, for instance, translational agriculture i.e., rapidly and efficiently transferring results from agronomy research into the farms (“bench to farmside”). Semantic interoperability enables data integration and fosters new scientific discoveries by exploiting various data acquired from different perspectives and domains. D2KAB’s primary objective is to create a framework to turn agronomy and biodiversity data into –semantically described, interoperable, actionable, open– knowledge, along with investigating scientific methods and tools to exploit this knowledge for applications in science and agriculture. We will adopt an interdisciplinary semantic data science approach that will provide the means –ontologies and linked open data– to produce and exploit FAIR (Findable, Accessible, Interoperable, and Re-usable) data. To do so, we will develop original approaches and algorithms to address the specificities of our domain of interests, but also rely on existing tools and methods. D2KAB involves a multidisciplinary (and international) research consortium of three computer science labs (UM-LIRMM, CNRS-I3S, STANFORD-BMIR), four bioinformatics, biology, agronomy and agriculture labs (INRA-URGI, INRA-MaIAGE, INRA-IATE, IRSTEA-TSCF), two ecology and ecosystems labs (CNRS-CEFE, INRA-URFM), one scientific & technical information unit (INRA-DIST), and one association of agriculture stakeholders (ACTA). The consortium’s expertise ranges from ontologies and metadata, semantic Web, linked data, ontology alignment, knowledge reasoning and extraction, natural language processing to bioinformatics, agronomy, food science, ecosystems, biodiversity and agriculture. The project is structured with three work-packages of research and development in informatics and two work-packages of driving scenarios. WP1 will focus on ontologies/ vocabularies and turn the AgroPortal prototype into a reference platform that addresses the community needs and reaches a high level of quality regarding both content and services offered e.g., SKOS compliance, semantic search over linked data, text annotation, interoperability with other repositories. WP2 will focus on the critical issue of ontology alignment and develop new functionalities and state-of-the-art algorithms in AgroPortal using background knowledge methods validated in ag & biodiv. WP3 will design the methods and tools to reconcile the scenarios' heterogeneous ag & biodiv data sources and turn them into linked data within D2KAB distributed knowledge graph. It will also investigate exploitation of this graph through novel visualization, navigation and search methods. WP4 includes four interdisciplinary research driving scenarios implementing translational agriculture. For instances, an ontology-driven decision support system to select the most appropriate food packaging or an augmented semantic reader for Plant Health Bulletins. We will provide a unique scientific knowledge base for wheat phenotypes and offer the first agricultural data resource empowered by linked open data. WP5 will develop semantic resources for the annotation of ecosystem experiments data and functional biogeography observations. A plant trait-environment-relationships study will be conducted to understand the impacts of climatic changes on vegetation of the Mediterranean Basin. Within a dedicated work-package, we will focus on maximizing the impact of our research. Each of the project driving scenarios will produce concrete outcomes for ag & biodiv scientific communities and stakeholders in agriculture. We have planned multiple dissemination actions and events where we will use our driving scenarios as demonstrators of the potential of semantic technologies in agronomy and biodiversity.

Non-coding pervasive transcription initiating from cryptic signals or resulting from terminator read-through is widespread in all organisms. Its biological role is well-established in eukaryotes, but poorly understood in bacteria. Two major mechanisms control bacterial pervasive transcription: transcription termination by Rho and RNA degradation by RNases. Our recent data suggest a connection between these two pathways. The multidisciplinary project CoNoCo aims to define the mutual contributions of Rho and RNase III in the control of pervasive transcription in the Gram-positive model bacteria Bacillus subtilis and Staphyloccoccus aureus. It will also establish the roles of the non-coding transcriptome in bacterial cell biology highlighted by recent discoveries of Rho-mediated regulation of B. subtilis cell differentiation and the involvement of the double-strand specific RNase III in gene regulation by small non-coding RNAs.

Synthetic microbiology is among the most promising approaches for getting more at lower cost and in the respect of the environment. Directed evolution is recognized as a key approach to obtain biobricks for synthetic biology. In this context there is a considerable interest in the development of continuous systems for directed evolution of biomolecules based on “orthogonal” evolution vector on which accumulation of mutations can be uncoupled from accumulation of mutations on the host genome. This project aims at developing such a system for the gram-positive bacterium Bacillus subtilis. An important step towards biotechnological applications will also be made by using the proposed system for: the evolution of new transcription factors for genetic circuit engineering in B. subtilis; and the evolution of new proteins binding inorganic ions such as heavy metals that might serve as biosensors and in bioextraction systems. The work program decomposes into three work-packages : development of a system for directed evolution in B. subtilis ; in silico analyses for the optimization of the system ; application to biobrick production. B. subtilis is a totally harmless bacterium of considerable biotechnological interest: it stands as the second model bacterium after Escherichia coli and is as such a natural chassis for synthetic biology; it is also a soil dweller (and probably a normal gut commensal in humans) with highly diverse physiological capabilities, and an ability to survive extreme conditions in the form of spores. B. subtilis and several of its close relatives of the Bacillus genus (notably B. licheniformis and B. amyloliquefaciens) exhibit a remarkable capacity of biological compound production that can be scaled-up to industrial levels are widely used in the industry for enzyme production.

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.

Developing a multiscale model for plants, capable of managing complex plant response to environmental conditions and its underlying genetic diversity, is a major issue in agronomy and biology. The Resource Balance Analysis framework is promising to integrate the finest scales (genes) to the individual scale, by refining the organ description in existing functional-structural plant models and by taking advantage of new omics data, such as quantitative proteomics. Dealing with multicellular organisms such as the plant opens mathematical and computational challenges for building, calibrating and simulating such multiscale models in steady-state and dynamical conditions. This project aims to first address these mathematical challenges, develop and experimentally validate the first multiscale model of the plant Arabidopsis thaliana. Second, the model will help biologists study the plant's resource allocation strategy under normal and limiting environmental conditions, primarily using data sets generated by one of the most robust plant phenotyping systems. The model will be used to generate new biological knowledge or hypotheses (e.g., cellular functions affected in plant plasticity to nutritional stress). This clearly represents a significant advance in the context of plant adaptation, and to decipher the specific responses of other A. thaliana genotypes to complex environmental conditions. The modeling framework will address important challenges such as the integration of heterogeneous and multiscale data (from omics to phenotypic traits), genotype-phenotype relationships in complex environments, and the integration of genetic diversity into modeling.