Analysis of Actin Filament Bending in Gliding Assays

Gliding assays have been the standard tools of the past two decades to analyze the properties of molecular motors. Recently, we have shown that they can be used as model systems to study bending of bio-filaments. There are several approaches in the literature to measure filament deformations. One of the most commonly used approaches is Fourier analysis. In this project, we investigate this technique in great detail using computer simulations, and compare it with another novel technique, namely curvature distributions. We also compare our theoretical predictions and simulation results with experimental measurements of filament bending from actin gliding assays

Understanding Mechanical Properties of Bio-filaments through Curvature

Cells are dynamic systems that generate and respond to forces through the complex interplay between biochemical and mechanical regulations. Since cellular processes often happen at the molecular level and are challenging to be observed under in vivo conditions due to limitations in optical microscopy, multiple analysis tools have been developed to gain insight into those processes. One of the ways to characterize these mechanical properties is by measuring their persistence length, the average length over which filaments stay straight. There are several approaches in the literature for measuring the persistence length of the filaments, including Fourier analysis of images obtained using fluorescence microscopy. Here, we show how curvature can be used to quantify local deformations of cell shape and cellular components. We develop a novel technique, called curvature analysis, to measure the stiffness of bio-filaments from fluorescent images. We test our predictions with Monte-Carlo generated filaments. We also apply our approach to microtubules and actin filaments obtained from in vitro gliding assay experiments with high densities of non-functional motors. The presented curvature analysis is significantly more accurate compared to existing approaches for small data sets. To study the effect of motors on filament deformations and velocities observed in gliding assays with functional and non-functional motors, we developed Langevin dynamics simulations of on glass and lipid surfaces. We found that generally the gliding velocity increases with an increase in motor density and a decrease in diffusion coefficient, and that motor density and diffusion coefficient have no clear effect on filament curvatures, except at a very low diffusion coefficients. Finally, we provide an ImageJ plugin to make curvature and persistence length measurements more accessible to everyone

Revitalizing Worcester and Burnside Fountain

Downtown Worcester lacks the qualities that should make it the life of the city. The goal of this project was to research ways to revitalize downtown Worcester, specifically the Common area, through the restoration of the historic Burnside Fountain. We proposed sustainable options to the City of Worcester for powering and restoring Burnside Fountain, otherwise known as "Turtle Boy," to initiate this revitalization. Through the use of case studies, interviews, cost analysis, and a survey, we devised a detailed schematic for the sustainable restoration of Burnside Fountain and provided recommendations to the City of Worcester for breathing life back into the area.

Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data

The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion’s polarizability and mass-to-charge ratio (m/z) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR_CCSPrediction under the GNU General Public License (GPL) version 3