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The BER program supports basic research to understand the fundamental nature of biological processes relevant to DOE energy and environmental mission goals. Within BER, the GSP supports systems biology research on microbial, plant, plant-microbe interactions, and environmental microbial communities to address DOE’s mission in sustainable bioenergy development. Understanding and harnessing the metabolic and regulatory networks of plants and microbes will enable their design and re-engineering for improved energy resilience and sustainability, including advanced biofuels and bio products. The widespread adoption of high-throughput, multi-omic techniques has revolutionized biological research, enabling a broader view and deeper understanding of cellular processes and the biological systems they drive. In pursuit of predictive modeling and genome-scale engineering of complex biological systems important for bioenergy, the research supported by the GSP generates vast amounts of complex omic’ and other data from a wide range of analytical technologies and experimental approaches. These data span multiple spatio-temporal scales, reflecting the organizational complexities of biological systems, and present significant computational challenges for identifying causal variants that influence phenotype. Accurate modeling of the underlying systems biology depends on surmounting those challenges.The collective characterization and quantification of pools of biological molecules (genomics, transcriptomics, proteomics, metabolomics) and their systems processes are essential to construct coherent knowledge of the systems underpinning and governing the diverse phenomics and functioning of plants, microbes, and their communities. Such characterizations necessitate the ability to combine data sets of heterogeneous types, integrated over time and space, and to represent emergent relationships in a coherent framework.The breadth of data types and the complexities inherent in the integration of different data layers present significant conceptual and implementation challenges. New algorithms for incorporating data derived from innovations in genomics, molecular imaging, structural biology, and spectroscopy are needed to work effectively with, and glean useful insights from, complex, integrated molecular, -omics’ data. Computational simulation and rigorous hypothesis testing depend on the ability to incorporate multiple experimental/environmental conditions and associated meta-datasets.Through this FOA, BSSD solicits applications that propose innovative computational solutions that can integrate large, disparate data types from multiple and varied sources, and/or the integration of data to achieve coordinated knowledge or integration of knowledge to decipher relationships of biological systems of relevance to DOE. Novel computational tools and analytical approaches of large-scale, multimodal, and multiscale data that will lead to scalable solutions for omics analysis, data mining, and knowledge extraction from complex data sets (experimental and calculated) are sought. Bioinformatics tools or computational applications that are interoperable and effective for computationally intensive data processing and analyses for systems-level investigations are desirable. Also encouraged is the enhancement of existing software or approaches that are demonstrated to be in broad use by the genomics community, to aid the interpretation of multimodal data for environmental sciences.