BMI faculty Dr. Daifeng Wang, together with Dr. Ian Blaby from Brookhaven National Laboratory, won the 2017 SBU-BNL SEED grant on "Large-Scale Comparative Regulatory Network Analysis in Photosynthetic Organisms".
As primary bioproducers, photosynthetic organisms are fundamental to biological and geochemical cycles. Yet, despite recent availability of genome-wide engineering tools, biological redesign to exploit plants for increased biomass, bioenergy and food production purposes is fundamentally thwarted by a lack of foundational knowledge in plant gene regulation and protein function. This project will explore novel computational and network science approaches to comparatively analyze plant transcriptomes. This will enable elucidation of gene regulatory networks, genetic circuits, regulatory components and facilitate gene functional inferences. We will develop and apply our approaches on complex genomes to identify functionally related gene co-expression modules, infer gene regulatory networks and elucidate gene regulatory circuits driving evolutionarily conserved and species-specific genomic functions. Specifically, we propose to apply large-scale comparative analyses based on network science and machine learning approaches to study gene networks across multiple photosynthetic organisms and simultaneously cluster these networks into functional gene modules. Species-specific and cross-species modules will be exploited to infer gene function where presently none exist. We will initially focus on the Department of Energy (DOE) Office of Biological and Environmental Research (BER) flagship organisms (taxonomically diverse photosynthetic species of high relevance to DOE missions), although additional organisms can be added to the study during the course of the project. However the proposed comparative analysis will benefit all of the plant genomics community, and by virtue of gene evolutionary conservation, biology at large. While our research will initially be focused on transcriptomic data, the computational genomics platform we will develop will be able to integrate and analyze multi-omics data such as metabolomics, proteomics and protein-protein interactions.