CAREER: Stochastic Analyses to Optimize Designs for Single-Cell Optical Microscopy Experiments

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Computational analyses can provide powerful keys to unravel basic biological mysteries, to discover new mechanisms, and to drive national advances in medical, agricultural, and environmental sciences. The exceptionally complicated nature of biological processes requires that new computational tools be created to address specific challenges and opportunities that frequently arise in modern biological investigations. This project discovers, improves, and validates several sets of integrated computational and experimental methods to explore and more fully understand one of the most fundamental processes in molecular biology – the translation of individual mRNA molecules to form functional proteins. The project creates and distributes new computational software packages to design, interpret, and predict cutting-edge single-cell and single-molecule experiments for use in future biological and biomedical investigations. This project also incorporates a new three-week annual summer school program to train undergraduate students from the physical, engineering, and computer sciences, to use modern statistical methods to measure, analyze, predict, and control complex biological processes. In addition, the project involves developing and disseminating an online quantitative biology course curriculum for senior undergraduate and graduate students to complement the recent community-written textbook Quantitative Biology: Theory, Computational Methods, and Models (MIT Press, 2018).This project creates, tests, and validates new technologies to integrate super-resolution fluorescence microscopy experiments with high-performance computing to measure, interpret and predict the complex dynamics of single-mRNA translation within variable genomic and environmental contexts. Project goals comprise three integrated research objectives: (1) Develop rigorous computational tools to quantify and take advantage of intrinsic and extrinsic fluctuations (often called ‘process noise’) that affect the dynamics of single-mRNA translation, while minimizing impacts of experimental measurement noise; (2) Demonstrate new methodologies to rigorously estimate model uncertainties and choose the best possible experiments to resolve subtle differences in competing hypotheses in the most efficient and cost-effective manner; and (3) Combine intrinsic, temporal mRNA translation fluctuations with statistical analyses to enable simultaneous measurement of multiple translating mRNA species in real time and in single, living cells. The project incorporates tools from all three objectives to produce and distribute an open-source, user-friendly Python software package called RNA Sequence to NAscent Protein Simulator (rSNAPSim), which enables experimental biologists to analyze, predict, and redesign single-mRNA translation experiments.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.