COMPLEX DATA: ADVANCED DATA ANALYTIC TECHNOLOGIES FOR SYSTEMS BIOLOGY AND BIOENERGY

The Biological and Environmental Research (BER) program supports transformative science to achieve a predictive understanding of complex biological, earth and environmental systems. BER's Biological Systems Science Division (BSSD) research activities integrate multidisciplinary scientific discovery driven science with technology development to understand plant and microbial systems relevant to national priorities in sustainable energy and innovation in life sciences. BSSD research activities span bioenergy research focused on plant genomics, microbial conversion, sustainable energy, biosystems design (including secure biosystems design), and environmental microbiome research. BSSD's Computational Biology, Biomolecular Characterization and Bioimaging (including quantum enabled bioimaging) activities combined with DOE User Facilities (such as the Joint Genome Institute) serve as key enabling capabilities. a. Complex Data: Advanced Data Analytic Technologies for Systems Biology and Bioenergy BSSD science programs generate very large, complex, and multimodal data sets that have all the characteristics of big data' these data sets and associated analytics are critical to BSSD scientific discovery and bio-design applications. Technology improvements in biological instruments from sequencers to advanced imaging devices are continuing to advance at exponential rates, with data volumes in petabytes today and expected to grow to exabytes in the future. These data are highly complex ranging from high throughput omics data, experimental and contextual environmental data across multiple scales of observations spanning molecular to cellular to multicellular scale (plants and microbial communities); multiscale 3D and 4D images for conceptualizing and visualizing spatiotemporal expression and function of biomolecules, intracellular structures, and the flux of materials across cellular compartments. The ability to generate complex multi- omic environmental data and associated meta-datasets greatly exceeds the ability to interpret these data. The current need is for general solutions for managing and interpreting complex data and extracting knowledge and information from data sets rather than generating primary data. The objective of analytical and algorithmic approaches could include taxonomy but should also enable integration and functional connection of experimental components. The objective of generalized approaches to data integration should be generation of testable hypotheses and models that propose functional or causal connections among system components. Innovative solutions and frameworks for management and analysis of large-scale, multimodal, and multiscale data leveraging artificial intelligence and machine learning methods, that enhance effectiveness and efficiency of data processing for investigations across spatial scales and scientific disciplines are needed. These include interoperable computational platforms and data resources to facilitate specialized data workflows starting from data collection, processing, access, sharing, integration, and analysis for large-scale, integrated omics, instrumental, and imaging datasets. Novel approaches, software tools and modelling frameworks for managing, integrating, and analyzing big data' will be considered. Questions Contact: Ramana Madupu, Ramana.Madupu@Science.doe.gov or Resham Kulkarni, Resham.Kulkarni@science.doe.gov b. Other In addition to the specific subtopics listed above, the Department invites grant applications in other areas that fall within the scope of the topic description above.