The genome is home to thousands of genes, whose activities are turned on and off by many biological molecules. The proper functioning of gene control systems profoundly influences all aspects of life, such as the formation of diverse tissues, evolution of different species, and response to environmental stimuli. However, the genome is not simply a one-dimensional entity but takes on complex three-dimensional (3D) structure, which plays a fundamental role to facilitate interactions among biological molecules. The overarching goal of this project is to develop rigorous computational methods to discover the control mechanisms of genes by integrating 3D genome structure information. The answer to this question is crucial for revealing the underlying rules of life which will provide us the capacity to link complex genome to changes in a biological system. This project will generate open-source software and large-scale resources with wide applications and impacts in cell biology, bioengineering, disease diagnostics, crop improvement and ecology. Activities, such as summer research experiences, online resources for public scientific literacy, and education modules for high-school students, will greatly motivate next generation students to pursue interdisciplinary study and research.
This project will develop a suite of advanced probabilistic models and efficient inference algorithms to delineate complex gene regulation by systematically integrating the 3D chromatin structure information. The first goal of this project is to develop a predictive algorithm of cell-type specific transcription factor binding site based on a novel Bayesian graphical model. The second goal is to design an efficient search algorithm to identify structures of regulatory circuitry of transcription factors involved in context-specific gene expression. Building upon these methodological developments, the third goal is to develop a new regulatory network-based algorithm to predict gene expression changes induced by different perturbations. The algorithms and software generated by this project will be tools for both experimental and computational biologists to analyze large-scale multi-omics datasets, to derive interpretable predictions, and to understand how complex gene regulatory systems are coordinated in 3D chromatin space. To motivate and engage students, especially underrepresented minority undergraduates, into biological and computational science, novel virtual reality visualization Youtube videos and Jupyter notebooks teaching materials will be disseminated along with participation in national Women in Engineering Program and NSF REU program. Results of this project, including the developed computational infrastructure and education materials, can be found at https://cmse.msu.edu/directory/faculty/jianrong-wang/.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
