Nature and Nanotechnology likewise employ nanoscale machines that self-assemble into structures of complex architecture and functionality. Fluorescence microscopy offers a non-invasive tool to probe and ultimately dissect and control these nanoassemblies in real-time. In particular, single molecule fluorescence resonance energy transfer (smFRET) allows us to measure distances at the 2-8 nm scale, whereas complementary super-resolution localization techniques based on Gaussian fitting of imaged point spread functions (PSFs) measure distances in the 10 nm and longer range. In terms of Big Data Analysis, we have developed a method for the intracellular single molecule, high-resolution localization and counting (iSHiRLoC) of microRNAs (miRNAs), a large group of gene silencers with profound roles in our body, from stem cell development to cancer. Microinjected, singly-fluorophore labeled, functional miRNAs are tracked at super-resolution within individual diffusing particles. Observed mobility and mRNA dependent assembly changes suggest the existence of two kinetically distinct assembly processes. We are currently feeding these data into a single molecule systems biology pipeline to bring into focus the unifying molecular mechanism of such a ubiquitous gene regulatory pathway. In addition, we are using cluster analysis of smFRET time traces to show that large RNA processing machines such as single spliceosomes – responsible for the accurate removal of all intervening sequences (introns) in pre-messenger RNAs – are working as biased Brownian ratchet machines. On the opposite end of the application spectrum, we utilize smFRET and super-resolution fluorescence microscopy to monitor enhanced enzyme cascades and nanorobots engineered to self-assemble and function on DNA origami.
The Zimmerman research group is a pioneer in computational chemical reaction discovery, and has developed algorithms which map out complex multi-component, multi-elementary step reaction mechanisms without reliance on prior chemical knowledge. These methods, being low computational cost compared to other quantum chemical techniques, provide a large amount of detailed and accurate chemical information to be processed and exploited. These data present an opportunity for application of statistical methods, specifically from machine learning, to determine the key chemical features that enable chemical reactions to occur. Machine learning not only provides a detailed analysis of how chemical processes work, but also provides a rapid, predictive method to determine the rate and selectivity of novel chemical reaction sequences. Ongoing work in the Zimmerman group is taking advantage of tools such as the Kernel Ridge and logistic regressions, k-nearest neighbors, and genetic algorithms to transform chemical reaction data into predictive tools applicable to many types of chemistry.