CAREER: Mathematical inference and analysis of spatio-temporal RNA transport and organization in development

Across the tree of life, cells are able to sort and deliver protein components to the appropriate cell locations, thus ensuring healthy biological function and development. In large cells such as developing oocytes and neurons, the transport of proteins such as messenger RNA (mRNA) is especially important in controlling how genes express in space and time, with defects often leading to neurological and developmental dysfunctions. While microscopy and imaging experiments of these proteins have undergone significant advances, the mathematical theory and quantitative methods for interpreting this complex experimental data are lacking. This project aims to establish a rigorous mathematical framework for analyzing complex data from living cells and for learning mechanisms of mRNA transport and organization. The project will advance dynamical systems and data-driven modeling techniques with the goal of testing biological hypotheses and making predictions about the dynamics of proteins in living cells. In addition, the project will improve the training of students interested in interdisciplinary research by incorporating regional undergraduates in summer and semester research projects. A network of local mathematical modeling competitions will also engage mathematics and STEM students and make instructional resources more accessible. This research aims to establish a framework for inferring underlying protein dynamics from microscopy images and for predicting mechanisms that govern the spatio-temporal organization of mRNA in cells. Challenges include the limited spatial resolution of experiments performed in living cells and difficulties associated with coupling the relevant timescales of protein cargo and filament dynamics. The project will build new reaction-diffusion mathematical models for the interaction of mRNA and binding proteins in biomolecular condensates, which package the RNA in healthy transport and development. Novel parameter inference and identifiability techniques will be developed to connect these models with microscopy data. Methods from pattern formation and stochastic modeling will be leveraged to investigate the formation of protein assemblies and their heterogeneity. In addition, the interaction of mRNA and cytoskeleton filaments will be investigated using state-switching models, informed by machine learning and model selection techniques. From the standpoint of biotechnology, the project studies the formation of RNA complex and develops machine-learning-based computational tools that includes spatio-temporal information for accurate understanding of dynamic biological systems. These advances have the potential to impact our biological understanding of embryonic patterning and development, with implications for neurological function and disorders. 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. NSF Award ID: 2544169 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Maria-Veronica Ciocanel | Institution: Duke University, DURHAM, NC | Award Amount: $505,185 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2544169 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2544169.html