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Molecular engineering is an emerging field that aims to create functional devices for modular purposes, particularly bottom-up design of nano-assemblies using mechanical and chemical methods to perform complex tasks. In this study, we present a novel method for constructing an RNA clamp using circularized RNA and a broccoli aptamer for fluorescence sensing. By designing a circular RNA with the broccoli aptamer and a complementary DNA strand, we created a molecular clamp that can stabilize the aptamer. The broccoli aptamer displays enhanced fluorescence when bound to its ligand, DFHBI-1T. Upon induction with this small molecule, the clamp can exhibit or destroy fluorescence. We demonstrated that we could control the fluorescence of the RNA clamp by introducing different complementary DNA strands, which regulate the level of fluorescence. Additionally, we designed allosteric control by introducing new DNA strands, making the system reversible. We explored the use of mechanical tension to regulate RNA function by attaching a spring-like activity through the RNA clamp to two points on the RNA surface. By adjusting the stiffness of the spring, we could control the tension between the two points and induce reversible conformational changes, effectively turning RNA function on and off. Our approach offers a simple and versatile method for creating RNA clamps with various applications, including RNA detection, regulation, and future nanodevice design. Our findings highlight the crucial role of mechanical forces in regulating RNA function, paving the way for developing new strategies for RNA manipulation, and potentially advancing molecular engineering. Although the current work is ongoing, we provide current progress of both theoretical and experimental calculations based on our findings.
The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain.
The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models.
The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.